Analytic Combinatorics Philippe Flajolet Robert Sedgewick
ANALYTIC COMBINATORICS PHILIPPE FLAJOLET Algorithms Project ...

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Analytic Combinatorics Philippe Flajolet Robert Sedgewick

ANALYTIC COMBINATORICS PHILIPPE FLAJOLET Algorithms Project INRIA Rocquencourt 78153 Le Chesnay France

& ROBERT SEDGEWICK Department of Computer Science Princeton University Princeton, NJ 08540 USA

ISBN 978-0-521-89806-5 c

Cambridge University Press 2009 (print version) c Flajolet and R. Sedgewick 2009 (e-version)

P.

This version is dated June 26, 2009. It is essentially identical to the print version. c

Philippe Flajolet and Robert Sedgewick 2009, for e-version c

Cambridge University Press 2009, for print version ISBN-13: 9780521898065

On-screen viewing and printing of individual copy of this free PDF version for research purposes (non-commercial single-use) is permitted.

ANALYTIC COMBINATORICS

Analytic combinatorics aims to enable precise quantitative predictions of the properties of large combinatorial structures. The theory has emerged over recent decades as essential both for the analysis of algorithms and for the study of scientific models in many disciplines, including probability theory, statistical physics, computational biology and information theory. With a careful combination of symbolic enumeration methods and complex analysis, drawing heavily on generating functions, results of sweeping generality emerge that can be applied in particular to fundamental structures such as permutations, sequences, strings, walks, paths, trees, graphs and maps. This account is the definitive treatment of the topic. In order to make it selfcontained, the authors give full coverage of the underlying mathematics and give a thorough treatment of both classical and modern applications of the theory. The text is complemented with exercises, examples, appendices and notes throughout the book to aid understanding. The book can be used as a reference for researchers, as a textbook for an advanced undergraduate or a graduate course on the subject, or for self-study. PHILIPPE FLAJOLET is Research Director of the Algorithms Project at INRIA Rocquencourt. ROBERT SEDGEWICK is William O. Baker Professor of Computer Science at Princeton University.

(from print version, front)

Contents P REFACE

vii

A N I NVITATION TO A NALYTIC C OMBINATORICS

1

Part A. SYMBOLIC METHODS

13

I. C OMBINATORIAL S TRUCTURES AND O RDINARY G ENERATING F UNCTIONS I. 1. Symbolic enumeration methods I. 2. Admissible constructions and specifications I. 3. Integer compositions and partitions I. 4. Words and regular languages I. 5. Tree structures I. 6. Additional constructions I. 7. Perspective

15 16 24 39 49 64 83 92

II. L ABELLED S TRUCTURES AND E XPONENTIAL G ENERATING F UNCTIONS II. 1. Labelled classes II. 2. Admissible labelled constructions II. 3. Surjections, set partitions, and words II. 4. Alignments, permutations, and related structures II. 5. Labelled trees, mappings, and graphs II. 6. Additional constructions II. 7. Perspective

95 96 100 106 119 125 136 147

III. C OMBINATORIAL PARAMETERS AND M ULTIVARIATE G ENERATING F UNCTIONS III. 1. An introduction to bivariate generating functions (BGFs) III. 2. Bivariate generating functions and probability distributions III. 3. Inherited parameters and ordinary MGFs III. 4. Inherited parameters and exponential MGFs III. 5. Recursive parameters III. 6. Complete generating functions and discrete models III. 7. Additional constructions III. 8. Extremal parameters III. 9. Perspective

151 152 156 163 174 181 186 198 214 218

Part B. COMPLEX ASYMPTOTICS

221

IV. C OMPLEX A NALYSIS , R ATIONAL AND M EROMORPHIC A SYMPTOTICS IV. 1. Generating functions as analytic objects IV. 2. Analytic functions and meromorphic functions

223 225 229

iii

iv

CONTENTS

IV. 3. IV. 4. IV. 5. IV. 6. IV. 7. IV. 8.

Singularities and exponential growth of coefficients Closure properties and computable bounds Rational and meromorphic functions Localization of singularities Singularities and functional equations Perspective

238 249 255 263 275 286

V. A PPLICATIONS OF R ATIONAL AND M EROMORPHIC A SYMPTOTICS V. 1. A roadmap to rational and meromorphic asymptotics V. 2. The supercritical sequence schema V. 3. Regular specifications and languages V. 4. Nested sequences, lattice paths, and continued fractions V. 5. Paths in graphs and automata V. 6. Transfer matrix models V. 7. Perspective

289 290 293 300 318 336 356 373

VI. S INGULARITY A NALYSIS OF G ENERATING F UNCTIONS VI. 1. A glimpse of basic singularity analysis theory VI. 2. Coefficient asymptotics for the standard scale VI. 3. Transfers VI. 4. The process of singularity analysis VI. 5. Multiple singularities VI. 6. Intermezzo: functions amenable to singularity analysis VI. 7. Inverse functions VI. 8. Polylogarithms VI. 9. Functional composition VI. 10. Closure properties VI. 11. Tauberian theory and Darboux’s method VI. 12. Perspective

375 376 380 389 392 398 401 402 408 411 418 433 437

VII. A PPLICATIONS OF S INGULARITY A NALYSIS VII. 1. A roadmap to singularity analysis asymptotics VII. 2. Sets and the exp–log schema VII. 3. Simple varieties of trees and inverse functions VII. 4. Tree-like structures and implicit functions VII. 5. Unlabelled non-plane trees and P´olya operators VII. 6. Irreducible context-free structures VII. 7. The general analysis of algebraic functions VII. 8. Combinatorial applications of algebraic functions VII. 9. Ordinary differential equations and systems VII. 10. Singularity analysis and probability distributions VII. 11. Perspective

439 441 445 452 467 475 482 493 506 518 532 538

VIII. S ADDLE - POINT A SYMPTOTICS VIII. 1. Landscapes of analytic functions and saddle-points VIII. 2. Saddle-point bounds VIII. 3. Overview of the saddle-point method VIII. 4. Three combinatorial examples VIII. 5. Admissibility VIII. 6. Integer partitions

541 543 546 551 558 564 574

CONTENTS

VIII. 7. VIII. 8. VIII. 9. VIII. 10. VIII. 11.

Saddle-points and linear differential equations. Large powers Saddle-points and probability distributions Multiple saddle-points Perspective

v

581 585 594 600 606

Part C. RANDOM STRUCTURES

609

IX. M ULTIVARIATE A SYMPTOTICS AND L IMIT L AWS IX. 1. Limit laws and combinatorial structures IX. 2. Discrete limit laws IX. 3. Combinatorial instances of discrete laws IX. 4. Continuous limit laws IX. 5. Quasi-powers and Gaussian limit laws IX. 6. Perturbation of meromorphic asymptotics IX. 7. Perturbation of singularity analysis asymptotics IX. 8. Perturbation of saddle-point asymptotics IX. 9. Local limit laws IX. 10. Large deviations IX. 11. Non-Gaussian continuous limits IX. 12. Multivariate limit laws IX. 13. Perspective

611 613 620 628 638 644 650 666 690 694 699 703 715 716

Part D. APPENDICES

719

Appendix A. AUXILIARY E LEMENTARY N OTIONS A.1. Arithmetical functions A.2. Asymptotic notations A.3. Combinatorial probability A.4. Cycle construction A.5. Formal power series A.6. Lagrange inversion A.7. Regular languages A.8. Stirling numbers. A.9. Tree concepts

721 721 722 727 729 730 732 733 735 737

Appendix B. BASIC C OMPLEX A NALYSIS B.1. Algebraic elimination B.2. Equivalent definitions of analyticity B.3. Gamma function B.4. Holonomic functions B.5. Implicit Function Theorem B.6. Laplace’s method B.7. Mellin transforms B.8. Several complex variables

739 739 741 743 748 753 755 762 767

Appendix C. C ONCEPTS OF P ROBABILITY T HEORY C.1. Probability spaces and measure C.2. Random variables C.3. Transforms of distributions

769 769 771 772

vi

CONTENTS

C.4. C.5.

Special distributions Convergence in law

774 776

B IBLIOGRAPHY

779

I NDEX

801

Preface A NALYTIC C OMBINATORICS aims at predicting precisely the properties of large structured combinatorial configurations, through an approach based extensively on analytic methods. Generating functions are the central objects of study of the theory. Analytic combinatorics starts from an exact enumerative description of combinatorial structures by means of generating functions: these make their first appearance as purely formal algebraic objects. Next, generating functions are interpreted as analytic objects, that is, as mappings of the complex plane into itself. Singularities determine a function’s coefficients in asymptotic form and lead to precise estimates for counting sequences. This chain of reasoning applies to a large number of problems of discrete mathematics relative to words, compositions, partitions, trees, permutations, graphs, mappings, planar configurations, and so on. A suitable adaptation of the methods also opens the way to the quantitative analysis of characteristic parameters of large random structures, via a perturbational approach. T HE APPROACH to quantitative problems of discrete mathematics provided by analytic combinatorics can be viewed as an operational calculus for combinatorics organized around three components. Symbolic methods develops systematic relations between some of the major constructions of discrete mathematics and operations on generating functions that exactly encode counting sequences. Complex asymptotics elaborates a collection of methods by which one can extract asymptotic counting information from generating functions, once these are viewed as analytic transformations of the complex domain. Singularities then appear to be a key determinant of asymptotic behaviour. Random structures concerns itself with probabilistic properties of large random structures. Which properties hold with high probability? Which laws govern randomness in large objects? In the context of analytic combinatorics, these questions are treated by a deformation (adding auxiliary variables) and a perturbation (examining the effect of small variations of such auxiliary variables) of the standard enumerative theory. The present book expounds this view by means of a very large number of examples concerning classical objects of discrete mathematics and combinatorics. The eventual goal is an effective way of quantifying metric properties of large random structures. vii

viii

PREFACE

Given its capacity of quantifying properties of large discrete structures, Analytic Combinatorics is susceptible to many applications, not only within combinatorics itself, but, perhaps more importantly, within other areas of science where discrete probabilistic models recurrently surface, like statistical physics, computational biology, electrical engineering, and information theory. Last but not least, the analysis of algorithms and data structures in computer science has served and still serves as an important incentive for the development of the theory. ⋆⋆⋆⋆⋆⋆ Part A: Symbolic methods. This part specifically develops Symbolic methods, which constitute a unified algebraic theory dedicated to setting up functional relations between counting generating functions. As it turns out, a collection of general (and simple) theorems provide a systematic translation mechanism between combinatorial constructions and operations on generating functions. This translation process is a purely formal one. In fact, with regard to basic counting, two parallel frameworks coexist—one for unlabelled structures and ordinary generating functions, the other for labelled structures and exponential generating functions. Furthermore, within the theory, parameters of combinatorial configurations can be easily taken into account by adding supplementary variables. Three chapters then form Part A: Chapter I deals with unlabelled objects; Chapter II develops labelled objects in a parallel way; Chapter III treats multivariate aspects of the theory suitable for the analysis of parameters of combinatorial structures. ⋆⋆⋆⋆⋆⋆ Part B: Complex asymptotics. This part specifically expounds Complex asymptotics, which is a unified analytic theory dedicated to the process of extracting asymptotic information from counting generating functions. A collection of general (and simple) theorems now provide a systematic translation mechanism between generating functions and asymptotic forms of coefficients. Five chapters form this part. Chapter IV serves as an introduction to complex-analytic methods and proceeds with the treatment of meromorphic functions, that is, functions whose singularities are poles, rational functions being the simplest case. Chapter V develops applications of rational and meromorphic asymptotics of generating functions, with numerous applications related to words and languages, walks and graphs, as well as permutations. Chapter VI develops a general theory of singularity analysis that applies to a wide variety of singularity types, such as square-root or logarithmic, and has consequences regarding trees as well as other recursively-defined combinatorial classes. Chapter VII presents applications of singularity analysis to 2–regular graphs and polynomials, trees of various sorts, mappings, context-free languages, walks, and maps. It contains in particular a discussion of the analysis of coefficients of algebraic functions. Chapter VIII explores saddle-point methods, which are instrumental in analysing functions with a violent growth at a singularity, as well as many functions with a singularity only at infinity (i.e., entire functions). ⋆⋆⋆⋆⋆⋆

PREFACE

ix

Part C: Random structures. This part is comprised of Chapter IX, which is dedicated to the analysis of multivariate generating functions viewed as deformation and perturbation of simple (univariate) functions. Many known laws of probability theory, either discrete or continuous, from Poisson to Gaussian and stable distributions, are found to arise in combinatorics, by a process combining symbolic methods, complex asymptotics, and perturbation methods. As a consequence, many important characteristics of classical combinatorial structures can be precisely quantified in distribution. ⋆⋆⋆⋆⋆⋆ Part D: Appendices. Appendix A summarizes some key elementary concepts of combinatorics and asymptotics, with entries relative to asymptotic expansions, languages, and trees, among others. Appendix B recapitulates the necessary background in complex analysis. It may be viewed as a self-contained minicourse on the subject, with entries relative to analytic functions, the Gamma function, the implicit function theorem, and Mellin transforms. Appendix C recalls some of the basic notions of probability theory that are useful in analytic combinatorics. ⋆⋆⋆⋆⋆⋆ T HIS BOOK is meant to be reader-friendly. Each major method is abundantly illustrated by means of concrete Examples1 treated in detail—there are scores of them, spanning from a fraction of a page to several pages—offering a complete treatment of a specific problem. These are borrowed not only from combinatorics itself but also from neighbouring areas of science. With a view to addressing not only mathematicians of varied profiles but also scientists of other disciplines, Analytic Combinatorics is self-contained, including ample appendices that recapitulate the necessary background in combinatorics, complex function theory, and probability. A rich set of short Notes—there are more than 450 of them—are inserted in the text2 and can provide exercises meant for self-study or for student practice, as well as introductions to the vast body of literature that is available. We have also made every effort to focus on core ideas rather than technical details, supposing a certain amount of mathematical maturity but only basic prerequisites on the part of our gentle readers. The book is also meant to be strongly problem-oriented, and indeed it can be regarded as a manual, or even a huge algorithm, guiding the reader to the solution of a very large variety of problems regarding discrete mathematical models of varied origins. In this spirit, many of our developments connect nicely with computer algebra and symbolic manipulation systems. C OURSES can be (and indeed have been) based on the book in various ways. Chapters I–III on Symbolic methods serve as a systematic yet accessible introduction to the formal side of combinatorial enumeration. As such it organizes transparently some of the rich material found in treatises3 such as those of Bergeron– Labelle–Leroux, Comtet, Goulden–Jackson, and Stanley. Chapters IV–VIII relative to Complex asymptotics provide a large set of concrete examples illustrating the power 1Examples are marked by “Example · · · ”.

2Notes are indicated by

· · · .

3References are to be found in the bibliography section at the end of the book.

x

PREFACE

of classical complex analysis and of asymptotic analysis outside of their traditional range of applications. This material can thus be used in courses of either pure or applied mathematics, providing a wealth of non-classical examples. In addition, the quiet but ubiquitous presence of symbolic manipulation systems provides a number of illustrations of the power of these systems while making it possible to test and concretely experiment with a great many combinatorial models. Symbolic systems allow for instance for fast random generation, close examination of non-asymptotic regimes, efficient experimentation with analytic expansions and singularities, and so on. Our initial motivation when starting this project was to build a coherent set of methods useful in the analysis of algorithms, a domain of computer science now welldeveloped and presented in books by Knuth, Hofri, Mahmoud, and Szpankowski, in the survey by Vitter–Flajolet, as well as in our earlier Introduction to the Analysis of Algorithms published in 1996. This book, Analytic Combinatorics, can then be used as a systematic presentation of methods that have proved immensely useful in this area; see in particular the Art of Computer Programming by Knuth for background. Studies in statistical physics (van Rensburg, and others), statistics (e.g., David and Barton) and probability theory (e.g., Billingsley, Feller), mathematical logic (Burris’ book), analytic number theory (e.g., Tenenbaum), computational biology (Waterman’s textbook), as well as information theory (e.g., the books by Cover–Thomas, MacKay, and Szpankowski) point to many startling connections with yet other areas of science. The book may thus be useful as a supplementary reference on methods and applications in courses on statistics, probability theory, statistical physics, finite model theory, analytic number theory, information theory, computer algebra, complex analysis, or analysis of algorithms. Acknowledgements. This book would be substantially different and much less informative without Neil Sloane’s Encyclopedia of Integer Sequences, Steve Finch’s Mathematical Constants, Eric Weisstein’s MathWorld, and the MacTutor History of Mathematics site hosted at St Andrews. We have also greatly benefited of the existence of open on-line archives such as Numdam, Gallica, GDZ (digitalized mathematical documents), ArXiv, as well as the Euler Archive. All the corresponding sites are (or at least have been at some stage) freely available on the Internet. Bruno Salvy and Paul Zimmermann have developed algorithms and libraries for combinatorial structures and generating functions that are based on the M APLE system for symbolic computations and that have proven to be extremely useful. We are deeply grateful to the authors of the free software Unix, Linux, Emacs, X11, TEX and LATEX as well as to the designers of the symbolic manipulation system M APLE for creating an environment that has proved invaluable to us. We also thank students in courses at Barcelona, Berkeley (MSRI), Bordeaux, ´ ´ Caen, Graz, Paris (Ecole Polytechnique, Ecole Normale Sup´erieure, University), Princeton, Santiago de Chile, Udine, and Vienna whose reactions have greatly helped us prepare a better book. Thanks finally to numerous colleagues for their contributions to this book project. In particular, we wish to acknowledge the support, help, and interaction provided at a high level by members of the Analysis of Algorithms (AofA) community, with a special mention for Nico´ Fusy, Hsien-Kuei Hwang, Svante Janson, Don Knuth, Guy las Broutin, Michael Drmota, Eric Louchard, Andrew Odlyzko, Daniel Panario, Carine Pivoteau, Helmut Prodinger, Bruno Salvy, Mich`ele Soria, Wojtek Szpankowski, Brigitte Vall´ee, Mark Daniel Ward, and Mark Wilson. In addition, Ed Bender, Stan Burris, Philippe Dumas, Svante Janson, Philippe Robert, Lo¨ıc Turban, and Brigitte Vall´ee have provided insightful suggestions and generous feedback that have

PREFACE

xi

led us to revise the presentation of several sections of this book and correct many errors. We were also extremely lucky to work with David Tranah, the mathematics editor of Cambridge University Press, who has been an exceptionally supportive (and patient) companion of this book project, throughout all these years. Finally, support of our home institutions (INRIA and Princeton University) as well as various grants (French government, European Union, and NSF) have contributed to making our collaboration possible.

An Invitation to Analytic Combinatorics diä d summeignÔmena aÎt te prä aÍt kaÈ prä llhla tn poikilan âstÈn peira; © d deØ jewroÌ ggnesjai toÌ mèllonta perÈ fÔsew eÊkìti lìgú

— P LATO, The Timaeus1

A NALYTIC C OMBINATORICS is primarily a book about combinatorics, that is, the study of finite structures built according to a finite set of rules. Analytic in the title means that we concern ourselves with methods from mathematical analysis, in particular complex and asymptotic analysis. The two fields, combinatorial enumeration and complex analysis, are organized into a coherent set of methods for the first time in this book. Our broad objective is to discover how the continuous may help us to understand the discrete and to quantify its properties. C OMBINATORICS is, as told by its name, the science of combinations. Given basic rules for assembling simple components, what are the properties of the resulting objects? Here, our goal is to develop methods dedicated to quantitative properties of combinatorial structures. In other words, we want to measure things. Say that we have n different items like cards or balls of different colours. In how many ways can we lay them on a table, all in one row? You certainly recognize this counting problem—finding the number of permutations of n elements. The answer is of course the factorial number n ! = 1 · 2 · . . . · n.

This is a good start, and, equipped with patience or a calculator, we soon determine that if n = 31, say, then the number of permutations is the rather large quantity 31 ! = 8222838654177922817725562880000000, .

an integer with 34 decimal digits. The factorials solve an enumeration problem, one that took mankind some time to sort out, because the sense of the “· · · ” in the formula for n! is not that easily grasped. In his book The Art of Computer Programming 1“So their combinations with themselves and with each other give rise to endless complexities, which anyone who is to give a likely account of reality must survey.” Plato speaks of Platonic solids viewed as idealized primary constituents of the physical universe.

1

2

AN INVITATION TO ANALYTIC COMBINATORICS

3

ր

4

ց

1

ր

5

ց

2

5 4 3

2 1

Figure 0.1. An example of the correspondence between an alternating permutation (top) and a decreasing binary tree (bottom): each binary node has two descendants, which bear smaller labels. Such constructions, which give access to generating functions and eventually provide solutions to counting problems, are the main subject of Part A.

(vol III, p. 23), Donald Knuth traces the discovery to the Hebrew Book of Creation (c. AD 400) and the Indian classic Anuyogadv¯ara-sutra (c. AD 500). Here is another more subtle problem. Assume that you are interested in permutations such that the first element is smaller than the second, the second is larger than the third, itself smaller than the fourth, and so on. The permutations go up and down and they are diversely known as up-and-down or zigzag permutations, the more dignified name being alternating permutations. Say that n = 2m + 1 is odd. An example is for n = 9: 8 7 9 3 ր ց ր ց ր ց ր ց 4 6 5 1 2 The number of alternating permutations for n = 1, 3, 5, . . . , 15 turns out to be 1, 2, 16, 272, 7936, 353792, 22368256, 1903757312. What are these numbers and how do they relate to the total number of permutations of corresponding size? A glance at the corresponding figures, that is, 1!, 3!, 5!, . . . , 15!, or 1, 6, 120, 5040, 362880, 39916800, 6227020800, 1307674368000, suggests that the factorials grow somewhat faster—just compare the lengths of the last two displayed lines. But how and by how much? This is the prototypical question we are addressing in this book. Let us now examine the counting of alternating permutations. In 1881, the French mathematician D´esir´e Andr´e made a startling discovery. Look at the first terms of the Taylor expansion of the trigonometric function tan z: z3 z5 z7 z9 z 11 z + 2 + 16 + 272 + 7936 + 353792 + ··· . 1! 3! 5! 7! 9! 11! The counting sequence for alternating permutations, 1, 2, 16, . . ., curiously surfaces. We say that the function on the left is a generating function for the numerical sequence (precisely, a generating function of the exponential type, due to the presence of factorials in the denominators). tan z = 1

AN INVITATION TO ANALYTIC COMBINATORICS

3

Andr´e’s derivation may nowadays be viewed very simply as reflecting the construction of permutations by means of certain labelled binary trees (Figure 0.1 and p. 143): given a permutation σ a tree can be obtained once σ has been decomposed as a triple hσ L , max, σ R i, by taking the maximum element as the root, and appending, as left and right subtrees, the trees recursively constructed from σ L and σ R . Part A of this book develops at length symbolic methods by which the construction of the class T of all such trees, T = 1 ∪ (T , max , T ) , translates into an equation relating generating functions, Z z T (z) = z + T (w)2 dw. P

0

z n /n!

is the exponential generating function of the In this equation, T (z) := n Tn sequence (Tn ), where Tn is the number of alternating permutations of (odd) length n. There is a compelling formal analogy between the combinatorial specification and its generating function: Unions (∪) give rise to sums (+), max-placement gives an R integral ( ), forming a pair of trees corresponds to taking a square ([·]2 ). At this stage, we know that T (z) must solve the differential equation d T (z) = 1 + T (z)2 , dz

T (0) = 0,

which, by classical manipulations2, yields the explicit form T (z) = tan z. The generating function then provides a simple algorithm to compute the coefficients recurrently. Indeed, the formula, z− sin z tan z = = cos z 1−

z3 3! z2 2!

+

+

z5 5! z4 4!

− ···

− ···

,

implies, for n odd, the relation (extract the coefficient of z n in T (z) cos z = sin z) a! a n n = Tn−4 − · · · = (−1)(n−1)/2 , where Tn−2 + Tn − b 4 2 b!(a − b)! is the conventional notation for binomial coefficients. Now, the exact enumeration problem may be regarded as solved since a very simple algorithm is available for determining the counting sequence, while the generating function admits an explicit expression in terms of well-known mathematical objects. A NALYSIS, by which we mean mathematical analysis, is often described as the art and science of approximation. How fast do the factorial and the tangent number sequences grow? What about comparing their growths? These are typical problems of analysis. 2We have T ′ /(1 + T 2 ) = 1, hence arctan(T ) = z and T = tan z.

4

AN INVITATION TO ANALYTIC COMBINATORICS

4

2

K6

K4

K2

0

2

4

6

z

K2 K4

Figure 0.2. Two views of the function z 7→ tan z. Left: a plot for √ real values of z ∈ [−6, 6]. Right: the modulus | tan z| when z = x + i y (with i = −1) is assigned complex values in the square ±6 ± 6i. As developed at length in Part B, it is the nature of singularities in the complex domain that matters.

First, consider the number of permutations, n!. Quantifying its growth, as n gets large, takes us to the realm of asymptotic analysis. The way to express factorial numbers in terms of elementary functions is known as Stirling’s formula3 √ n! ∼ n n e−n 2π n,

where the ∼ sign means “approximately equal” (in the precise sense that the ratio of both terms tends to 1 as n gets large). This beautiful formula, associated with the name of the Scottish mathematician James Stirling (1692–1770), curiously involves both the basis e of natural logarithms and the perimeter 2π of the circle. Certainly, you cannot get such a thing without analysis. As a first step, there is an estimate Z n n n X , log j ∼ log n! = log x d x ∼ n log e 1 j=1

n n e−n

explaining at least the term, but already requiring a certain amount of elementary calculus. (Stirling’s formula precisely came a few decades after the fundamental bases of calculus had been laid by Newton and Leibniz.) Note the utility of Stirling’s formula: it tells us almost instantly that 100! has 158 digits, while 1000! borders the astronomical 102568 . We are now left with estimating the growth of the sequence of tangent numbers, Tn . The analysis leading to the derivation of the generating function tan(z) has been so far essentially algebraic or “formal”. Well, we can plot the graph of the tangent function, for real values of its argument and see that the function becomes infinite at the points ± π2 , ±3 π2 , and so on (Figure 0.2). Such points where a function ceases to be 3 In this book, we shall encounter five different proofs of Stirling’s formula, each of interest for its

own sake: (i) by singularity analysis of the Cayley tree function (p. 407); (ii) by singularity analysis of polylogarithms (p. 410); (iii) by the saddle-point method (p. 555); (iv) by Laplace’s method (p. 760); (v) by the Mellin transform method applied to the logarithm of the Gamma function (p. 766).

AN INVITATION TO ANALYTIC COMBINATORICS

5

“smooth” (differentiable) are called singularities. By methods amply developed in this book, it is the local nature of a generating function at its “dominant” singularities (i.e., the ones closest to the origin) that determines the asymptotic growth of the sequence of coefficients. From this perspective, the basic fact that tan z has dominant singularities at ± π2 enables us to reason as follows: first approximate the generating function tan z near its two dominant singularities, namely, tan(z)

∼

z→±π/2 π 2

8z ; − 4z 2

then extract coefficients of this approximation; finally, get in this way a valid approximation of coefficients: n+1 Tn 2 ∼ 2· (n odd). n! n→∞ π With present day technology, we also have available symbolic manipulation systems (also called “computer algebra” systems) and it is not difficult to verify the accuracy of our estimates. Here is a small pyramid for n = 3, 5, . . . , 21, 2 16 272 7936 353792 22368256 1903757312 209865342976 29088885112832 4951498053124096 (Tn )

1 15 27 1 793 5 35379 1 2236825 1 1903757 267 20986534 2434 290888851 04489 495149805 2966307 (Tn⋆ )

comparing the exact values of Tn against the approximations Tn⋆ , where (n odd) $ n+1 % 2 ⋆ , Tn := 2 · n! π and discrepant digits of the approximation are displayed in bold. For n = 21, the error is only of the order of one in a billion. Asymptotic analysis (p. 269) is in this case wonderfully accurate. In the foregoing discussion, we have played down a fact—one that is important. When investigating generating functions from an analytic standpoint, one should generally assign complex values to arguments not just real ones. It is singularities in the complex plane that matter and complex analysis is needed in drawing conclusions regarding the asymptotic form of coefficients of a generating function. Thus, a large portion of this book relies on a complex analysis technology, which starts to be developed in Part B dedicated to Complex asymptotics. This approach to combinatorial enumeration parallels what happened in the nineteenth century, when Riemann first recognized P the deep relation between complex analytic properties of the zeta function, ζ (s) := 1/n s , and the distribution of primes, eventually leading to the long-sought proof of the Prime Number Theorem by Hadamard and de la Vall´ee-Poussin in 1896. Fortunately, relatively elementary complex analysis suffices for our purposes, and we

6

AN INVITATION TO ANALYTIC COMBINATORICS

Figure 0.3. The collection of binary trees with n = 0, 1, 2, 3 binary nodes, with respective cardinalities 1, 1, 2, 5.

can include in this book a complete treatment of the fragment of the theory needed to develop the fundamentals of analytic combinatorics. Here is yet another example illustrating the close interplay between combinatorics and analysis. When discussing alternating permutations, we have enumerated binary trees bearing distinct integer labels that satisfy a constraint—to decrease along branches. What about the simpler problem of determining the number of possible shapes of binary trees? Let Cn be the number of binary trees that have n binary branching nodes, hence n + 1 “external nodes”. It is not hard to come up with an exhaustive listing for small values of n (Figure 0.3), from which we determine that C0 = 1,

C1 = 1,

C2 = 2,

C3 = 5,

C4 = 14,

C5 = 42.

These numbers are probably the most famous ones of combinatorics. They have come to be known as the Catalan numbers as a tribute to the Franco-Belgian mathematician Eug`ene Charles Catalan (1814–1894), but they already appear in the works of Euler and Segner in the second half of the eighteenth century (see p. 20). In his reference treatise Enumerative Combinatorics, Stanley, over 20 pages, lists a collection of some 66 different types of combinatorial structures that are enumerated by the Catalan numbers. First, one can write a combinatorial equation, very much in the style of what has been done earlier, but without labels: C

=

∪

2

(C, • , C) .

(Here, the 2–symbol represents an external node.) With symbolic methods, it is easy to see that the ordinary generating function of the Catalan numbers, defined as X C(z) := Cn z n , n≥0

satisfies an equation that is a direct reflection of the combinatorial definition, namely, C(z)

=

1

+

z C(z)2 .

This is a quadratic equation whose solution is √ 1 − 1 − 4z . C(z) = 2z

AN INVITATION TO ANALYTIC COMBINATORICS

7

3 0.55

0.5

2.5

0.45 2 0.4

0.35

1.5

0.3 1 0.25 -0.3

-0.2

-0.1

0

0.1

0.2

10

20

30

40

50

Figure 0.4. Left: the real values of the Catalan generating function, which has a square-root singularity at z = 14 . Right: the ratio Cn /(4n n −3/2 ) plotted together √ . with its asymptote at 1/ π = 0.56418. The correspondence between singularities and asymptotic forms of coefficients is the central theme of Part B.

Then, by means of Newton’s theorem relative to the expansion of (1 + x)α , one finds easily (x = −4z, α = 21 ) the closed form expression 2n 1 . Cn = n+1 n Stirling’s asymptotic formula now comes to the rescue: it implies 4n where Cn⋆ := √ . π n3 . This last approximation is quite usable4: it gives C1⋆ = 2.25 (whereas C1 = 1), which is off by a factor of 2, but the error drops to 10% already for n = 10, and it appears to be less than 1% for any n ≥ 100. A plot of the generating function C(z) in Figure 0.4 illustrates the fact that C(z) has a singularity at z = 41 as it ceases to be differentiable (its derivative becomes infinite). That singularity is quite different from a pole and for natural reasons it is known as a square-root singularity. As we shall see repeatedly, under suitable conditions in the complex plane, a square root singularity for a function at a point ρ invariably entails an asymptotic form ρ −n n −3/2 for its coefficients. More generally, it suffices to estimate a generating function near a singularity in order to deduce an asymptotic approximation of its coefficients. This correspondence is a major theme of the book, one that motivates the five central chapters (Chapters IV to VIII). A consequence of the complex analytic vision of combinatorics is the detection of universality phenomena in large random structures. (The term is originally borrowed from statistical physics and is nowadays finding increasing use in areas of mathematics such as probability theory.) By universality is meant here that many quantitative Cn ∼ Cn⋆

. 4We use α = d to represent a numerical approximation of the real α by the decimal d, with the last

digit of d being at most ±1 from its actual value.

8

AN INVITATION TO ANALYTIC COMBINATORICS

properties of combinatorial structures only depend on a few global features of their definitions, not on details. For instance a growth in the counting sequence of the form K · An n −3/2 , arising from a square-root singularity, will be shown to be universal across all varieties of trees determined by a finite set of allowed node degrees—this includes unary– binary trees, ternary trees, 0–11–13 trees, as well as many variations such as non-plane trees and labelled trees. Even though generating functions may become arbitrarily complicated—as in an algebraic function of a very high degree or even the solution to an infinite functional equation—it is still possible to extract with relative ease global asymptotic laws governing counting sequences. R ANDOMNESS is another ingredient in our story. How useful is it to determine, exactly or approximately, counts that may be so large as to require hundreds if not thousands of digits in order to be written down? Take again the example of alternating permutations. When estimating their number, we have indeed quantified the proportion of these among all permutations. In other words, we have been predicting the probability that a random permutation of some size n is alternating. Results of this sort are of interest in all branches of science. For instance, biologists routinely deal with genomic sequences of length 105 , and the interpretation of data requires developing enumerative or probabilistic models where the number of possibilities is of 5 the order of 410 . The language of probability theory then proves of great convenience when discussing characteristic parameters of discrete structures, since we can interpret exact or asymptotic enumeration results as saying something concrete about the likelihood of values that such parameters assume. Equally important of course are results from several areas of probability theory: as demonstrated in the last chapter of this book, such results merge extremely well with the analytic–combinatorial framework. Say we are now interested in runs in permutations. These are the longest fragments of a permutation that already appear in (increasing) sorted order. Here is a permutation with 4 runs, separated by vertical bars: 2 5 8 | 3 9 | 1 4 7 | 6. Runs naturally present in a permutation are for instance exploited by a sorting algorithm called “natural list mergesort”, which builds longer and longer runs, starting from the original ones and merging them until the permutation is eventually sorted. For our understanding of this algorithm, it is then of obvious interest to quantify how many runs a permutation is likely to have. Let Pn,k be the number of permutations of size n having k runs. Then, the problem is once more best approached by generating functions and one finds that the coefficient of u k z n inside the bivariate generating function, z3 1−u z2 u(u + 1) + u(u 2 + 4u + 1) + · · · , = 1 + zu + 2! 3! 1 − ue z(1−u) gives the desired numbers Pn,k /n!. (A simple way of establishing the last formula bases itself on the tree decomposition of permutations and on the symbolic method; the numbers Pn,k , whose importance seems to have been first recognized by Euler, P(z, u) ≡

AN INVITATION TO ANALYTIC COMBINATORICS

9

10

0.6 5

0.5 z 0

0.2

0.4

0.6

0.4 0.8

1

1.2

0

0.3 0.2 -5

0.1 0

0.2

0.4

0.6

0.8

1

-10

Figure 0.5. Left: A partial plot of the real values of the Eulerian generating function z 7→ P(z, u) for z ∈ [0, 54 ], illustrates the presence of a movable pole for A as u varies between 0 and 45 . Right: A suitable superposition of the histograms of the distribution of the number of runs, for n = 2, . . . , 60, reveals the convergence to a Gaussian distribution (p. 695). Part C relates systematically the analysis of such a collection of singular behaviours to limit distributions.

are related to the Eulerian numbers, p. 210.) From here, we can easily determine effectively the mean, variance, and even the higher moments of the number of runs that a random permutation has: it suffices to expand blindly, or even better with the help of a computer, the bivariate generating function above as u → 1: 1 z (2 − z) 1 z2 6 − 4 z + z2 1 + (u − 1) + (u − 1)2 + · · · . 1−z 2 (1 − z)2 2 (1 − z)3 When u = 1, we just enumerate all permutations: this is the constant term 1/(1 − z) equal to the exponential generating function of all permutations. The coefficient of the term u − 1 gives the generating function of the mean number of runs, the next one provides the second moment, and so on. In this way, we discover the expectation and standard deviation of the number of runs in a permutation of size n: r n+1 n+1 µn = , σn = . 2 12 Then, by easy analytic–probabilistic inequalities (Chebyshev inequalities) that otherwise form the basis of what is known as the second moment method, we learn that the distribution of the number of runs is concentrated around its mean: in all likelihood, if one takes a random permutation, the number of its runs is going to be very close to its mean. The effects of such quantitative laws are quite tangible. It suffices to draw a sample of one element for n = 30 to get, for instance: 13, 22, 29|12, 15, 23|8, 28|18|6, 26|4, 10, 16|1, 5, 27|3, 14, 17, 20|2, 21, 30|25|11, 19|9|7, 24.

For n = 30, the mean is 15 12 , and this sample comes rather close as it has 13 runs. We shall furthermore see in Chapter IX that even for moderately large permutations of size 10 000 and beyond, the probability for the number of observed runs to deviate

10

AN INVITATION TO ANALYTIC COMBINATORICS 2

y 1.5

1

0.5

0 0

0.05

0.1

0.15

0.2

0.25

0.3

Figure 0.6. Left: The bivariate generating function z 7→ C(z, u) enumerating binary trees by size and number of leaves exhibits consistently a square-root singularity, for several values of u. Right: a binary tree of size 300 drawn uniformly at random has 69 leaves. As shown in Part C, singularity perturbation properties are at the origin of many randomness properties of combinatorial structures.

by more than 10% from the mean is less than 10−65 . As witnessed by this example, much regularity accompanies properties of large combinatorial structures. More refined methods combine the observation of singularities with analytic results from probability theory (e.g., continuity theorems for characteristic functions). In the case of runs in permutations, the quantity P(z, u) viewed as a function of z when u is fixed appears to have a pole: this fact is suggested by Figure 0.5 [left]. Then we are confronted with a fairly regular deformation of the generating function of all permutations. A parameterized version (with parameter u) of singularity analysis then gives access to a description of the asymptotic behaviour of the Eulerian numbers Pn,k . This enables us to describe very precisely what goes on: in a random permutation of large size n, once it has been centred by its mean and scaled by its standard deviation, the distribution of the number of runs is asymptotically Gaussian; see Figure 0.5 [right]. A somewhat similar type of situation prevails for binary trees. Say we are interested in leaves (also sometimes figuratively known as “cherries”) in trees: these are binary nodes that are attached to two external nodes (2). Let Cn,k be the number of trees P of size n having k leaves. The bivariate generating function C(z, u) := n,k Cn,k z n u k encodes all the information relative to leaf statistics in random binary trees. A modification of previously seen symbolic arguments shows that C(z, u) still satisfies a quadratic equation resulting in the explicit form, p 1 − 1 − 4z + 4z 2 (1 − u) . C(z, u) = 2z This reduces to C(z) for u = 1, as it should, and the bivariate generating function C(z, u) is a deformation of C(z) as u varies. In fact, the network of curves of Figure 0.6 for several fixed values of u illustrates the presence of a smoothly varying square-root singularity (the aspect of each curve is similar to that of Figure 0.4). It is possible to analyse the perturbation induced by varying values of u, to the effect that

AN INVITATION TO ANALYTIC COMBINATORICS

11

Combinatorial structures

SYMBOLIC METHODS (Part A) Generating functions, OGF, EGF Chapters I, II

Multivariate generating functions, MGF Chapter III

COMPLEX ASYMPTOTICS (Part B) Singularity analysis Chapters IV, V, VI, VII Saddle−point method Chapter VIII

Exact counting

RANDOM STRUCTURES (Part C) Multivariate asymptotics and limit laws Chapter IX

Limit laws, large deviations

Asymptotic counting, moments of parameters

Figure 0.7. The logical structure of Analytic Combinatorics.

C(z, u) is of the global analytic type r 1−

z , ρ(u)

for some analytic ρ(u). The already evoked process of singularity analysis then shows that the probability generating function of the number of leaves in a tree of size n is of the rough form ρ(1) n (1 + o(1)) . ρ(u) This is known as a “quasi-powers” approximation. It resembles very much the probability generating function of a sum of n independent random variables, a situation that gives rise to the classical Central Limit Theorem of probability theory. Accordingly, one gets that the limit distribution of the number of leaves in a large random binary tree is Gaussian. In abstract terms, the deformation induced by the secondary parameter (here, the number of leaves, previously, the number of runs) is susceptible to a perturbation analysis, to the effect that a singularity gets smoothly displaced without changing its nature (here, a square root singularity, earlier a pole) and a limit law systematically results. Again some of the conclusions can be verified even by very small samples: the single tree of size 300 drawn at random and displayed in Figure 0.6 (right) has 69 leaves, whereas the expected value of this number . is = 75.375 and the standard deviation is a little over 4. In a large number of cases of which this one is typical, we find metric laws of combinatorial structures that govern large structures with high probability and eventually make them highly predictable. Such randomness properties form the subject of Part C of this book dedicated to random structures. As our earlier description implies, there is an extreme degree of

12

AN INVITATION TO ANALYTIC COMBINATORICS

generality in this analytic approach to combinatorial parameters, and after reading this book, the reader will be able to recognize by herself dozens of such cases at sight, and effortlessly establish the corresponding theorems. A RATHER ABSTRACT VIEW of combinatorics emerges from the previous discussion; see Figure 0.7. A combinatorial class, as regards its enumerative properties, can be viewed as a surface in four-dimensional real space: this is the graph of its generating function, considered as a function from the set C ∼ = R2 of complex numbers to itself, and is otherwise known as a Riemann surface. This surface has “cracks”, that is, singularities, which determine the asymptotic behaviour of the counting sequence. A combinatorial construction (such as those freely forming sequences, sets, and so on) can then be examined through the effect it has on singularities. In this way, seemingly different types of combinatorial structures appear to be subject to common laws governing not only counting but also finer characteristics of combinatorial structures. For the already discussed case of universality in tree enumerations, additional universal laws valid across many tree varieties constrain for instance height (which, with high probability, is proportional to the square root of size) and the number of leaves (which is invariably normal in the asymptotic limit). What happens regarding probabilistic properties of combinatorial parameters is this. A parameter of a combinatorial class is fully determined by a bivariate generating function, which is a deformation of the basic counting generating function of the class (in the sense that setting the secondary variable u to 1 erases the information relative to the parameter and leads back to the univariate counting generating function). Then, the asymptotic distribution of a parameter of interest is characterized by a collection of surfaces, each having its own singularities. The way the singularities’ locations move or their nature changes under deformation encodes all the necessary information regarding the distribution of the parameter under consideration. Limit laws for combinatorial parameters can then be obtained and the corresponding phenomena can be organized into broad categories, called schemas. It would be inconceivable to attain such a far-reaching classification of metric properties of combinatorial structures by elementary real analysis alone. Objects on which we are going to inflict the treatments just described include many of the most important ones of discrete mathematics, as well as the ones that surface recurrently in several branches of the applied sciences. We shall thus encounter words and sequences, trees and lattice paths, graphs of various sorts, mappings, allocations, permutations, integer partitions and compositions, polyominoes and planar maps, to name but a few. In most cases, their principal characteristics will be finely quantified by the methods of analytic combinatorics. This book indeed develops a coherent theory of random combinatorial structures based on a powerful analytic methodology. Literally dozens of quite diverse combinatorial types can then be treated by a logically transparent chain. You will not find ready-made answers to all questions in this book, but, hopefully, methods that can be successfully used to address a great many of them. Bienvenue! Welcome!

Part A

SYMBOLIC METHODS

I

Combinatorial Structures and Ordinary Generating Functions Laplace discovered the remarkable correspondence between set theoretic operations and operations on formal power series and put it to great use to solve a variety of combinatorial problems. — G IAN –C ARLO ROTA [518]

I. 1. I. 2. I. 3. I. 4. I. 5. I. 6. I. 7.

Symbolic enumeration methods Admissible constructions and specifications Integer compositions and partitions Words and regular languages Tree structures Additional constructions Perspective

16 24 39 49 64 83 92

This chapter and the next are devoted to enumeration, where the problem is to determine the number of combinatorial configurations described by finite rules, and do so for all possible sizes. For instance, how many different words are there of length 17? Of length n, for general n? These questions are easy, but what if some constraints are imposed, e.g., no four identical elements in a row? The solutions are exactly encoded by generating functions, and, as we shall see, generating functions are the central mathematical object of combinatorial analysis. We examine here a framework that, contrary to traditional treatments based on recurrences, explains the surprising efficiency of generating functions in the solution of combinatorial enumeration problems. This chapter serves to introduce the symbolic approach to combinatorial enumerations. The principle is that many general set-theoretic constructions admit a direct translation as operations over generating functions. This principle is made concrete by means of a dictionary that includes a collection of core constructions, namely the operations of union, cartesian product, sequence, set, multiset, and cycle. Supplementary operations such as pointing and substitution can also be similarly translated. In this way, a language describing elementary combinatorial classes is defined. The problem of enumerating a class of combinatorial structures then simply reduces to finding a proper specification, a sort of computer program for the class expressed in terms of the basic constructions. The translation into generating functions becomes, after this, a purely mechanical symbolic process. We show here how to describe in such a context integer partitions and compositions, as well as many word and tree enumeration problems, by means of ordinary 15

16

I. COMBINATORIAL STRUCTURES AND ORDINARY GENERATING FUNCTIONS

generating functions. A parallel approach, developed in Chapter II, applies to labelled objects—in contrast the plain structures considered in this chapter are called unlabelled. The methodology is susceptible to multivariate extensions with which many characteristic parameters of combinatorial objects can also be analysed in a unified manner: this is to be examined in Chapter III. The symbolic method also has the great merit of connecting nicely with complex asymptotic methods that exploit analyticity properties and singularities, to the effect that precise asymptotic estimates are usually available whenever the symbolic method applies—a systematic treatment of these aspects forms the basis of Part B of this book Complex asymptotics (Chapters IV–VIII). I. 1. Symbolic enumeration methods First and foremost, combinatorics deals with discrete objects, that is, objects that can be finitely described by construction rules. Examples are words, trees, graphs, permutations, allocations, functions from a finite set into itself, topological configurations, and so on. A major question is to enumerate such objects according to some characteristic parameter(s). Definition I.1. A combinatorial class, or simply a class, is a finite or denumerable set on which a size function is defined, satisfying the following conditions: (i) the size of an element is a non-negative integer; (ii) the number of elements of any given size is finite. If A is a class, the size of an element α ∈ A is denoted by |α|, or |α|A in the few cases where the underlying class needs to be made explicit. Given a class A, we consistently denote by An the set of objects in A that have size n and use the same group of letters for the counts An = card(An ) (alternatively, also an = card(An )). An axiomatic presentation is then as follows: a combinatorial class is a pair (A, | · |) where A is at most denumerable and the mapping | · | ∈ (A 7→ Z≥0 ) is such that the inverse image of any integer is finite. Definition I.2. The counting sequence of a combinatorial class is the sequence of integers (An )n≥0 where An = card(An ) is the number of objects in class A that have size n. Example I.1. Binary words. Consider first the set W of binary words, which are sequences of elements taken from the binary alphabet A = {0,1}, W := {ε, 0, 1, 00, 01, 10, 11, 000, 001, 010, . . . , 1001101, . . . }, with ε the empty word. Define size to be the number of letters that a word comprises. There are two possibilities for each letter and possibilities multiply, so that the counting sequence (Wn ) satisfies Wn = 2n . (This sequence has a well-known legend associated with the invention of the game of chess: the inventor was promised by his king one grain of rice for the first square of the chessboard, two for the second, four for the third, and so on. The king naturally could not deliver the promised 264 − 1 grains!) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

I. 1. SYMBOLIC ENUMERATION METHODS

17

Figure I.1. The collection T of all triangulations of regular polygons (with size defined as the number of triangles) is a combinatorial class, whose counting sequence starts as T0 = 1, T1 = 1, T2 = 2, T3 = 5, T4 = 14, T5 = 42. Example I.2. Permutations. A permutation of size n is by definition a bijective mapping of the integer interval1 In := [1 . . n]. It is thus representable by an array, 1 2 n σ1 σ2 · · · σn , or equivalently by the sequence σ1 σ2 · · · σn of its distinct elements. The set P of permutations is P = {. . . , 12, 21, 123, 132, 213, 231, 312, 321, 1234, . . . , 532614, . . . }, For a permutation written as a sequence of n distinct numbers, there are n places where one can accommodate n, then n − 1 remaining places for n − 1, and so on. Therefore, the number Pn of permutations of size n satisfies Pn = n! = 1 · 2 · . . . · n . As indicated in our Invitation chapter (p. 2), this formula has been known for at least fifteen centuries. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Example I.3. Triangulations. The class T of triangulations comprises triangulations of convex polygonal domains which are decompositions into non-overlapping triangles (taken up to smooth deformations of the plane). We define the size of a triangulation to be the number of triangles it is composed of. For instance, a convex quadrilateral ABC D can be decomposed into two triangles in two ways (by means of either the diagonal AC or the diagonal B D); similarly, there are five different ways to dissect a convex pentagon into three triangles: see Figure I.1. Agreeing that T0 = 1, we then find T0 = 1,

T1 = 1,

T2 = 2,

T3 = 5,

T4 = 14,

T5 = 42.

It is a non-trivial combinatorial result due to Euler and Segner [146, 196, 197] around 1750 that the number Tn of triangulations is 1 (2n)! 2n (1) Tn = = , n+1 n (n + 1)! n! a central quantity of combinatorial analysis known as a Catalan number: see our Invitation, p. 7, the historical synopsis on p. 20, the discussion on p. 35, and Subsection I. 5.3, p. 73. 1We borrow from computer science the convenient practice of denoting an integer interval by 1 . . n or

[1 . . n], whereas [0, n] represents a real interval.

18

I. COMBINATORIAL STRUCTURES AND ORDINARY GENERATING FUNCTIONS

Following Euler [196], the counting of triangulations is best approached by generating functions: see again Figure I.2, p. 20 for historical context. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Although the previous three examples are simple enough, it is generally a good idea, when confronted with a combinatorial enumeration problem, to determine the initial values of counting sequences, either by hand or better with the help of a computer, somehow. Here, we find:

(2)

n

0

1

2

3

4

5

6

7

8

9

10

Wn Pn Tn

1 1 1

2 1 1

4 2 2

8 6 5

16 24 14

32 120 42

64 720 132

128 5040 429

256 40320 1430

512 362880 4862

1024 3628800 16796

Such an experimental approach may greatly help identify sequences. For instance, had we not known the formula (1) for triangulations, observing unusual factorizations such as T40 = 22 · 5 · 72 · 11 · 23 · 43 · 47 · 53 · 59 · 61 · 67 · 71 · 73 · 79, which contains all prime numbers from 43 to 79 and no prime larger than 80, would quickly put us on the track of the right formula. There even exists nowadays a huge On-line Encyclopedia of Integer Sequences (EIS) due to Sloane that is available in electronic form [543] (see also an earlier book by Sloane and Plouffe [544]) and contains more than 100 000 sequences. Indeed, the three sequences (Wn ), (Pn ), and (Tn ) are respectively identified2 as EIS A000079, EIS A000142, and EIS A000108.

I.1. Necklaces. How many different types of necklace designs can you form with n beads, each having one of two colours, ◦ and •, where it is postulated that orientation matters? Here are the possibilities for n = 1, 2, 3, . This is equivalent to enumerating circular arrangements of two letters and an exhaustive listing program can be based on the smallest lexicographical representation of each word, as suggested by (20), p. 26. The counting sequence starts as 2, 3, 4, 6, 8, 14, 20, 36, 60, 108, 188, 352 and constitutes EIS A000031. [An explicit formula appears later in this chapter (p. 64).] What if two necklace designs that are mirror images of one another are identified?

I.2. Unimodal permutations. Such a permutation has exactly one local maximum. In other words it is of the form σ1 · · · σn with σ1 < σ2 < · · · < σk = n and σk = n > σk+1 > · · · > σn , for some k ≥ 1. How many such permutations are there of size n? For n = 5, the number is 16: the permutations are 12345, 12354, 12453, 12543, 13452, 13542, 14532 and 15432 and their reversals. [Due to Jon Perry, see EIS A000079.]

It is also of interest to note that words and permutations may be enumerated using the most elementary counting principles, namely, for finite sets B and C card(B ∪ C) = card(B) + card(C) (provided B ∩ C = ∅) (3) card(B × C) = card(B) · card(C).

2 Throughout this book, a reference such as EIS Axxx points to Sloane’s Encyclopedia of Integer

Sequences [543]. The database contains more than 100 000 entries.

I. 1. SYMBOLIC ENUMERATION METHODS

19

We shall see soon that these principles, which lie at the basis of our very concept of number, admit a powerful generalization (Equation (19), p. 23, below). Next, for combinatorial enumeration purposes, it proves convenient to identify combinatorial classes that are merely variants of one another. Definition I.3. Two combinatorial classes A and B are said to be (combinatorially) isomorphic, which is written A ∼ = B, iff their counting sequences are identical. This condition is equivalent to the existence of a bijection from A to B that preserves size, and one also says that A and B are bijectively equivalent. We normally identify isomorphic classes and accordingly employ a plain equality sign (A = B). We then confine the notation A ∼ = B to stress cases where combinatorial isomorphism results from some non-trivial transformation. Definition I.4. The ordinary generating function (OGF) of a sequence (An ) is the formal power series (7)

A(z) =

∞ X

An z n .

n=0

The ordinary generating function (OGF) of a combinatorial class A is the generating function of the numbers An = card(An ). Equivalently, the OGF of class A admits the combinatorial form X (8) A(z) = z |α| . α∈A

It is also said that the variable z marks size in the generating function. The combinatorial form of an OGF in (8) results straightforwardly from observing that the term z n occurs as many times as there are objects in A having size n. We stress the fact that, at this stage and throughout Part A, generating functions are manipulated algebraically as formal sums; that is, they are considered as formal power series (see the framework of Appendix A.5: Formal power series, p. 730) Naming convention. We adhere to a systematic naming convention: classes, their counting sequences, and their generating functions are systematically denoted by the same groups of letters: for instance, A for a class, {An } (or {an }) for the counting sequence, and A(z) (or a(z)) for its OGF. Coefficient extraction. We let generally [z n ] f (z) denote of extractPthe operation ing the coefficient of z n in the formal power series f (z) = f n z n , so that (9)

X [z n ] fn zn = fn . n≥0

(The coefficient extractor [z n ] f (z) reads as “coefficient of z n in f (z)”.)

20

I. COMBINATORIAL STRUCTURES AND ORDINARY GENERATING FUNCTIONS

1. On September 4, 1751, Euler writes to his friend Goldbach [196]: Ich bin neulich auf eine Betrachtung gefallen, welche mir nicht wenig merkw¨urdig vorkam. Dieselbe betrifft, auf wie vielerley Arten ein gegebenes polygonum durch Diagonallinien in triangula zerchnitten werden k¨onne.

I have recently encountered a question, which appears to me rather noteworthy. It concerns the number of ways in which a given [convex] polygon can be decomposed into triangles by diagonal lines.

Euler then describes the problem (for an n–gon, i.e., (n − 2) triangles) and concludes: Setze ich nun die Anzahl dieser verschiedenen Arten = x [. . . ]. Hieraus habe ich nun den Schluss gemacht, dass generaliter sey 2.6.10.14....(4n − 10) x= 2.3.4.5....(n − 1) [. . . ] Ueber die Progression der Zahlen 1, 2, 5, 14, 42, 132, etc. habe ich auch diese Eigenschaft angemerket, dass 1 + 2a +√5a 2 +

1−4a . 14a 3 + 42a 4 + 132a 5 + etc. = 1−2a− 2aa

Let me now denote by x this number of ways [. . . ]. I have then reached the conclusion that in all generality 2.6.10.14....(4n − 10) x= 2.3.4.5....(n − 1) [. . . ] Regarding the progression of the numbers 1, 2, 5, 14, 42, 132, and so on, I have also observed the following property: 1 + 2a +√5a 2 +

1−4a . 14a 3 + 42a 4 + 132a 5 + etc. = 1−2a− 2aa

Thus, as early as 1751, Euler knew the solution as well as the associated generating function. From his writing, it is however unclear whether he had found complete proofs. 2. In the course of the 1750s, Euler communicated the problem, together with initial elements of the counting sequence, to Segner, who writes in his publication [146] dated 1758: “The great Euler has benevolently communicated these numbers to me; the way in which he found them, and the law of their progression having remained hidden to me” [“quos numeros mecum beneuolus communicauit summus Eulerus; modo, quo eos reperit, atque progressionis ordine, celatis”]. Segner develops a recurrence approach to Catalan numbers. By a root decomposition analogous to ours, on p. 35, he proves (in our notation, for decompositions into n triangles) (4)

Tn =

n−1 X k=0

Tk Tn−1−k ,

T0 = 1,

a recurrence by which the Catalan numbers can be computed to any desired order. (Segner’s work was to be reviewed in [197], anonymously, but most probably, by Euler.) 3. During the 1830s, Liouville circulated the problem and wrote to Lam´e, who answered the next day(!) with a proof [399] based on recurrences similar to (4) of the explicit expression: 2n 1 . (5) Tn = n+1 n Interestingly enough, Lam´e’s three-page note [399] appeared in the 1838 issue of the Journal de math´ematiques pures et appliqu´ees (“Journal de Liouville”), immediately followed by a longer study by Catalan [106], who also observed that the Tn intervene in the number of ways of multiplying n numbers (this book, §I. 5.3, p. 73). Catalan would then return to these problems [107, 108], and the numbers 1, 1, 2, 5, 14, 42, . . . eventually became known as the Catalan numbers. In [107], Catalan finally proves the validity of Euler’s generating function: √ X 1 − 1 − 4z Tn z n = (6) T (z) := . 2z n 4. Nowadays, symbolic methods directly yield the generating function (6), from which both the recurrence (4) and the explicit form (5) follow easily; see pp. 6 and 35. Figure I.2. The prehistory of Catalan numbers.

I. 1. SYMBOLIC ENUMERATION METHODS

21

N HC

CH 3

CH C

HC

N CH CH 2

HC

H⇒

C10 H14 N2

;

z 26

CH 2

H 2C

Figure I.3. A molecule, methylpyrrolidinyl-pyridine (nicotine), is a complex assembly whose description can be reduced to a single formula corresponding here to a total of 26 atoms.

The OGFs corresponding to our three examples W, P, T are then ∞ X 1 W (z) = 2n z n = 1 − 2z n=0 ∞ X n! z n P(z) = (10) n=0 √ ∞ X 1 1 − 1 − 4z 2n n T (z) = z = . n+1 n 2z n=0

The first expression relative to W (z) is immediate as it is the sum of a geometric progression. The second generating function P(z) is not clearly related to simple functions of analysis. (Note that the expression still makes sense within the strict framework of formal power series.) The third expression relative to T (z) is equivalent to the explicit form of Tn via Newton’s expansion of (1 + x)1/2 (pp. 7 and 35 as well as Figure I.2). The OGFs W (z) and T (z) can then be interpreted as standard analytic objects, upon assigning values in the complex domain C to the formal variable z. In effect, the series W (z) and T (z) converge in a neighbourhood of 0 and represent complex functions that are well defined near the origin, namely when |z| < 12 for W (z) and |z| < 14 for T (z). The OGF P(z) is a purely formal power series (its radius of convergence is 0) that can nonetheless be subjected to the usual algebraic operations of power series. (Permutation enumeration is most conveniently approached by the exponential generating functions developed in Chapter II.) Combinatorial form of generating functions (GFs). The combinatorial form (8) shows that generating functions are nothing but a reduced representation of the combinatorial class, where internal structures are destroyed and elements contributing to size (atoms) are replaced by the variable z. In a sense, this is analogous to what chemists do by writing linear reduced (“molecular”) formulae for complex molecules (Figure I.3). Great use of this observation was made by Sch¨utzenberger as early as the 1950s and 1960s. It explains the many formal similarities that are observed between combinatorial structures and generating functions.

22

I. COMBINATORIAL STRUCTURES AND ORDINARY GENERATING FUNCTIONS

H=

H (z) =

zzzz

zz

zzz

zzzz

z

zzzz

zzz

+ z4

+ z2

+ z3

+ z4

+z

+ z4

+ z3

z + z 2 + 2z 3 + 3z 4

Figure I.4. A finite family of graphs and its eventual reduction to a generating function.

Figure I.4 provides a combinatorial illustration: start with a (finite) family of graphs H, with size taken as the number of vertices. Each vertex in each graph is replaced by the variable z and the graph structure is “forgotten”; then the monomials corresponding to each graph are formed and the generating function is finally obtained by gathering all the monomials. For instance, there are 3 graphs of size 4 in H, in agreement with the fact that [z 4 ]H (z) = 3. If size had been instead defined by number of edges, another generating function would have resulted, namely, with y marking the new size: 1+ y + y 2 +2y 3 + y 4 + y 6 . If both number of vertices and number of edges are of interest, then a bivariate generating function is obtained: H (z, y) = z+z 2 y+z 3 y 2 +z 3 y 3 +z 4 y 3 +z 4 y 4 +z 4 y 6 ; such multivariate generating functions are developed systematically in Chapter III. A path often taken in the literature is to decompose the structures to be enumerated into smaller structures either of the same type or of simpler types, and then extract from such a decomposition recurrence relations that are satisfied by the {An }. In this context, the recurrence relations are either solved directly—whenever they are simple enough—or by means of ad hoc generating functions, introduced as mere technical artifices. By contrast, in the framework of this book, classes of combinatorial structures are built directly in terms of simpler classes by means of a collection of elementary combinatorial constructions. This closely resembles the description of formal languages by means of grammars, as well as the construction of structured data types in programming languages. The approach developed here has been termed symbolic, as it relies on a formal specification language for combinatorial structures. Specifically, it is based on so–called admissible constructions that permit direct translations into generating functions. Definition I.5. Let 8 be an m–ary construction that associates to any collection of classes B (1) , . . . B (m) a new class A = 8[B (1) , . . . , B (m) ]. The construction 8 is admissible iff the counting sequence (An ) of A only depends on (1) (m) the counting sequences (Bn ), . . . , (Bn ) of B (1) , . . . , B (m) .

I. 1. SYMBOLIC ENUMERATION METHODS

23

For such an admissible construction, there then exists a well-defined operator 9 acting on the corresponding ordinary generating functions: A(z) = 9[B (1) (z), . . . , B (m) ], and it is this basic fact about admissibility that will be used throughout the book. As an introductory example, take the construction of cartesian product, which is the usual one enriched with a natural notion of size. Definition I.6. The cartesian product construction applied to two classes B and C forms ordered pairs, (11)

A=B×C

iff A = {α = (β, γ ) | β ∈ B, γ ∈ C },

with the size of a pair α = (β, γ ) being defined by (12)

|α|A = |β|B + |γ |C .

By considering all possibilities, it is immediately seen that the counting sequences corresponding to A, B, C are related by the convolution relation (13)

An =

n X

Bk Cn−k ,

k=0

which means admissibility. Furthermore, we recognize here the formula for a product of two power series: (14)

A(z) = B(z) · C(z).

In summary: the cartesian product is admissible and it translates as a product of OGFs. Similarly, let A, B, C be combinatorial classes satisfying (15)

A = B ∪ C,

with B ∩ C = ∅,

with size defined in a consistent manner: for ω ∈ A, |ω| B if ω ∈ B (16) |ω|A = |ω| if ω ∈ C. C

One has (17)

An = Bn + Cn ,

which, at generating function level, means (18)

A(z) = B(z) + C(z).

Thus, the union of disjoint sets is admissible and it translates as a sum of generating functions. (A more formal version of this statement is given in the next section.) The correspondences provided by (11)–(14) and (15)–(18) are summarized by the strikingly simple dictionary A = B ∪ C H⇒ A(z) = B(z) + C(z) (provided B ∩ C = ∅) (19) A = B × C H⇒ A(z) = B(z) · C(z),

24

I. COMBINATORIAL STRUCTURES AND ORDINARY GENERATING FUNCTIONS

to be compared with the plain arithmetic case of (3), p. 18. The merit of such relations is that they can be stated as general purpose translation rules that only need to be established once and for all. As soon as the problem of counting elements of a union of disjoint sets or a cartesian product is recognized, it becomes possible to dispense altogether with the intermediate stages of writing explicitly coefficient relations or recurrences as in (13) or (17). This is the spirit of the symbolic method for combinatorial enumerations. Its interest lies in the fact that several powerful set-theoretic constructions are amenable to such a treatment, as we see in the next section.

I.3. Continuity, Lipschitz and H¨older conditions. An admissible construction is said to be continuous if it is a continuous function on the space of formal power series equipped with its standard ultrametric distance (Appendix A.5: Formal power series, p. 730). Continuity captures the desirable property that constructions depend on their arguments in a finitary way. For all the constructions of this book, there furthermore exists a function ϑ(n), such that (An ) only (1) (m) depends on the first ϑ(n) elements of the (Bk ), . . . , (Bk ), with ϑ(n) ≤ K n + L (H¨older condition) or ϑ(n) ≤ n + L (Lipschitz condition). For instance, the functional f (z) 7→ f (z 2 ) is H¨older; the functional f (z) 7→ ∂z f (z) is Lipschitz. I. 2. Admissible constructions and specifications The main goal of this section is to introduce formally the basic constructions that constitute the core of a specification language for combinatorial structures. This core is based on disjoint unions, also known as combinatorial sums, and on cartesian products that we have just discussed. We shall augment it by the constructions of sequence, cycle, multiset, and powerset. A class is constructible or specifiable if it can be defined from primal elements by means of these constructions. The generating function of any such class satisfies functional equations that can be transcribed systematically from a specification; see Theorems I.1 (p. 27) and I.2 (p. 33), as well as Figure I.18 (p. 93) at the end of this chapter for a summary. I. 2.1. Basic constructions. First, we assume we are given a class E called the neutral class that consists of a single object of size 0; any such object of size 0 is called a neutral object and is usually denoted by symbols such as ǫ or 1. The reason for this terminology becomes clear if one considers the combinatorial isomorphism A∼ = A × E. =E ×A∼ We also assume as given an atomic class Z comprising a single element of size 1; any such element is called an atom; an atom may be used to describe a generic node in a tree or graph, in which case it may be represented by a circle (• or ◦), but also a generic letter in a word, in which case it may be instantiated as a, b, c, . . . . Distinct copies of the neutral or atomic class may also be subscripted by indices in various ways. Thus, for instance, we may use the classes Za = {a}, Zb = {b} (with a, b of size 1) to build up binary words over the alphabet {a, b}, or Z• = {•}, Z◦ = {◦} (with •, ◦ taken to be of size 1) to build trees with nodes of two colours. Similarly, we may introduce E2 , E1 , E2 to denote a class comprising the neutral objects 2, ǫ1 , ǫ2 respectively. Clearly, the generating functions of a neutral class E and an atomic class Z are E(z) = 1,

Z (z) = z,

I. 2. ADMISSIBLE CONSTRUCTIONS AND SPECIFICATIONS

25

corresponding to the unit 1, and the variable z, of generating functions. Combinatorial sum (disjoint union). The intent of combinatorial sum also known as disjoint union is to capture the idea of a union of disjoint sets, but without any extraneous condition (disjointness) being imposed on the arguments of the construction. To do so, we formalize the (combinatorial) sum of two classes B and C as the union (in the standard set-theoretic sense) of two disjoint copies, say B 2 and C 3 , of B and C. A picturesque way to view the construction is as follows: first choose two distinct colours and repaint the elements of B with the first colour and the elements of C with the second colour. This is made precise by introducing two distinct “markers”, say 2 and 3, each a neutral object (i.e., of size zero); the disjoint union B + C of B, C is then defined as a standard set-theoretic union: B + C := ({2} × B) ∪ ({3} × C) .

The size of an object in a disjoint union A = B + C is by definition inherited from its size in its class of origin, as in Equation (16). One good reason behind the definition adopted here is that the combinatorial sum of two classes is always well defined, no matter whether or not the classes intersect. Furthermore, disjoint union is equivalent to a standard union whenever it is applied to disjoint sets. Because of disjointness of the copies, one has the implication A=B+C

H⇒

An = Bn + Cn

and

A(z) = B(z) + C(z),

so that disjoint union is admissible. Note that, in contrast, standard set-theoretic union is not an admissible construction since card(Bn ∪ Cn ) = card(Bn ) + card(Cn ) − card(Bn ∩ Cn ),

and information on the internal structure of B and C (i.e., the nature of their intersection) is needed in order to be able to enumerate the elements of their union. Cartesian product. This construction A = B ×C forms all possible ordered pairs in accordance with Definition I.6. The size of a pair is obtained additively from the size of components in accordance with (12). Next, we introduce a few fundamental constructions that build upon set-theoretic union and product, and form sequences, sets, and cycles. These powerful constructions suffice to define a broad variety of combinatorial structures. Sequence construction. If B is a class then the sequence class S EQ(B) is defined as the infinite sum S EQ(B) = {ǫ} + B + (B × B) + (B × B × B) + · · · with ǫ being a neutral structure (of size 0). In other words, we have A = (β1 , . . . , βℓ ) ℓ ≥ 0, β j ∈ B ,

which matches our intuition as to what sequences should be. (The neutral structure in this context corresponds to ℓ = 0; it plays a rˆole similar to that of the “empty” word in formal language theory.) It is then readily checked that the construction A = S EQ(B) defines a proper class satisfying the finiteness condition for sizes if and only if B contains no object of size 0. From the definition of size for sums and products, it

26

I. COMBINATORIAL STRUCTURES AND ORDINARY GENERATING FUNCTIONS

follows that the size of an object α ∈ A is to be taken as the sum of the sizes of its components: α = (β1 , . . . , βℓ )

H⇒

|α| = |β1 | + · · · + |βℓ |.

Cycle construction. Sequences taken up to a circular shift of their components define cycles, the notation being C YC(B). In precise terms, one has3 C YC(B) := (S EQ(B) \ {ǫ}) /S, where S is the equivalence relation between sequences defined by (β1 , . . . , βr ) S (β1′ , . . . , βr′ ) iff there exists some circular shift τ of [1 . . r ] such that for all j, β ′j = βτ ( j) ; in other words, for some d, one has β ′j = β1+( j−1+d) mod r . Here is, for instance, a depiction of the cycles formed from the 8 and 16 sequences of lengths 3 and 4 over two types of objects (a, b): the number of cycles is 4 (for n = 3) and 6 (for n = 4). Sequences are grouped into equivalence classes according to the relation S: (20)

3–cycles :

(

aaa aab aba baa abb bba bab , bbb

aaaa aaab aaba abaa baaa aabb abba bbaa baab 4–cycles : . abab baba abbb bbba bbab babb bbbb

According to the definition, this construction corresponds to the formation of directed cycles (see also the necklaces of Note I.1, p. 18). We make only a limited use of it for unlabelled objects; however, its counterpart plays a rather important rˆole in the context of labelled structures and exponential generating functions of Chapter II. Multiset construction. Following common mathematical terminology, multisets are like finite sets (that is the order between elements does not count), but arbitrary repetitions of elements are allowed. The notation is A = MS ET(B) when A is obtained by forming all finite multisets of elements from B. The precise way of defining MS ET(B) is as a quotient: MS ET(B) := S EQ(B)/R with R, the equivalence relation of sequences being defined by (α1 , . . . , αr ) R (β1 , . . . , βr ) iff there exists some arbitrary permutation σ of [1 . . r ] such that for all j, β j = ασ ( j) . Powerset construction. The powerset class (or set class) A = PS ET(B) is defined as the class consisting of all finite subsets of class B, or equivalently, as the class PS ET(B) ⊂ MS ET(B) formed of multisets that involve no repetitions. We again need to make explicit the way the size function is defined when such constructions are performed: as for products and sequences, the size of a composite object—set, multiset, or cycle—is defined to be the sum of the sizes of its components.

I.4. The semi-ring of combinatorial classes. Under the convention of identifying isomorphic classes, sum and product acquire pleasant algebraic properties: combinatorial sums and cartesian products become commutative and associative operations, e.g., (A + B) + C = A + (B + C), A × (B × C) = (A × B) × C, while distributivity holds, (A + B) × C = (A × C) + (B × C). 3By convention, there are no “empty” cycles.

I. 2. ADMISSIBLE CONSTRUCTIONS AND SPECIFICATIONS

27

I.5. Natural numbers. Let Z := {•} with • an atom (of size 1). Then I = S EQ(Z) \

{ǫ} is a way of describing positive integers in unary notation: I = {•, • •, •••, . . .}. The corresponding OGF is I (z) = z/(1 − z) = z + z 2 + z 3 + · · · .

I.6. Interval coverings. Let Z := {•} be as before. Then A = Z + (Z × Z) is a set of two elements, • and (•, •), which we choose to draw as {•, •–•}. Then C = S EQ(A) contains •, • •, •–•, • •–•, •–• •, •–• •–•, • • • •, . . .

With the notion of size adopted, the objects of size n in C = S EQ(Z +(Z ×Z)) are (isomorphic to) the coverings of [0, n] by intervals (matches) of length either 1 or 2. The OGF C(z) = 1 + z + 2 z 2 + 3 z 3 + 5 z 4 + 8 z 5 + 13 z 6 + 21 z 7 + 34 z 8 + 55 z 9 + · · · ,

is, as we shall see shortly (p. 42), the OGF of Fibonacci numbers.

I. 2.2. The admissibility theorem for ordinary generating functions. This section is a formal treatment of admissibility proofs for the constructions that we have introduced. The final implication is that any specification of a constructible class translates directly into generating function equations. The translation of the cycle construction involves the Euler totient function ϕ(k) defined as the number of integers in [1, k] that are relatively prime to k (Appendix A.1: Arithmetical functions, p. 721). Theorem I.1 (Basic admissibility, unlabelled universe). The constructions of union, cartesian product, sequence, powerset, multiset, and cycle are all admissible. The associated operators are as follows. Sum:

A=B+C

Cartesian product: A = B × C

H⇒ A(z) = B(z) + C(z) H⇒ A(z) = B(z) · C(z)

Sequence:

A = S EQ(B)

H⇒ A(z) =

Powerset:

A = PS ET(B)

H⇒ A(z) =

Multiset:

A = MS ET(B) H⇒ A(z) =

1 1 − B(z) Y (1 + z n ) Bn n≥1

X ∞ (−1)k−1 k exp B(z ) k k=1 Y (1 − z n )−Bn n≥1

X ∞ 1 k B(z ) exp k k=1

Cycle:

A = C YC(B)

H⇒ A(z) =

∞ X ϕ(k) k=1

k

log

1 . 1 − B(z k )

For the sequence, powerset, multiset, and cycle translations, it is assumed that B0 = ∅.

28

I. COMBINATORIAL STRUCTURES AND ORDINARY GENERATING FUNCTIONS

The class E = {ǫ} consisting of the neutral object only, and the class Z consisting of a single “atomic” object (node, letter) of size 1 have OGFs E(z) = 1

Z (z) = z.

and

Proof. The proof proceeds case by case, building upon what we have just seen regarding unions and products. Combinatorial sum (disjoint union). Let A = B + C. Since the union is disjoint, and the size of an A–element coincides with its size in B or C, one has An = Bn + Cn and A(z) = B(z) + C(z), as discussed earlier. The rule also follows directly from the combinatorial form of generating functions as expressed by (8), p. 19: X X X A(z) = z |α| = z |α| + z |α| = B(z) + C(z). α∈A

α∈B

α∈C

Cartesian product. The admissibility result for A = B × C was considered as an example for Definition I.6, the convolution equation (13) leading to the relation A(z) = B(z) · C(z). We can also offer a direct derivation based on the combinatorial form of generating functions (8), p. 19, X X X X A(z) = z |α| = z |β|+|γ | = z |β| × z |γ | = B(z) · C(z), α∈A

(β,γ )∈(B×C )

γ ∈C

β∈B

as follows from distributing products over sums. This derivation readily extends to an arbitrary number of factors. Sequence construction. Admissibility for A = S EQ(B) (with B0 = ∅) follows from the union and product relations. One has so that

A = {ǫ} + B + (B × B) + (B × B × B) + · · · ,

1 , 1 − B(z) where the geometric sum converges in the sense of formal power series since [z 0 ]B(z) = 0, by assumption. Powerset construction. Let A = PS ET(B) and first take B to be finite. Then, the class A of all the finite subsets of B is isomorphic to a product, Y (21) PS ET(B) ∼ ({ǫ} + {β}), = A(z) = 1 + B(z) + B(z)2 + B(z)3 + · · · =

β∈B

with ǫ a neutral structure of size 0. Indeed, distributing the products in all possible ways forms all the possible combinations (sets with no repetition allowed) of elements of B; the reasoning is the same as what leads to an identity such as (1 + a)(1 + b)(1 + c) = 1 + [a + b + c] + [ab + bc + ac] + abc,

where all combinations of variables appear in monomials. Then, directly from the combinatorial form of generating functions and the sum and product rules, we find Y Y (22) A(z) = (1 + z |β| ) = (1 + z n ) Bn . β∈B

n

I. 2. ADMISSIBLE CONSTRUCTIONS AND SPECIFICATIONS

29

The exp–log transformation A(z) = exp(log A(z)) then yields X ∞ A(z) = exp Bn log(1 + z n ) (23)

= =

n=1 X ∞

exp

∞ nk X k−1 z (−1) Bn · k

k=1 n=1 B(z) B(z 2 ) B(z 3 ) exp − + − ··· , 1 2 3

where the second line results from expanding the logarithm, u2 u3 u − + − ··· , 1 2 3 and the third line results from exchanging the order of summations. The proof finally extends to the case of B being infinite by noting that each An depends only on those B j for which j ≤ n, to which the relations given above for the P (≤m) = PS ET(B (≤m) ). Then, finite case apply. Precisely, let B (≤m) = m k=1 B j and A m+1 with O(z ) denoting any series that has no term of degree ≤ m, one has log(1 + u) =

A(z) = A(≤m) (z) + O(z m+1 )

and

B(z) = B (≤m) (z) + O(z m+1 ).

On the other hand, A(≤m) (z) and B (≤m) (z) are connected by the fundamental exponential relation (23) , since B (≤m) is finite. Letting m tend to infinity, there follows in the limit B(z) B(z 2 ) B(z 3 ) A(z) = exp − + − ··· . 1 2 3 (See Appendix A.5: Formal power series, p. 730 for the notion of formal convergence.) Multiset construction. First for finite B (with B0 = ∅), the multiset class A = MS ET(B) is definable by Y (24) MS ET(B) ∼ S EQ({β}). = β∈B

In words, any multiset can be sorted, in which case it can be viewed as formed of a sequence of repeated elements β1 , followed by a sequence of repeated elements β2 , where β1 , β2 , . . . is a canonical listing of the elements of B. The relation translates into generating functions by the product and sequence rules, A(z) = (25)

Y

β∈B

=

exp

=

exp

(1 − z |β| )−1 =

X ∞ n=1

∞ Y

n=1

(1 − z n )−Bn

n −1

Bn log(1 − z )

B(z 2 ) B(z 3 ) B(z) + + + ··· , 1 2 3

30

I. COMBINATORIAL STRUCTURES AND ORDINARY GENERATING FUNCTIONS

where the exponential form results from the exp–log transformation. The case of an infinite class B follows by a limit argument analogous the one used for powersets. Cycle construction. The translation of the cycle relation A = C YC(B) turns out to be ∞ X 1 ϕ(k) log , A(z) = k 1 − B(z k ) k=1

where ϕ(k) is the Euler totient function. The first terms, with L k (z) := log(1 − B(z k ))−1 are 1 1 2 2 4 2 A(z) = L 1 (z) + L 2 (z) + L 3 (z) + L 4 (z) + L 5 (z) + L 6 (z) + · · · . 1 2 3 4 5 6 We reserve the proof to Appendix A.4: Cycle construction, p. 729, since it relies in part on multivariate generating functions to be officially introduced in Chapter III. The results for sets, multisets, and cycles are particular cases of the well-known P´olya theory that deals more generally with the enumeration of objects under group symmetry actions; for P´olya’s original and its edited version, see [488, 491]. This theory is described in many textbooks, for instance, those of Comtet [129] and Harary and Palmer [129, 319]; Notes I.58–I.60, pp. 85–86, distil its most basic aspects. The approach adopted here amounts to considering simultaneously all possible values of the number of components by means of bivariate generating functions. Powerful generalizations within Joyal’s elegant theory of species [359] are presented in the book by Bergeron, Labelle, and Leroux [50].

I.7. Vall´ee’s identity. Let M = MS ET(C), P = PS ET(C). One has combinatorially:

M(z) = P(z)M(z 2 ). (Hint: a multiset contains elements of either odd or even multiplicity.) Accordingly, one can deduce the translation of powersets from the formula for multisets. Iterating the relation above yields M(z) = P(z)P(z 2 )P(z 4 )P(z 8 ) · · · : this is closely related to the binary representation of numbers and to Euler’s identity (p. 49). It is used for instance in Note I.66 p. 91.

Restricted constructions. In order to increase the descriptive power of the framework of constructions, we ought to be able to allow restrictions on the number of components in sequences, sets, multisets, and cycles. Let K be a metasymbol representing any of S EQ, C YC, MS ET, PS ET and let be a predicate over the integers; then K (A) will represent the class of objects constructed by K, with a number of components constrained to satisfy . For instance, the notation (26)

S EQ=k (or simply S EQk ), S EQ>k , S EQ1 . . k

refers to sequences whose number of components are exactly k, larger than k, or in the interval 1 . . k respectively. In particular, k times

}| { z S EQk (B) := B × · · · × B ≡ B k ,

MS ETk (B) := S EQk (B)/R.

S EQ≥k (B) =

X j≥k

Bj ∼ = B k × S EQ(B),

Similarly, S EQodd , S EQeven will denote sequences with an odd or even number of components, and so on.

I. 2. ADMISSIBLE CONSTRUCTIONS AND SPECIFICATIONS

31

Translations for such restricted constructions are available, as shown generally in Subsection I. 6.1, p. 83. Suffice it to note for the moment that the construction A = S EQk (B) is really an abbreviation for a k-fold product, hence it admits the translation into OGFs (27)

A = S EQk (B)

H⇒

A(z) = B(z)k .

I. 2.3. Constructibility and combinatorial specifications. By composing basic constructions, we can build compact descriptions (specifications) of a broad variety of combinatorial classes. Since we restrict attention to admissible constructions, we can immediately derive OGFs for these classes. Put differently, the task of enumerating a combinatorial class is reduced to programming a specification for it in the language of admissible constructions. In this subsection, we first discuss the expressive power of the language of constructions, then summarize the symbolic method (for unlabelled classes and OGFs) by Theorem I.2. First, in the framework just introduced, the class of all binary words is described by W = S EQ(A), where A = {a, b} ∼ = Z + Z, the ground alphabet, comprises two elements (letters) of size 1. The size of a binary word then coincides with its length (the number of letters it contains). In other terms, we start from basic atomic elements and build up words by forming freely all the objects determined by the sequence construction. Such a combinatorial description of a class that only involves a composition of basic constructions applied to initial classes E, Z is said to be an iterative (or non-recursive) specification. Other examples already encountered include binary necklaces (Note I.1, p. 18) and the positive integers (Note I.5, p. 27) respectively defined by N = C YC(Z + Z)

and

I = S EQ≥1 (Z).

From this, one can construct ever more complicated objects. For instance, P = MS ET(I) ≡ MS ET(S EQ≥1 (Z))

means the class of multisets of positive integers, which is isomorphic to the class of integer partitions (see Section I. 3 below for a detailed discussion). As such examples demonstrate, a specification that is iterative can be represented as a single term built on E, Z and the constructions +, ×, S EQ, C YC, MS ET, PS ET. An iterative specification can be equivalently listed by naming some of the subterms (for instance, partitions in terms of natural integers I, themselves defined as sequences of atoms Z). Semantics of recursion. We next turn our attention to recursive specifications, starting with trees (cf also Appendix A.9: Tree concepts, p. 737, for basic definitions). In graph theory, a tree is classically defined as an undirected graph that is connected and acyclic. Additionally, a tree is rooted if a particular vertex is specified (this vertex is then kown as the root). Computer scientists commonly make use of trees called plane4 that are rooted but also embedded in the plane, so that the ordering of subtrees 4 The alternative terminology “planar tree” is also often used, but it is frowned upon by some as incorrect (all trees are planar graphs). We have thus opted for the expression “plane tree”, which parallels the phrase “plane curve”.

32

I. COMBINATORIAL STRUCTURES AND ORDINARY GENERATING FUNCTIONS

attached to any node matters. Here, we will give the name of general plane trees to such rooted plane trees and call G their class, where size is the number of vertices; see, e.g., reference [538]. (The term “general” refers to the fact that all nodes degrees are allowed.) For instance, a general tree of size 16, drawn with the root on top, is:

τ=

.

As a consequence of the definition, if one interchanges, say, the second and third root subtrees, then a different tree results—the original tree and its variant are not equivalent under a smooth deformation of the plane. (General trees are thus comparable to graphical renderings of genealogies where children are ordered by age.). Although we have introduced plane trees as two-dimensional diagrams, it is obvious that any tree also admits a linear representation: a tree τ with root ζ and root subtrees τ1 , . . . , τr (in that order) can be seen as the object ζ τ1 , . . . , τr , where the box encloses similar representations of subtrees. Typographically, a box · may be reduced to a matching pair of parentheses, “(·)”, and one gets in this way a linear description that illustrates the correspondence between trees viewed as plane diagrams and functional terms of mathematical logic and computer science. Trees are best described recursively. A plane tree is a root to which is attached a (possibly empty) sequence of trees. In other words, the class G of general trees is definable by the recursive equation (28)

G = Z × S EQ(G),

where Z comprises a single atom written “•” that represents a generic node. Although such recursive definitions are familiar to computer scientists, the specification (28) may look dangerously circular to some. One way of making good sense of it is via an adaptation of the numerical technique of iteration. Start with G [0] = ∅, the empty set, and define successively the classes G [ j+1] = Z × S EQ(G [ j] ). For instance, G [1] = Z × S EQ(∅) = {(•, ǫ)} ∼ = {•} describes the tree of size 1, and G [2] = • , • • , • • • , • • • • , . . . G [3] = •, • • , • •• , • • • • , ... , • • • , • • •• , • •• • , • • •• •• ,... .

First, each G [ j] is well defined since it corresponds to a purely iterative specification. Next, we have the inclusion G [ j] ⊂ G [ j+1] (a simple interpretation of G [ j] is the class of all trees of height < j). We canS therefore regard the complete class G as defined by the limit of the G [ j] ; that is, G := j G [ j] .

I.8. Lim-sup of classes. Let {A[ j] } be any increasing sequence of combinatorial classes, in S the sense that A[ j] ⊂ A[ j+1] , and the notions of size are compatible. If A[∞] =

[ j] is a jA

I. 2. ADMISSIBLE CONSTRUCTIONS AND SPECIFICATIONS

33

combinatorial class (there are finitely many elements of size n, for each n), then the corresponding OGFs satisfy A[∞] (z) = lim j→∞ A[ j] (z) in the formal topology (Appendix A.5: Formal power series, p. 730).

Definition I.7. A specification for an collection of r equations, (1) A(2) = A = (29) (r ) · · · A =

r –tuple AE = (A(1) , . . . , A(r ) ) of classes is a 81 (A(1) , . . . , A(r ) ) 82 (A(1) , . . . , A(r ) ) 8r (A(1) , . . . , A(r ) )

where each 8i denotes a term built from the A using the constructions of disjoint union, cartesian product, sequence, powerset, multiset, and cycle, as well as the initial classes E (neutral) and Z (atomic). We also say that the system is a specification of A(1) . A specification for a combinatorial class is thus a sort of formal grammar defining that class. Formally, the system (29) is an iterative or non-recursive specification if it is strictly upper-triangular, that is, A(r ) is defined solely in terms of initial classes Z, E; the definition of A(r −1) only involves A(r ) , and so on; in that case, by back substitutions, it is apparent that for an iterative specification, A(1) can be equivalently described by a single term involving only the initial classes and the basic constructors. Otherwise, the system is said to be recursive. In the latter case, the semantics of recursion is identical to the one introduced in the case oftrees: start with the “empty” vector of classes, AE[0] := (∅, . . . , ∅), E AE[ j] , and finally take the limit. iterate AE[ j+1] = 8 There is an alternative and convenient way to visualize these notions. Given a specification of the form (29), we can associate its dependency (di)graph Ŵ to it as follows. The set of vertices of Ŵ is the set of indices {1, . . . , r }; for each equation A(i) = 4i (A(1) , . . . , A(r ) ) and for each j such that A( j) appears explicitly on the right-hand side of the equation, place a directed edge (i → j) in Ŵ. It is then easily recognized that a class is iterative if the dependency graph of its specification is acyclic; it is recursive is the dependency graph has a directed cycle. (This notion will serve to define irreducible linear systems, p. 341, and irreducible polynomial systems, p. 482, which enjoy strong asymptotic properties.) Definition I.8. A class of combinatorial structures is said to be constructible or specifiable iff it admits a (possibly recursive) specification in terms of sum, product, sequence, set, multiset, and cycle constructions. At this stage, we have therefore available a specification language for combinatorial structures which is some fragment of set theory with recursion added. Each constructible class has by virtue of Theorem I.1 an ordinary generating function for which functional equations can be produced systematically. (In fact, it is even possible to use computer algebra systems in order to compute it automatically! See the article by Flajolet, Salvy, and Zimmermann [255] for the description of such a system.) Theorem I.2 (Symbolic method, unlabelled universe). The generating function of a constructible class is a component of a system of functional equations whose terms

34

I. COMBINATORIAL STRUCTURES AND ORDINARY GENERATING FUNCTIONS

are built from 1, z, + , × , Q , Exp , Exp , Log, where Q[ f ]

Exp[ f ]

1 , 1− f ! ∞ X f (z k ) = exp , k

=

Log[ f ] Exp[ f ]

k=1

∞ X ϕ(k)

1 , k 1 − f (z k ) k=1 ! ∞ k X k−1 f (z ) . = exp (−1) k

=

log

k=1

P´olya operators. The operator Q translating sequences (S EQ) is classically known as the quasi-inverse. The operator Exp (multisets, MS ET) is called the P´olya exponential5 and Exp (powersets, PS ET) is the modified P´olya exponential. The operator Log is the P´olya logarithm. They are named after P´olya who first developed the general enumerative theory of objects under permutation groups (pp. 85–86). The statement of Theorem I.2 signifies that iterative classes have explicit generating functions involving compositions of the basic operators only, while recursive structures have OGFs that are accessible indirectly via systems of functional equations. As we shall see at various places in this chapter, the following classes are constructible: binary words, binary trees, general trees, integer partitions, integer compositions, non-plane trees, polynomials over finite fields, necklaces, and wheels. We conclude this section with a few simple illustrations of the symbolic method expressed by Theorem I.2. Binary words. The OGF of binary words, as seen already, can be obtained directly from the iterative specification, W = S EQ(Z + Z)

H⇒

W (z) =

1 , 1 − 2z

whence the expected result, Wn = 2n . (Note: in our framework, if a, b are letters, then Z + Z ∼ = {a, b}.) General trees. The recursive specification of general trees leads to an implicit definition of their OGF, z G = Z × S EQ(G) H⇒ G(z) = . 1 − G(z) From this point on, basic algebra6 does the rest. First the original equation is equivalent (in the ring of formal power series) to G − G 2 − z = 0. Next, the quadratic equation 5It is a notable fact that, although the P´olya operators look algebraically “difficult” to compute with, their treatment by complex asymptotic methods, as regards coefficient asymptotics, is comparatively “easy”. We shall see many examples in Chapters IV–VII (e.g., pp. 252, 475). 6Methodological note: for simplicity, our computation is developed using the usual language of mathematics. However, analysis is not needed in this derivation, and operations such as solving quadratic equations and expanding fractional powers can all be cast within the purely algebraic framework of formal power series (p. 730).

I. 2. ADMISSIBLE CONSTRUCTIONS AND SPECIFICATIONS

35

is solvable by radicals, and one finds √ G(z) = 12 1 − 1 − 4z

z + z 2 + 2 z 3 + 5 z 4 + 14 z 5 + 42 z 6 + 132 z 7 + 429 z 8 + · · · X 1 2n − 2 zn . = n n−1

=

n≥1

(The conjugate root is to be discarded since it involves a term z −1 as well as negative coefficients.) The expansion then results from Newton’s binomial expansion, α α(α − 1) 2 (1 + x)α = 1 + x + x + ··· , 1 2! applied with α = 21 and x = −4z. The numbers √ 1 (2n)! 2n 1 − 1 − 4z (30) Cn = = with OGF C(z) = n+1 n (n + 1)! n! 2z are known as the Catalan numbers (EIS A000108) in the honour of Eug`ene Catalan, the mathematician who first studied their properties in geat depth (pp. 6 and 20). In summary, general trees are enumerated by Catalan numbers: 1 2n − 2 . G n = Cn−1 ≡ n n−1 For this reason the term Catalan tree is often employed as synonymous to “general (rooted unlabelled plane) tree”. Triangulations. Fix n + 2 points arranged in anticlockwise order on a circle and conventionally numbered from 0 to n + 1 (for instance the (n + 2)th roots of unity). A triangulation is defined as a (maximal) decomposition of the convex (n + 2)-gon defined by the points into n triangles (Figure I.1, p. 17). Triangulations are taken here as abstract topological configurations defined up to continuous deformations of the plane. The size of the triangulation is the number of triangles; that is, n. Given a triangulation, we define its “root” as a triangle chosen in some conventional and unambiguous manner (e.g., at the start, the triangle that contains the two smallest labels). Then, a triangulation decomposes into its root triangle and two subtriangulations (that may well be “empty”) appearing on the left and right sides of the root triangle; the decomposition is illustrated by the following diagram:

=

+

The class T of all triangulations can be specified recursively as T

=

{ǫ}

+

(T × ∇ × T ) ,

36

I. COMBINATORIAL STRUCTURES AND ORDINARY GENERATING FUNCTIONS

provided that we agree to consider a 2-gon (a segment) as giving rise to an “empty” triangulation of size 0. (The subtriangulations are topologically and combinatorially equivalent to standard ones, with vertices regularly spaced on a circle.) Consequently, the OGF T (z) satisfies the equation √ 1 (31) T (z) = 1 + zT (z)2 , so that T (z) = 1 − 1 − 4z . 2z As a result of (30) and (31), triangulations are enumerated by Catalan numbers: 2n 1 . Tn = Cn ≡ n+1 n This particular result goes back to Euler and Segner, a century before Catalan; see Figure I.1 on p. 17 for first values and p. 73 below for related bijections.

I.9. A bijection. Since both general trees and triangulations are enumerated by Catalan numbers, there must exist a size-preserving bijection between the two classes. Find one such bijection. [Hint: the construction of triangulations is evocative of binary trees, while binary trees are themselves in bijective correspondence with general trees (p. 73).]

I.10. A variant specification of triangulations. Consider the class U of “non-empty” triangulations of the n-gon, that is, we exclude the 2-gon and the corresponding “empty” triangulation of size 0. Then U = T \ {ǫ} admits the specification U = ∇ + (∇ × U) + (U × ∇) + (U × ∇ × U)

which also leads to the Catalan numbers via U = z(1 + U )2 , so that U (z) = (1 − 2z − √ 1 − 4z)/(2z) ≡ T (z) − 1.

I. 2.4. Exploiting generating functions and counting sequences. In this book we are going to see altogether more than a hundred applications of the symbolic method. Before engaging in technical developments, it is worth inserting a few comments on the way generating functions and counting sequences can be put to good use in order to solve combinatorial problems. Explicit enumeration formulae. In a number of situations, generating functions are explicit and can be expanded in such a way that explicit formulae result for their coefficients. A prime example is the counting of general trees and of triangulations above, where the quadratic equation satisfied by an OGF is amenable to an explicit solution—the resulting OGF could then be expanded by means of Newton’s binomial theorem. Similarly, we derive later in this chapter an explicit form for the number of integer compositions by means of the symbolic method (the answer turns out to be simply 2n−1 ) and obtain in this way, through OGFs, many related enumeration results. In this book, we assume as known the elementary techniques from basic calculus by which the Taylor expansion of an explicitly given function can be obtained. (Elementary references on such aspects are Wilf’s Generatingfunctionology [608], Graham, Knuth, and Patashnik’s Concrete Mathematics [307], and our book [538].) Implicit enumeration formulae. In a number of cases, the generating functions obtained by the symbolic method are still in a sense explicit, but their form is such that their coefficients are not clearly reducible to a closed form. It is then still possible to obtain initial values of the corresponding counting sequence by means of a symbolic

I. 2. ADMISSIBLE CONSTRUCTIONS AND SPECIFICATIONS

37

manipulation system. Furthermore, from generating functions, it is possible systematically to derive recurrences that lead to a procedure for computing an arbitrary number of terms of the counting sequence in a reasonably efficient manner. A typical example of this situation is the OGF of integer partitions, ∞ Y

m=1

1 , 1 − zm

for which recurrences obtained from the OGF and associated to fast algorithms are given in Note I.13 (p. 42) and Note I.19 (p. 49). An even more spectacular example is the OGF of non-plane trees, which is proved below (p. 71) to satisfy the infinite functional equation 1 1 H (z) = z exp H (z) + H (z 2 ) + H (z 3 ) + · · · , 2 3 and for which coefficients are computable in low complexity: see Note I.43, p. 72. (The references [255, 264, 456] develop a systematic approach to such problems.) The corresponding asymptotic analysis constitutes the main theme of Section VII. 5, p. 475. Asymptotic formulae. Such forms are our eventual goal as they allow for an easy interpretation and comparison of counting sequences. From a quick glance at the table of initial values of Wn (words), Pn (permutations), Tn (triangulations), as given in (2), p. 18, it is apparent that Wn grows more slowly than Tn , which itself grows more slowly than Pn . The classification of growth rates of counting sequences belongs properly to the asymptotic theory of combinatorial structures which neatly relates to the symbolic method via complex analysis. A thorough treatment of this part of the theory is presented in Chapters IV–VIII. Given the methods expounded there, it becomes possible to estimate asymptotically the coefficients of virtually any generating function, however complicated, that is provided by the symbolic method; that is, implicit enumerations in the sense above are well covered by complex asymptotic methods. Here, we content ourselves with a few remarks based on elementary real analysis. (The basic notations are described in Appendix A.2: Asymptotic notation, p. 722.) The sequence Wn = 2n grows exponentially and, in such an extreme simple case, the exact form coincides with the asymptotic form. The sequence Pn = n! must grow faster. But how fast? The answer is provided by Stirling’s formula, an important approximation originally due to James Stirling (Invitation, p. 4): n n √ 1 (n → +∞). 2π n 1 + O (32) n! = e n

(Several proofs are given in this book, based on the method of Laplace, p. 760, Mellin transforms, p. 766, singularity analysis, p. 407, and the saddle-point method, p 555.) The ratios of the exact values to Stirling’s approximations n n! √

n n e−n 2π n

1

2

5

10

100

1 000

1.084437

1.042207

1.016783

1.008365

1.000833

1.000083

38

I. COMBINATORIAL STRUCTURES AND ORDINARY GENERATING FUNCTIONS

60

50

Figure I.5. The growth regimes of three sequences f (n) = 2n , Tn , n! (from bottom to top) rendered by a plot of log10 f (n) versus n.

40

30

20

10

0 0

10

20

30

40

50

show an excellent quality of the asymptotic estimate: the error is only 8% for n = 1, less than 1% for n = 10, and less than 1 per thousand for any n greater than 100. Stirling’s formula provides in turn the asymptotic form of the Catalan numbers, by means of a simple calculation: √ 1 (2n)2n e−2n 4π n 1 (2n)! ∼ , Cn = n + 1 (n!)2 n n 2n e−2n 2π n which simplifies to (33)

4n Cn ∼ √ . π n3

n Thus, the growth of Catalan numbers is roughly √ comparable to an exponential, 4 , 3 modulated by a subexponential factor, here 1/ π n . A surprising consequence of this asymptotic estimate in the area of boolean function complexity appears in Example I.17 below (p. 77). Altogether, the asymptotic number of general trees and triangulations is well summarized by a simple formula. Approximations become more and more accurate as n becomes large. Figure I.5 illustrates the different growth regimes of our three reference sequences while Figure I.6 exemplifies the quality of the approximation with subtler phenomena also apparent on the figures and well explained by asymptotic theory. Such asymptotic formulae then make comparison between the growth rates of sequences easy. The interplay between combinatorial structure and asymptotic structure is indeed the principal theme of this book. We shall see in Part B that the generating functions provided by the symbolic method typically admit similarly simple asymptotic coefficient estimates.

I.11. The complexity of coding. A company specializing in computer-aided design has sold to you a scheme that (they claim) can encode any triangulation of size n ≥ 100 using at most 1.5n bits of storage. After reading these pages, what do you do? [Hint: sue them!] See also Note I.24 (p. 53) for related coding arguments.

I. 3. INTEGER COMPOSITIONS AND PARTITIONS

n

Cn

Cn⋆

Cn⋆ /Cn 2.25675 83341 91025 14779 23178 ˙ 1.11383 05127 5244589437 89064

1

1

2.25

10

16796

18707.89

100

0.89651 · 1057

0.90661 · 1057

1 000 10 000 100 000 1 000 000

0.20461 · 10598

0.22453 · 106015

0.17805 · 1060199

0.55303 · 10602051

0.20484 · 10598

0.22456 · 106015

0.17805 · 1060199

0.55303 · 10602051

39

1.01126 32841 24540 52257 13957 1.00112 51328 15424 16470 12827 1.00011 25013 28127 92913 51406 1.00001 12500 13281 25292 96322 1.00000 11250 00132 81250 29296

√ Figure I.6. The Catalan numbers Cn , their Stirling approximation Cn⋆ = 4n / π n 3 , and the ratio Cn⋆ /Cn . ⋆ /C I.12. Experimental asymptotics. From the data of Figure I.6, guess the values7 of C10 7 107

and of C ⋆ 6 /C5·106 to 25D. (See, Figure VI.3, p. 384, as well as, e.g., [385] for related 5·10 asymptotic expansions and [80] for similar properties.)

I. 3. Integer compositions and partitions This section and the next few provide examples of counting via specifications in classical areas of combinatorial theory. They illustrate the benefits of the symbolic method: generating functions are obtained with hardly any computation, and at the same time, many counting refinements follow from a basic combinatorial construction. The most direct applications described here relate to the additive decomposition of integers into summands with the classical combinatorial–arithmetic structures of partitions and compositions. The specifications are iterative and simply combine two levels of constructions of type S EQ, MS ET, C YC, PS ET. I. 3.1. Compositions and partitions. Our first examples have to do with decomposing integers into sums. Definition I.9. A composition of an integer n is a sequence (x1 , x2 , . . . , xk ) of integers (for some k) such that n = x1 + x2 + · · · + xk ,

x j ≥ 1.

A partition of an integer n is a sequence (x1 , x2 , . . . , xk ) of integers (for some k) such that n = x1 + x2 + · · · + xk and x1 ≥ x2 ≥ · · · ≥ xk ≥ 1. In both cases, the xi are called the summands or the parts and the quantity n is called the size. By representing summands in unary using small discs (“•”), we can render graphically a composition by drawing bars between some of the balls; if we arrange summands vertically, compositions appear as ragged landscapes. In contrast, partitions appear as staircases, also known as Ferrers diagrams [129, p. 100]; see Figure I.7. We 7In this book, we abbreviate a phrase such as “25 decimal places” by “25D”.

40

I. COMBINATORIAL STRUCTURES AND ORDINARY GENERATING FUNCTIONS

Figure I.7. Graphical representations of compositions and partitions: (left) the composition 1 + 3 + 1 + 4 + 2 + 3 = 14 with its “ragged landscape” and “balls-and-bars” models; (right) the partition 8 + 8 + 6 + 5 + 4 + 4 + 4 + 2 + 1 + 1 = 43 with its staircase (Ferrers diagram) model.

let C and P denote the class of all compositions and all partitions, respectively. Since a set can always be presented in sorted order, the difference between compositions and partitions lies in the fact that the order of summands does or does not matter. This is reflected by the use of a sequence construction (for C) against a multiset construction (for P). From this perspective, it proves convenient to regard 0 as obtained by the empty sequence of summands (k = 0), and we shall do so from now on.

Integers, as a combinatorial class. Let I = {1, 2, . . .} denote the combinatorial class of all integers at least 1 (the summands), and let the size of each integer be its value. Then, the OGF of I is X z , (34) I (z) = zn = 1−z n≥1

since In = 1 for n ≥ 1, corresponding to the fact that there is exactly one object in I for each size n ≥ 1. If integers are represented in unary, say by small balls, one has (35) I = {1, 2, 3, . . .} ∼ = {•, • •, • • •, . . .} = S EQ≥1 {•}, which constitutes a direct way to visualize the equality I (z) = z/(1 − z).

Compositions. First, the specification of compositions as sequences admits, by Theorem I.1, a direct translation into OGF: 1 (36) C = S EQ(I) H⇒ C(z) = . 1 − I (z) The collection of equations (34), (36) thus fully determines C(z): C(z)

=

1−z 1 z = 1 − 1−z 1 − 2z

= 1 + z + 2z 2 + 4z 3 + 8z 4 + 16z 5 + 32z 6 + · · · . From here, the counting problem for compositions is solved by a straightforward expansion of the OGF: one has X X C(z) = 2n z n − 2n z n+1 , n≥0

n≥0

I. 3. INTEGER COMPOSITIONS AND PARTITIONS 0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200 210 220 230 240 250

1 1024 1048576 1073741824 1099511627776 1125899906842624 1152921504606846976 1180591620717411303424 1208925819614629174706176 1237940039285380274899124224 1267650600228229401496703205376 1298074214633706907132624082305024 1329227995784915872903807060280344576 1361129467683753853853498429727072845824 1393796574908163946345982392040522594123776 1427247692705959881058285969449495136382746624 1461501637330902918203684832716283019655932542976 1496577676626844588240573268701473812127674924007424 1532495540865888858358347027150309183618739122183602176 1569275433846670190958947355801916604025588861116008628224 1606938044258990275541962092341162602522202993782792835301376 1645504557321206042154969182557350504982735865633579863348609024 1684996666696914987166688442938726917102321526408785780068975640576 1725436586697640946858688965569256363112777243042596638790631055949824 1766847064778384329583297500742918515827483896875618958121606201292619776 1809251394333065553493296640760748560207343510400633813116524750123642650624

41 1 42 627 5604 37338 204226 966467 4087968 15796476 56634173 190569292 607163746 1844349560 5371315400 15065878135 40853235313 107438159466 274768617130 684957390936 1667727404093 3972999029388 9275102575355 21248279009367 47826239745920 105882246722733 230793554364681

Figure I.8. For n = 0, 10, 20, . . . , 250 (left), the number of compositions Cn (middle) and the number of partitions Pn (right). √ The figure illustrates the difference in growth between Cn = 2n−1 and Pn = e O( n) .

implying C0 = 1 and Cn = 2n − 2n−1 for n ≥ 1; that is, Cn = 2n−1 , n ≥ 1.

(37)

This agrees with basic combinatorics since a composition of n can be viewed as the placement of separation bars at a subset of the n − 1 existing places in between n aligned balls (the “balls-and-bars” model of Figure I.7), of which there are clearly 2n−1 possibilities. Partitions. For partitions specified as multisets, the general translation mechanism of Theorem I.1, p. 27, provides 1 1 2 3 (38) P = MS ET(I) H⇒ P(z) = exp I (z) + I (z ) + I (z ) + · · · , 2 3 together with the product form corresponding to (25), p. 29, P(z) = (39)

∞ Y

m=1

1 1 − zm

= 1 + z + z2 + · · ·

1 + z2 + z4 + · · ·

1 + z3 + z6 + · · · · · ·

= 1 + z + 2z 2 + 3z 3 + 5z 4 + 7z 5 + 11z 6 + 15z 7 + 22z 8 + · · ·

(the counting sequence is EIS A000041). Contrary to compositions that are counted by the explicit formula 2n−1 , no simple form exists for Pn . Asymptotic analysis of the OGF (38) based on the saddle-point method (Chapter VIII, p. 574) shows that √ Pn = e O( n) . In fact an extremely famous theorem of Hardy and Ramanujan later improved by Rademacher (see Andrews’ book [14] and Chapter VIII) provides a full expansion of which the asymptotically dominant term is r ! 1 2n (40) Pn ∼ √ exp π . 3 4n 3

42

I. COMBINATORIAL STRUCTURES AND ORDINARY GENERATING FUNCTIONS

There are consequently appreciably fewer partitions than compositions (Figure I.8).

I.13. A recurrence for the partition numbers. Logarithmic differentiation gives z

∞ X nz n P ′ (z) = P(z) 1 − zn

implying

n=1

n Pn =

n X

σ ( j)Pn− j ,

j=1

where σ (n) is the sum of the divisors of n (e.g., σ (6) = 1 + 2 + 3 + 6 = 12). Consequently, P1 , . . . , PN can be computed in O(N 2 ) integer-arithmetic operations. (The technique is generally applicable to powersets and multisets; √ see Note I.43 (p. 72) for another application. Note I.19 (p. 49) further lowers the bound to O(N N ), in the case of partitions.)

By varying (36) and (38), we can use the symbolic method to derive a number of counting results in a straightforward manner. First, we state the following proposition. Proposition I.1. Let T ⊆ I be a subset of the positive integers. The OGFs of the classes C T := S EQ(S EQT (Z)) and P T := MS ET(S EQT (Z)) of compositions and partitions having summands restricted to T ⊂ Z≥1 are given by C T (z) =

1−

1 P

n∈T

zn

=

1 , 1 − T (z)

P T (z) =

Y

n∈T

1 . 1 − zn

Proof. A direct consequence of the specifications and Theorem I.1, p. 27.

This proposition permits us to enumerate compositions and partitions with restricted summands, as well as with a fixed number of parts. Example I.4. Compositions with restricted summands. In order to enumerate the class C {1,2} of compositions of n whose parts are only allowed to be taken from the set {1, 2}, simply write C {1,2} = S EQ(I {1,2} )

with I {1,2} = {1, 2}.

Thus, in terms of generating functions, one has C {1,2} (z) =

1 1 − I {1,2} (z)

with

I {1,2} (z) = z + z 2 .

This formula implies C {1,2} (z) =

1 = 1 + z + 2z 2 + 3z 3 + 5z 4 + 8z 5 + 13z 6 + · · · , 1 − z − z2

and the number of compositions of n in this class is expressed by a Fibonacci number, " √ !n # √ !n 1 1− 5 1+ 5 {1,2} = Fn+1 where Fn = √ Cn , − 2 2 5 of daisy–artichoke–rabbit fame In particular, the rate of growth is of the exponential type ϕ n , √ 1+ 5 where ϕ := is the golden ratio. 2 Similarly, compositions all of whose summands lie in the set {1, 2, . . . , r } have generating function (41)

C {1,...,r } (z) =

1 1 1−z = , r = 1−z 1 − z − z 2 − · · · zr 1 − 2z + zr +1 1 − z 1−z

I. 3. INTEGER COMPOSITIONS AND PARTITIONS

43

and the corresponding counts are generalized Fibonacci numbers. A double combinatorial sum expresses these counts X z(1 − zr ) j X j n − rk − 1 {1,...,r } . (−1)k (42) Cn = [z n ] = k j −1 (1 − z) j

j,k

This result is perhaps not too useful for grasping the rate of growth of the sequence when n gets large, so that asymptotic analysis is called for. Asymptotically, for any fixed r ≥ 2, there is a unique root ρr of the denominator 1 − 2z + zr +1 in ( 21 , 1), this root dominates all the other roots and is simple. Methods amply developed in Chapter IV and Example V.4 (p. 308) imply that, for some constant cr > 0, {1,...,r }

(43)

Cn

∼ cr ρr−n

for fixed r as n → ∞.

The quantity ρr plays a rˆole similar to that of the golden ratio when r = 2. . . . . . . . . . . . . . . .

I.14. Compositions into primes. The additive decomposition of integers into primes is still surrounded with mystery. For instance, it is not known whether every even number is the sum of two primes (Goldbach’s conjecture). However, the number of compositions of n into prime summands (any number of summands is permitted) is Bn = [z n ]B(z) where −1 −1 X B(z) = 1 − z p = 1 − z 2 − z 3 − z 5 − z 7 − z 11 − · · · p prime

=

1 + z 2 + z 3 + z 4 + 3 z 5 + 2 z 6 + 6 z 7 + 6 z 8 + 10 z 9 + 16 z 10 + · · ·

(EIS A023360), and complex asymptotic methods make it easy to determine the asymptotic form Bn ∼ 0.30365 · 1.47622n ; see Example V.2, p. 297. Example I.5. Partitions with restricted summands (denumerants). Whenever summands are restricted to a finite set, the special partitions that result are called denumerants. A denumerant problem popularized by P´olya [493, §3] consists in finding the number of ways of giving change of 99 cents using coins that are pennies (1 cent), nickels (5 cents), dimes (10 cents) and quarters (25 cents). (The order in which the coins are taken does not matter and repetitions are allowed.) For the case of a finite T , we predict from Proposition I.1 that P T (z) is always a rational function with poles that are at roots of unity; also the PnT satisfy a linear recurrence related to the structure of T . The solution to the original coin change problem is found to be 1 [z 99 ] = 213. (1 − z)(1 − z 5 )(1 − z 10 )(1 − z 25 ) In the same vein, one proves that 2n + 3 {1,2} Pn = 4

{1,2,3} Pn =

&

(n + 3)2 12

%

;

here ⌈x⌋ ≡ ⌊x + 12 ⌋ denotes the integer closest to the real number x. Such results are typically obtained by the two-step process: (i) decompose the rational generating function into simple fractions; (ii) compute the coefficients of each simple fraction and combine them to get the final result [129, p. 108]. The general argument also gives the generating function of partitions whose summands lie in the set {1, 2, . . . , r } as (44)

P {1,...,r } (z) =

r Y

m=1

1 . 1 − zm

44

I. COMBINATORIAL STRUCTURES AND ORDINARY GENERATING FUNCTIONS

In other words, we are enumerating partitions according to the value of the largest summand. One then finds by looking at the poles (Theorem IV.9, p. 256): {1,...,r }

(45)

Pn

∼ cr nr −1

with

cr =

1 . r !(r − 1)!

A similar argument provides the asymptotic form of PnT when T is an arbitrary finite set: PnT ∼

1 nr −1 τ (r − 1)!

Y

with τ :=

n, r := card(T ).

n∈T

This last estimate, originally due to Schur, is proved in Proposition IV.2, p. 258. . . . . . . . . . .

We next examine compositions and partitions with a fixed number of summands. Example I.6. Compositions with a fixed number of parts. Let C (k) denote the class of compositions made of k summands, k a fixed integer ≥ 1. One has C (k) = S EQk (I) ≡ I × I × · · · × I,

where the number of terms in the cartesian product is k. From here, the corresponding generating function is found to be k z C (k) (z) = I (z) with I (z) = . 1−z The number of compositions of n having k parts is thus zk n−1 (k) Cn = [z n ] , = k−1 (1 − z)k

a result which constitutes a combinatorial refinement of Cn = 2n−1 . (Note that the formula (k) Cn = n−1 k−1 also results easily from the balls-and-bars model of compositions (Figure I.7)). (k)

In such a case, the asymptotic estimate Cn ∼ n k−1 /(k − 1)! results immediately from the polynomial form of the binomial coefficient n−1 k−1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Example I.7. Partitions with a fixed number of parts. Let P (≤k) be the class of integer partitions with at most k summands. With our notation for restricted constructions (p. 30), this class is specified as P (≤k) = MS ET≤k (I).

It would be possible to appeal to the admissibility of such restricted compositions as developed in Subsection I. 6.1 below, but the following direct argument suffices in the case at hand. Geometrically, partitions, are represented as collections of points: this is the staircase model of Figure I.7, p. 40. A symmetry around the main diagonal (also known in the specialized literature as conjugation) exchanges number of summands and value of largest summand; one then has (with earlier notations) P (≤k) ∼ = P {1, . . k}

H⇒

P (≤k) (z) = P {1, . . k} (z),

so that, by (44), (46)

P (≤k) (z) ≡ P {1,...,k} =

k Y

m=1

1 . 1 − zm

I. 3. INTEGER COMPOSITIONS AND PARTITIONS

45

As a consequence, the OGF of partitions with exactly k summands, P (k) (z) = P (≤k) (z) − P (≤k−1) (z), evaluates to zk . (1 − z)(1 − z 2 ) · · · (1 − z k ) Given the equivalence between number of parts and largest part in partitions, the asymptotic estimate (45) applies verbatim here. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . P (k) (z) =

I.15. Compositions with summands bounded in number and size. The number of compositions of size n with k summands each at most r is expressible as 1 − zr k , [z n ] z 1−z which reduces to a simple binomial convolution (the calculation is similar to (42), p. 43). I.16. Partitions with summands bounded in number and size. The number of partitions of size n with at most k summands each at most ℓ is (1 − z)(1 − z 2 ) · · · (1 − z k+ℓ ) . [z n ] 2 (1 − z)(1 − z ) · · · (1 − z k ) · (1 − z)(1 − z 2 ) · · · (1 − z ℓ ) (Verifying this by recurrence is easy.) The GF reduces to the binomial coefficient k+ℓ as k , or a “q–analogue” of z → 1; it is known as a Gaussian binomial coefficient, denoted k+ℓ k z the binomial coefficient [14, 129]. The last example of this section illustrates the close interplay between combinatorial decompositions and special function identities, which constitutes a recurrent theme of classical combinatorial analysis. Example I.8. The Durfee square of partitions and stack polyominoes. The diagram of any partition contains a uniquely determined square (known as the Durfee square) that is maximal, as exemplified by the following diagram:

=

This decomposition is expressed in terms of partition GFs as [ 2 P∼ Z h × P (≤h) × P {1,...,h} , = h≥0

It gives automatically, via (44) and (46), a non-trivial identity, which is nothing but a formal rewriting of the geometric decomposition: ∞ Y

2 X zh 1 = 1 − zn h 2 h≥0 (1 − z) · · · (1 − z ) n=1

(h is the size of the Durfee square, known to manic bibliometricians as the “H-index”). Stack polyominoes. Here is a similar case illustrating the direct correspondence between geometric diagrams and generating functions, as afforded by the symbolic method. A stack polyomino is the diagram of a composition such that for some j, ℓ, one has 1 ≤ x1 ≤ x2 ≤

46

I. COMBINATORIAL STRUCTURES AND ORDINARY GENERATING FUNCTIONS

· · · ≤ x j ≥ x j+1 ≥ · · · ≥ xℓ ≥ 1 (see [552, §2.5] for further properties). The diagram representation of stack polyominoes

k

←→

P {1,...,k−1} × Z k × P {1,...,k}

translates immediately into the OGF S(z) =

X

1 zk , k 1 − z (1 − z)(1 − z 2 ) · · · (1 − z k−1 ) 2 k≥1

once use is made of the partition GFs P {1,...,k} (z) of (44). This last relation provides a bona fide algorithm for computing the initial values of the number of stack polyominoes (EIS A001523): S(z) = z + 2 z 2 + 4 z 3 + 8 z 4 + 15 z 5 + 27 z 6 + 47 z 7 + 79 z 8 + · · · . The book of van Rensburg [592] describes many such constructions and their relation to models of statistical physics, especially polyominoes. For instance, related “q–Bessel” functions appear in the enumeration of parallelogram polyominoes (Example IX.14, p. 660). . . . . . . . . . . . . . .

I.17. Systems of linear diophantine inequalities. Consider the class F of compositions of integers into four summands (x1 , x2 , x3 , x4 ) such that x1 ≥ 0,

x2 ≥ 2x1 ,

x3 ≥ 2x2 ,

x4 ≥ 2x3 ,

where the x j are in Z≥0 . The OGF is F(z) =

1 (1 − z)(1 − z 3 )(1 − z 7 )(1 − z 15 )

.

Generalize to r ≥ 4 summands (in Z≥0 ) and a similar system of inequalities. (Related GFs appear on p. 200.) Work out elementarily the OGFs corresponding to the following systems of inequalities: {x1 + x2 ≤ x3 },

{x1 + x2 ≥ x3 },

{x1 + x2 ≤ x3 + x4 },

{x1 ≤ x2 , x2 ≥ x3 , x3 ≤ x4 }.

More generally, the OGF of compositions into a fixed number of summands (in Z≥0 ), constrained to satisfy a linear system of equations and inequalities with coefficients in Z, is rational; its denominator is a product of factors of the form (1 − z j ). (Caution: this generalization is non-trivial: see Stanley’s treatment in [552, §4.6].)

Figure I.9 summarizes what has been learned regarding compositions and partitions. The way several combinatorial problems are solved effortlessly by the symbolic method is worth noting. I. 3.2. Related constructions. It is also natural to consider the two constructions of cycle and powerset when these are applied to the set of integers I.

I. 3. INTEGER COMPOSITIONS AND PARTITIONS

Specification

OGF

47

coefficients

Compositions: all

S EQ(S EQ≥1 (Z))

parts ≤ r

S EQ(S EQ1 . . r (Z))

k parts

S EQk (S EQ≥1 (Z))

cyclic

C YC(S EQ≥1 (Z))

1−z 2n−1 1 − 2z 1−z ∼ cr ρr−n 1 − 2z + zr +2 n k−1 zk ∼ k (k − 1)! (1 − z) 2n Eq. (48) ∼ n

Partitions: MS ET(S EQ≥1 (Z))

all parts ≤ r

MS ET(S EQ1 . . r (Z))

≤ k parts

∼ = MS ET(S EQ1 . . k (Z))

distinct parts PS ET(S EQ≥1 (Z))

∞ Y

m=1 r Y

m=1 k Y

m=1 ∞ Y

m=1

(p. 40) (pp. 42, 308) (p. 44) (p. 48) q

2n 3

(1 − z m )−1 ∼

1 π √ e 4n 3

(1 − z m )−1 ∼

nr −1 r !(r − 1)!

(pp. 43, 258)

(1 − z m )−1 ∼

n k−1 k!(k − 1)!

(pp. 44, 258)

(1 + z m )

∼

(pp. 41, 574)

33/4 π √n/3 (pp. 48, 579) e 12n 3/4

Figure I.9. Partitions and compositions: specifications, generating functions, and coefficients (in exact or asymptotic form).

Cyclic compositions (wheels). The class D = C YC(I) comprises compositions defined up to circular shift of the summands; so, for instance 2 + 3 + 1 + 2 + 5, 3 + 1 + 2 + 5 + 2, etc, are identified. Alternatively, we may view elements of D as “wheels” composed of circular arrangements of rows of balls (taken up to rotation):

a “wheel” (cyclic composition)

By the translation of the cycle construction, the OGF is

(47)

D(z) = =

∞ X ϕ(k) k=1

k

log 1 −

zk 1 − zk

−1

z + 2 z 2 + 3 z 3 + 5 z 4 + 7 z 5 + 13 z 6 + 19 z 7 + 35 z 8 + · · · .

48

I. COMBINATORIAL STRUCTURES AND ORDINARY GENERATING FUNCTIONS

The coefficients are thus (EIS A008965) 1X 2n 1X ϕ(k)(2n/k − 1) ≡ −1 + ϕ(k)2n/k ∼ , (48) Dn = n n n k|n

k|n

where the condition “k | n” indicates a sum over the integers k dividing n. Notice that Dn is of the same asymptotic order as n1 Cn , which is suggested by circular symmetry of wheels, but there is a factor: Dn ∼ 2Cn /n. Partitions into distinct summands. The class Q = PS ET(I) is the subclass of P = MS ET(I) corresponding to partitions determined as in Definition I.9, but with the strict inequalities xk > · · · > x1 , so that the OGF is Y (49) Q(z) = (1 + z n ) = 1 + z + z 2 + 2z 3 + 2z 4 + 3z 5 + 4z 6 + 5z 7 + · · · . n≥1

The coefficients (EIS A000009) are not expressible in closed form. However, the saddle-point method (Section VIII. 6, p. 574) yields the approximation: r n 33/4 (50) Qn ∼ , exp π 3 12n 3/4 which has a shape similar to that of Pn in (40), p. 41.

I.18. Odd versus distinct summands. The partitions of n into odd summands (On ) and the

ones into distinct summands (Qn ) are equinumerous. Indeed, one has Q(z) =

∞ Y

m=1

(1 + z m ),

O(z) =

∞ Y

(1 − z 2 j+1 )−1 .

j=0

Equality results from substituting (1 + a) = (1 − a 2 )/(1 − a) with a = z m , Q(z) =

1 1 1 − z2 1 − z4 1 − z6 1 − z8 1 − z10 1 ··· = ··· , 1 − z 1 − z2 1 − z 3 1 − z4 1 − z 5 1 − z 1 − z3 1 − z5

and simplification of the numerators with half of the denominators (in boldface).

Let I pow

Partitions into powers. = {1, 2, 4, 8, . . .} be the set of powers of 2. The corresponding P and Q partitions have OGFs P pow (z)

Q pow (z)

=

∞ Y

j=0

1 1 − z2

j

= 1 + z + 2z 2 + 2z 3 + 4z 4 + 4z 5 + 6z 6 + 6z 7 + 10z 8 + · · · ∞ Y j (1 + z 2 ) = j=0

= 1 + z + z2 + z3 + z4 + z5 + · · · .

The first sequence 1, 1, 2, 2, . . . is the “binary partition sequence” (EIS A018819); the difficult asymptotic analysis was performed by de Bruijn [141] who obtained an esti2 mate that involves subtle fluctuations and is of the global form e O(log n) . The function

I. 4. WORDS AND REGULAR LANGUAGES

49

Q pow (z) reduces to (1− z)−1 since every number has a unique additive decomposition into powers of 2. Accordingly, the identity ∞ Y 1 j (1 + z 2 ), = 1−z j=0

first observed by Euler is sometimes nicknamed the “computer scientist’s identity” as it reflects the property that every number admits a unique binary representation. There exists a rich set of identities satisfied by partition generating functions— this fact is down to deep connections with elliptic functions, modular forms, and q–analogues of special functions on the one hand, basic combinatorics and number theory on the other hand. See [14, 129] for introductions to this fascinating subject.

I.19. Euler’s pentagonal number theorem. This famous identity expresses 1/P(z) as Y

(1 − z n ) =

n≥1

X

(−1)k z k(3k+1)/2 .

k∈Z

It is proved formally and combinatorially in Comtet’s reference [129, p. 105] and it serves to illustrate “proofs from THE BOOK” in the splendid exposition √ of Aigner and Ziegler [7, §29]. Consequently, the numbers {P j } Nj=0 can be determined in O(N N ) integer operations.

I.20. A digital surprise. Define the constant 9 99 999 9999 ··· . 10 100 1000 10000 Is it a surprise that it evaluates numerically to . ϕ = 0.8900100999989990000001000099999999899999000000000010 · · · , ϕ :=

that is, its decimal representation involves only the digits 0, 1, 8, 9? [This is suggested by a note of S. Ramanujan, “Some definite integrals”, Messenger of Math. XLIV, 1915, pp. 10–18.]

I.21. Lattice points. The number of lattice points with integer coordinates that belong to the closed ball of radius n in d-dimensional Euclidean space is 2

[z n ]

1 (2(z))d 1−z

where

2(z) = 1 + 2

∞ X

2

zn .

n=1

Estimates may be obtained via the saddle-point method (Note VIII.35, p. 589).

I. 4. Words and regular languages Fix a finite alphabet A whose elements are called letters. Each letter is taken to have size 1; i.e., it is an atom. A word8 is any finite sequence of letters, usually written without separators. So, for us, with the choice of the Latin alphabet (A = {a,. . . ,z}), sequences such as ygololihp, philology, zgrmblglps are words. We denote the set of all words (often written as A⋆ in formal linguistics) by W. Following a well-established tradition in theoretical computer science and formal linguistics, any subset of W is called a language (or formal language, when the distinction with natural languages has to be made). 8An alternative to the term “word” sometimes preferred by computer scientists is “string”; biologists

often refer to words as “sequences”.

50

I. COMBINATORIAL STRUCTURES AND ORDINARY GENERATING FUNCTIONS

Words: a–runs < k exclude subseq. p exclude factor p circular

OGF

coefficients

1 1 − mz

mn

(p. 50)

∼ ck ρk−n

(pp. 51, 308)

≈ (m − 1)n n |p|−1

(p. 54)

∼ cpρp

(pp. 61, 271)

∼ m n /n

(p. 64)

1 − zk 1 − mz + (m − 1)z k+1 Eq. (55) cp(z) z |p| + (1 − mz)cp(z)

Eq. (64)

regular language

≈ C · An n k

[rational]

context-free lang.

−n

≈ C · An n p/q

[algebraic]

(pp. 56, 302, 342) (pp. 80, 501)

Figure I.10. Words over an m–ary alphabet: generating functions and coefficients.

From the definition of the set of words W, one has 1 , 1 − mz where m is the cardinality of the alphabet, i.e., the number of letters. The generating function gives us the counting result

(51)

W∼ = S EQ(A)

H⇒

W (z) =

Wn = m n . This result is elementary, but, as is usual with symbolic methods, many enumerative consequences result from a given construction. It is precisely the purpose of this section to examine some of them. We shall introduce separately two frameworks that each have great expressive power for describing languages. The first one is iterative (i.e., non-recursive) and it bases itself on “regular specifications” that only involve the constructions of sum, product, and sequence; the other one, which is recursive (but of a very simple form), is best conceived of in terms of finite automata and is equivalent to linear systems of equations. Both frameworks turn out to be logically equivalent in the sense that they determine the same family of languages, the regular languages, though the equivalence is non-trivial (Appendix A.7: Regular languages, p. 733), and each particular problem usually admits a preferred representation. The resulting OGFs are invariably rational functions, a fact to be systematically exploited from an asymptotic standpoint in Chapter V. Figure I.10 recapitulates some of the major word problems studied in this chapter, together with corresponding approximations9. 9 In this book, we reserve “∼” for the technical sense of “asymptotically equivalent” defined in Ap-

pendix A.2: Asymptotic notations, p. 722; we reserve the symbol “≈” to mean “approximately equal” in a vaguer sense, where formulae have been simplified by omitting constant factors or terms of secondary importance (in context).

I. 4. WORDS AND REGULAR LANGUAGES

51

I. 4.1. Regular specifications. Consider words (or strings) over the binary alphabet A = {a, b}. There is an alternative way to construct binary strings. It is based on the observation that, with a minor adjustment at the beginning, a string decomposes into a succession of “blocks” each formed with a single b followed by an arbitrary (possibly empty) sequence of as. For instance aaabaababaabbabbaaa decomposes as [aaa] baa | ba | baa | b | ba | b | baaa. Omitting redundant10 symbols, we have the alternative decomposition:

(52)

W∼ = S EQ(a) × S EQ(b S EQ(a))

H⇒

W (z) =

1 1 . 1 1 − z 1 − z 1−z

This last expression reduces to (1 − 2z)−1 as it should. Longest runs. The interest of the construction just seen is to take into account various meaningful properties, for example longest runs. Abbreviate by a n} = 1 − P {C ≤ n} = n![z n ] e z − e z/r − 1 .

An application of the Eulerian integral trick of (27) then provides a representation of the expectation of the time needed for a full collection as Z ∞ (31) E(C) = 1 − (1 − e−t/r )r dt. 0

A simple calculation (expand by the binomial theorem and integrate termwise) shows that r X r (−1) j−1 E(C) = r , j j j=1

which constitutes a first answer to the coupon collector problem in the form of an alternating sum. Alternatively, in (31), perform the change of variables v = 1 − e−t/r , then expand and integrate termwise; this process provides the more tractable form E(C) = r Hr ,

(32) where Hr is the harmonic number: (33)

Hr = 1 +

1 1 1 + + ··· + . 2 3 r

Formula (32) is by the way easy to interpret directly6: one needs on average 1 = r/r trials to get the first day, then r/(r − 1) to get a different day, etc. Regarding (32), one has available the well-known formula (by comparing sums with integrals or by Euler–Maclaurin summation), 1 . + O(r −2 ), γ = 0.57721 56649, Hr = log r + γ + 2r where γ is known as Euler’s constant. Thus, the expected time for a full collection satisfies 1 (34) E(C) = r log r + γ r + + O(r −1 ). 2 Here the “surprise” lies in the nonlinear growth of the expected time for a full collection. For a . year on Earth, r = 365, the exact expected value is = 2364.64602 whereas the approximation provided by the first three terms of (34) yields 2364.64625, representing a relative error of only one in ten million. As usual, the symbolic treatment adapts to a variety of situations, for instance, to multiple collections. One finds: the expected time till each item (birthday or coupon) is obtained b times is Z ∞ r J (r, b) = 1 − 1 − eb−1 (t/r )e−t/r dt. 0

6Such elementary derivations are very much problem specific: contrary to the symbolic method, they

do not usually generalize to more complex situations.

118

II. LABELLED STRUCTURES AND EGFS

This expression vastly generalizes the standard case (31), which corresponds to b = 1. From it, one finds [454] J (r, b) = r (log r + (b − 1) log log r + γ − log(b − 1)! + o(1)) , so that only a few more trials are needed in order to obtain additional collections. . . . . . . . . .

II.9. The little sister. The coupon collector has a little sister to whom he gives his duplicates. Foata, Lass, and Han [266] show that the little sister misses on average Hr coupons when her big brother first obtains a complete collection.

II.10. The probability distribution of time till a complete collection. The saddle-point method (Chapter VIII) may be used to prove that, in the regime n = r log r + tr , we have −t

lim P(C ≤ r log r + tr ) = e−e .

t→∞

This continuous probability distribution is known as a double exponential distribution. For the time C (b) till a collection of multiplicity b, one has lim P(C (b) < r log r + (b − 1)r log log r + tr ) = exp(−e−t /(b − 1)!),

t→∞

a property known as the Erd˝os–R´enyi law, which finds application in the study of random graphs [195].

Words as both labelled and unlabelled objects. What distinguishes a labelled structure from an unlabelled one? There is nothing intrinsic there, and everything is in the eye of the beholder—or rather in the type of construction adopted when modelling a specific problem. Take the class of words W over an alphabet of cardinality r . The two generating functions (an OGF and an EGF respectively), X X 1 zn b (z) ≡ W Wn z n = and W (z) ≡ = er z , Wn 1 − r z n! n n

leading in both cases to Wn = r n , correspond to two different ways of constructing words: the first one directly as an unlabelled sequence, the other as a labelled power of letter positions. A similar situation arises for r –partitions, for which we find as OGF and EGF, (e z − 1)r zr b and S (r ) (z) = , S (r ) (z) = (1 − z)(1 − 2z) · · · (1 − r z) r! by viewing these either as unlabelled structures (an encoding via words of a regular language in Section I. 4.3, p. 62) or directly as labelled structures (this chapter, p. 108).

II.11. Balls switching chambers: the Ehrenfest2 model. Consider a system of two chambers A and B (also classically called “urns”). There are N distinguishable balls, and, initially, chamber A contains them all. At any instant 21 , 32 , . . ., one ball is allowed to change from one

chamber to the other. Let E n[ℓ] be the number of possible evolutions that lead to chamber A containing ℓ balls at instant n and E [ℓ] (z) the corresponding EGF. Then N E [ℓ] (z) = (cosh z)ℓ (sinh z) N −ℓ , E [N ] (z) = (cosh z) N ≡ 2−N (e z + e−z ) N . ℓ

[Hint: the EGF E [N ] enumerates mappings where each preimage has an even cardinality.] In particular the probability that urn A is again full at time 2n is N X 1 N (N − 2k)2n . k 2 N N 2n k=0

II. 4. ALIGNMENTS, PERMUTATIONS, AND RELATED STRUCTURES

119

This famous model was introduced by Paul and Tatiana Ehrenfest [188] in 1907, as a simplified model of heat transfer. It helped resolve the apparent contradiction between irreversibility in thermodynamics (the case N → ∞) and recurrence of systems undergoing ergodic transformations (the case N < ∞). See especially Mark Kac’s discussion [361]. The analysis can also be carried out by combinatorial methods akin to those of weighted lattice paths: see Note V.25, p. 336 and [304].

II. 4. Alignments, permutations, and related structures In this section, we start by considering specifications built by piling up two constructions, sequences-of-cycles and sets-of-cycles respectively. They define a new class of objects, alignments, while serving to specify permutations in a novel way. (These specifications otherwise parallel surjections and set partitions.) In this context, permutations are examined under their cycle decomposition, the corresponding enumeration results being the most important ones combinatorially (Subsection II. 4.1 and Figure II.8, p. 123). In Subsection II. 4.2, we recapitulate the meaning of classes that can be defined iteratively by a combination of any two nested labelled constructions. II. 4.1. Alignments and permutations. The two specifications under consideration now are (35)

O = S EQ(C YC(Z)),

and

P = S ET(C YC(Z)),

specifying new objects called alignments (O) as well as an important decomposition of permutations (P). Alignments. An alignment is a well-labelled sequence of cycles. Let O be the class of all alignments. Schematically, one can visualize an alignment as a collection of directed cycles arranged in a linear order, somewhat like slices of a sausage fastened on a skewer:

The symbolic method provides, O = S EQ(C YC(Z))

H⇒

O(z) =

1 , 1 − log(1 − z)−1

and the expansion starts as O(z) = 1 + z + 3

z2 z3 z4 z5 + 14 + 88 + 694 + · · · , 2! 3! 4! 5!

but the coefficients (see EIS A007840: “ordered factorizations of permutations into cycles”) appear to admit no simple form.

120

II. LABELLED STRUCTURES AND EGFS

4 10

17

5

14

15

16

6

12

7

2

13

8

1 11

9 3

A permutation may be viewed as a set of cycles that are labelled circular digraphs. The diagram shows the decomposition of the permutation 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 σ = 11 12 13 17 10 15 14 9 3 4 6 2 7 8 1 5 16 .

(Cycles here read clockwise and i is connected to σi by an edge in the graph.) Figure II.7. The cycle decomposition of permutations.

Permutations and cycles. From elementary mathematics, it is known that a permutation admits a unique decomposition into cycles. Let σ be a permutation. Start with any element, say 1, and draw a directed edge from 1 to σ (1), then continue connecting to σ 2 (1), σ 3 (1), and so on; a cycle containing 1 is obtained after at most n steps. If one repeats the construction, taking at each stage an element not yet connected to earlier ones, the cycle decomposition of the permutation σ is obtained; see Figure II.7. This argument shows that the class of sets-of-cycles (corresponding to P in (35)) is isomorphic to the class of permutations as defined in Example II.2, p. 98: (36) P∼ = S EQ(Z). = S ET(C YC(Z)) ∼ This combinatorial isomorphism is reflected by the obvious series identity 1 1 P(z) = exp log = . 1−z 1−z The property that exp and log are inverse of one another is nothing but an analytic reflex of the combinatorial fact that permutations uniquely decompose into cycles! As regards combinatorial applications, what is especially fruitful is the variety of special results derived from the decomposition of permutations into cycles. By a use of restricted construction that entirely parallels Proposition II.2, p. 110, we obtain the following statement. Proposition II.4. The class P (A,B) of permutations with cycle lengths in A ⊆ Z>0 and with cycle number that belongs to B ⊆ Z≥0 has EGF X zb X za , β(z) = . P (A,B) (z) = β(α(z)) where α(z) = a b! a∈A

b∈B

II.12. What about alignments? With similar notations, one has for alignments O (A,B) (z) = β(α(z))

where

corresponding to O(A,B) = S EQ B (C YC A (Z)).

α(z) =

X za X zb , , β(z) = a

a∈A

b∈B

II. 4. ALIGNMENTS, PERMUTATIONS, AND RELATED STRUCTURES

121

Example II.12. Stirling cycle numbers. The class P (r ) of permutations that decompose into r cycles, satisfies r 1 1 (37) P (r ) = S ETr (C YC(Z)) H⇒ P (r ) (z) = log . r! 1−z The number of such permutations of size n is then r 1 n! n (r ) [z ] log . (38) Pn = r! 1−z These numbers are fundamental quantities of combinatorial analysis. They are known as the Stirling numbers of the first kind, or better, according to a proposal of Knuth, the Stirling cycle numbers. Together with the Stirling partition numbers, the properties of the Stirling cycle numbers are explored in the book by Graham, Knuth, and Patashnik [307] where they are denoted by nr . See Appendix A.8: Stirling numbers, p. 735. (Note that the number of alignments formed with r cycles is r ! nr .) As we shall see shortly (p. 140) Stirling numbers also surface in the enumeration of permutations by their number of records. It is also of interest to determine what happens regarding cycles in a random permutation of size n. Clearly, when the uniform distribution is placed over all elements of Pn , each particular permutation has probability exactly 1/n!. Since the probability of an event is the quotient of the number of favorable cases over the total number of cases, the quantity 1 n pn,k := n! k is the probability that a random element of Pn has k cycles. This probabilities can be effectively determined for moderate values of n from (38) by means of a computer algebra system. Here are for instance selected values for n = 100: k pn,k

1 0.01

2 0.05

3 0.12

4 0.19

5 0.21

6 0.17

7 0.11

8 0.06

9 0.03

10 0.01

For this value n = 100, we expect in a vast majority of cases the number of cycles to be in the interval [1, 10]. (The residual probability is only about 0.005.) Under this probabilistic model, the mean is found to be about 5.18. Thus: A random permutation of size 100 has on average a little more than 5 cycles; it rarely has more than 10 cycles. Such procedures demonstrate a direct exploitation of symbolic methods. They do not however tell us how the number of cycles could depend on n, as n increases unboundedly. Such questions are to be investigated systematically in Chapters III and IX. Here, we shall content ourselves with a brief sketch. First, form the bivariate generating function, P(z, u) :=

∞ X

P (r ) (z)u r ,

r =0

and observe that P(z, u) =

r ∞ r X u 1 1 log = (1 − z)−u . = exp u log r! 1−z 1−z

r =0

Newton’s binomial theorem then provides

−u . [z n ](1 − z)−u = (−1)n n

122

II. LABELLED STRUCTURES AND EGFS

In other words, a simple formula n X n k (39) u = u(u + 1)(u + 2) · · · (u + n − 1) k k=0

encodes precisely all the Stirling cycle numbers corresponding to a fixed value of n. From here, P the expected number of cycles, µn := k kpn,k is easily found to be expressed in terms of harmonic numbers (use logarithmic differentiation of (39)): µn = Hn ≡ 1 +

1 1 + ··· + . 2 n

. In particular, one has µ100 ≡ H100 = 5.18738. In general: The mean number of cycles in a random permutation of size n grows logarithmically with n, µn ∼ log n. . . . . . . . . . . . . . . . . . Example II.13. Involutions and permutations without long cycles. A permutation σ is an involution if σ 2 = Id, with Id the identity permutation. Clearly, an involution can have only cycles of sizes 1 and 2. The class I of all involutions thus satisfies ! z2 . (40) I = S ET(C YC1,2 (Z)) H⇒ I (z) = exp z + 2 The explicit form of the EGF lends itself to expansion, In =

⌊n/2⌋ X k=0

n! , (n − 2k)!2k k!

which solves the counting problem explicitly. A pairing is an involution without a fixed point. In other words, only cycles of length 2 are allowed, so that J = S ET(C YC2 (Z))

H⇒

2 J (z) = e z /2 ,

J2n = 1 · 3 · 5 · · · (2n − 1).

(The formula for Jn , hence that of In , can be checked by a direct reasoning.) Generally, the EGF of permutations, all of whose cycles (in particular the largest one) have length at most equal to r , satisfies r j X z (r ) . B (z) = exp j j=1

(r )

The numbers bn = [z n ]B (r ) (z) satisfy the recurrence (r )

(r )

(r )

(n + 1)bn+1 = (n + 1)bn − bn−r , by which they can be computed quickly, while they can be analysed asymptotically by means of the saddle-point method (Chapter VIII, p. 568). This gives access to the statistics of the longest cycle in a permutation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Example II.14. Derangements and permutations without short cycles. Classically, a derangement is defined as a permutation without fixed points, i.e., σi 6= i for all i. Given an integer r , an r –derangement is a permutation all of whose cycles (in particular the shortest one) have length larger than r . Let D(r ) be the class of all r –derangements. A specification is (41)

D(r ) = S ET(C YC>r (Z)),

II. 4. ALIGNMENTS, PERMUTATIONS, AND RELATED STRUCTURES

Specification

EGF 1 1 −z r 1 1 log r! 1−z

Permutations: S EQ(Z) r cycles

S ETr (C YC(Z))

involutions

S ET(C YC1 . . 2 (Z)) e z+z /2 zr z + ··· + S ET(C YC1 . . r (Z)) exp 1 r e−z S ET(C YC>1 (Z)) 1−z r exp − 1z − · · · − zr S ET(C YC>r (Z)) 1−z

all cycles ≤ r derangements

all cycles > r

2

123

coefficient n! n r

(p. 104) (p. 121)

≈ n n/2

(pp. 122, 558)

≈ n 1−1/r

(pp. 122, 568)

∼ n!e−1

(pp. 122, 261)

∼ n!e− Hr (pp. 123, 261)

Figure II.8. A summary of permutation enumerations.

the corresponding EGF then being (42)

D (r ) (z) = exp

P j exp(− rj=1 zj ) X zj = . j 1−z j>r

For instance, when r = 1, a direct expansion yields (1)

1 1 (−1)n Dn =1− + − ··· + , n! 1! 2! n! a truncation of the series expansion of exp(−1) that converges rapidly to e−1 . Phrased differently, this becomes a famous combinatorial problem with a pleasantly quaint nineteenth-century formulation [129]: “A number n of people go to the opera, leave their hats on hooks in the cloakroom and grab them at random when leaving; the probability that nobody gets back his own hat is asymptotic to 1/e, which is nearly 37%.” The usual proof uses inclusion–exclusion; see Section III. 7, p. 198 for both the classical and symbolic arguments. (It is a sign of changing times that Motwani and Raghavan [451, p. 11] describe the problem as one of sailors that return to their ship in a state of inebriation and choose random cabins to sleep in.) For the generalized derangement problem, we have, for any fixed r (with Hr a harmonic number, p. 117), (r )

(43)

Dn ∼ e− Hr , n!

which is proved easily by complex asymptotic methods (Chapter IV, p. 261). . . . . . . . . . . . . .

Similar to several other structures that we have been considering previously, permutation allow for transparent connections between structural constraints and the forms of generating functions. The major counting results encountered in this section are summarized in Figure II.8.

124

II. LABELLED STRUCTURES AND EGFS

II.13. Permutations such that σ f = Id. Such permutations are “roots of unity” in the symmetric group. Their EGF is X zd , exp d d| f

where the sum extends to all divisors d of f .

II.14. Parity constraints in permutations. The EGFs of permutations having only even-size cycles or odd-size cycles (O(z)) are, respectively, r 1 1 1+z 1 1+z 1 p , O(z) = exp = = log log . E(z) = exp 2 2 2 1 − z 1−z 1 − z2 1−z

One finds E 2n = (1 · 3 · 5 · · · (2n − 1))2 and O2n = E 2n , O2n+1 = (2n + 1)E 2n . The EGFs of permutations having an even number of cycles (E ∗ (z)) and an odd number of cycles (O ∗ (z)) are, respectively, 1 1 1 1 1−z 1 z−1 1 = = + , O ∗ (z) = sinh log + , E ∗ (z) = cosh log 1−z 21−z 2 1−z 21−z 2 so that parity of the number of cycles is evenly distributed among permutations of size n as soon as n ≥ 2. The generating functions obtained in this way are analogous to the ones appearing in the discussion of “Comtet’s square”, p. 111.

II.15. A hundred prisoners I. This puzzle originates with a paper of G´al and Miltersen [275, 612]. A hundred prisoners, each uniquely identified by a number between 1 and 100, have been sentenced to death. The director of the prison gives them a last chance. He has a cabinet with 100 drawers (numbered 1 to 100). In each, he’ll place at random a card with a prisoner’s number (all numbers different). Prisoners will be allowed to enter the room one after the other and open, then close again, 50 drawers of their own choosing, but will not in any way be allowed to communicate with one another afterwards. The goal of each prisoner is to locate the drawer that contains his own number. If all prisoners succeed, then they will all be spared; if at least one fails, they will all be executed. There are two mathematicians among the prisoners. The first one, a pessimist, declares . that their overall chances of success are only of the order of 1/2100 = 8 · 10−31 . The second one, a combinatorialist, claims he has a strategy for the prisoners, which has a greater than 30% chance of success. Who is right? [Note III.10, p. 176 provides a solution, but our gentle reader is advised to reflect on the problem for a few moments, before she jumps there.]

II. 4.2. Second-level structures. Consider the three basic constructors of labelled sequences (S EQ), sets (S ET), and cycles (C YC). We can play the formal game of examining what the various combinations produce as combinatorial objects. Restricting attention to superpositions of two constructors (an external one applied to an internal one) gives nine possibilities summarized by the table of Figure II.9. The classes of surjections, alignments, set partitions, and permutations appear naturally as S EQ ◦ S ET, S EQ ◦ C YC, S ET ◦ S ET, and S ET ◦ C YC (top right corner). The others represent essentially non-classical objects. The case of the class L = S EQ(S EQ≥1 (Z)) describes objects that are (ordered) sequences of linear graphs; this can be interpreted as permutations with separators inserted, e.g, 53|264|1, or alternatively as integer compositions with a labelling superimposed, so that L n = n! 2n−1 . The class F = S ET(S EQ≥1 (Z)) corresponds to unordered collections of permutations; in other words, “fragments” are obtained by breaking a permutation into pieces

II. 5. LABELLED TREES, MAPPINGS, AND GRAPHS

ext.\int.

S EQ

S EQ≥1

S ET≥1 Surjections (R)

Alignments (O)

S EQ ◦ S EQ 1−z 1 − 2z

S EQ ◦ S ET 1 2 − ez

S EQ ◦ C YC 1 1 − log(1 − z)−1 Permutations (P)

S ET ◦ S EQ e z/(1−z)

C YC

C YC

Labelled compositions (L)

Fragmented permutations (F ) Set partitions (S) S ET

125

S ET ◦ S ET e

e z −1

Supernecklaces (S I )

Supernecklaces (S I I )

C YC ◦ S EQ 1−z log 1 − 2z

C YC ◦ S ET

log(2 − e z )−1

S ET ◦ C YC 1 1−z Supernecklaces (S I I I ) C YC ◦ C YC 1 log 1 − log(1 − z)−1

Figure II.9. The nine second-level structures.

(pieces must be non-empty for definiteness). The interesting EGF is z3 z4 z2 + 13 + 73 + · · · , 2! 3! 4! (EIS A000262: “sets of lists”). The corresponding asymptotic analysis serves to illustrate an important aspect of the saddle-point method in Chapter VIII (p. 562). What we termed “supernecklaces” in the last row represents cyclic arrangements of composite objects existing in three brands. All sorts of refinements, of which Figures II.8 and II.9 may give an idea, are clearly possible. We leave to the reader’s imagination the task of determining which among the level 3 structures may be of combinatorial interest. . . F(z) = e z/(1−z) = 1 + z + 3

II.16. A meta-exercise: Counting specifications of level n. The algebra of constructions satisfies the combinatorial isomorphism S ET(C YC(X )) ∼ = S EQ(X ) for all X . How many different terms involving n constructions can be built from three symbols C YC, S ET, S EQ satisfying a semi-group law (“◦”) together with the relation S ET ◦ C YC = S EQ? This determines the number of specifications of level n. [Hint: the OGF is rational as normal forms correspond to words with an excluded pattern.] II. 5. Labelled trees, mappings, and graphs In this section, we consider labelled trees as well as other important structures that are naturally associated with them. As in the unlabelled case considered in Section I. 6, p. 83, the corresponding combinatorial classes are inherently recursive, since a tree is obtained by appending a root to a collection (set or sequence) of subtrees. From here, it is possible to build the “functional graphs” associated to mappings from a finite set to itself—these decompose as sets of connected components that are cycles of trees. Variations of these construction finally open up the way to the enumeration of graphs having a fixed excess of the number of edges over the number of vertices.

126

II. LABELLED STRUCTURES AND EGFS

3 2

5

&

( 3, 2, 5, 1, 7, 4, 6)

1 7

4

6

Figure II.10. A labelled plane tree is determined by an unlabelled tree (the “shape”) and a permutation of the labels 1, . . . , n.

II. 5.1. Trees. The trees to be studied here are labelled, meaning that nodes bear distinct integer labels. Unless otherwise specified, they are rooted, meaning as usual that one node is distinguished as the root. Labelled trees, like their unlabelled counterparts, exist in two varieties: (i) plane trees where an embedding in the plane is understood (or, equivalently, subtrees dangling from a node are ordered, say, from left to right); (ii) non-plane trees where no such embedding is imposed (such trees are then nothing but connected undirected acyclic graphs with a distinguished root). Trees may be further restricted by the additional constraint that the nodes’ outdegrees should belong to a fixed set ⊆ Z≥0 where ∋ 0. Plane labelled trees. We first dispose of the plane variety of labelled trees. Let A be the set of (rooted labelled) plane trees constrained by . This family is A = Z ⋆ S EQ (A),

where Z represents the atomic class consisting of a single labelled node: Z = {1}. The sequence construction appearing here reflects the planar embedding of trees, as subtrees stemming from a common root are ordered between themselves. Accordingly, the EGF A(z) satisfies X A(z) = zφ(A(z)) where φ(u) = uω. ω∈

This is exactly the same equation as the one satisfied by the ordinary GF of – 1 An is the number restricted unlabelled plane trees (see Proposition I.5, p. 66). Thus, n! of unlabelled trees. In other words: in the plane rooted case, the number of labelled trees equals n! times the corresponding number of unlabelled trees. As illustrated by Figure II.10, this is easily understood combinatorially: each labelled tree can be defined by its “shape” that is an unlabelled tree and by the sequence of node labels where nodes are traversed in some fixed order (preorder, say). In a way similar to Proposition I.5, p. 66, one has, by Lagrange inversion (Appendix A.6: Lagrange Inversion, p. 732): An = n![z n ]A(z) = (n − 1)![u n−1 ]φ(u)n .

II. 5. LABELLED TREES, MAPPINGS, AND GRAPHS

1

1 2

1

1

2

2 3 3

2

3

1

3 2 1

3

2

3

1 1 2

1 2

127

2 3

1

3 3

1

2

2 1

Figure II.11. There are T1 = 1, T2 = 2, T3 = 9, and in general Tn = n n−1 Cayley trees of size n.

This simple analytic–combinatorial relation enables us to transpose all of the enumeration results of Subsection I. 5.1, p. 65, to plane labelled trees, upon multiplying the evaluations by n!, of course. In particular, the total number of “general” plane labelled trees (with no degree restriction imposed, i.e., = Z≥0 ) is (2n − 2)! 1 2n − 2 = = 2n−1 (1 · 3 · · · (2n − 3)) . n! × n n−1 (n − 1)! The corresponding sequence starts as 1, 2, 12, 120, 1680 and is EIS A001813. Non-plane labelled trees. We next turn to non-plane labelled trees (Figure II.11) to which the rest of this section will be devoted. The class T of all such trees is definable by a symbolic equation, which provides an implicit equation satisfied by the EGF: (44)

T = Z ⋆ S ET(T )

H⇒

T (z) = ze T (z) .

There the set construction translates the fact that subtrees stemming from the root are not ordered between themselves. From the specification (44), the EGF T (z) is defined implicitly by the “functional equation” (45)

T (z) = ze T (z) .

The first few values are easily found, for instance by the method of indeterminate coefficients: z2 z3 z4 z5 T (z) = z + 2 + 9 + 64 + 625 + · · · . 2! 3! 4! 5! As suggested by the first few coefficients(9 = 32 , 64 = 43 , 625 = 54 ), the general formula is (46)

Tn = n n−1

which is established (as in the case of plane unlabelled trees) by Lagrange inversion: 1 n−1 u n n [u ](e ) = n n−1 . (47) Tn = n! [z ]T (z) = n! n The enumeration result Tn = n n−1 is a famous one, attributed to the prolific British mathematician Arthur Cayley (1821–1895) who had keen interest in combinatorial mathematics and published altogether over 900 papers and notes. Consequently, formula (46) given by Cayley in 1889 is often referred to as “Cayley’s formula” and unrestricted non-plane labelled trees are often called “Cayley trees”. See [67, p. 51] for a historical discussion. The function T (z) is also known as the

128

II. LABELLED STRUCTURES AND EGFS

(Cayley) “tree function”; it is a close relative of the W -function [131] defined implicitly by W e W = z, which was introduced by the Swiss mathematician Johann Lambert (1728–1777) otherwise famous for first proving the irrationality of the number π . A similar process gives the number of (non-plane rooted) trees where all outdegrees of nodes are restricted to lie in a set . This corresponds to the specification X uω T () = Z ⋆ S ET (T () ) H⇒ T () (z) = zφ(T () (z)), φ(u) := . ω! ω∈

What the last formula involves is the “exponential characteristic” of the degree sequence (as opposed to the ordinary characteristic, in the planar case). It is once more amenable to Lagrange inversion. In summary: Proposition II.5. The number of rooted non-plane trees, where all nodes have outdegree in , is X uω Tn() = (n − 1)![u n−1 ](φ(u))n where φ(u) = . ω! ω∈

In particular, when all node degrees are allowed, i.e., when ≡ Z≥0 , the number of trees is Tn = n n−1 and its EGF is the Cayley tree function satisfying T (z) = ze T (z) .

As in the unlabelled case (p. 66), we refer to a class of labelled trees defined by degree restrictions as a simple variety of trees: its EGF satisfies an equation of the form y = zφ(y).

II.17. Pr¨ufer’s bijective proofs of Cayley’s formula. The simplicity of Cayley’s formula calls for a combinatorial explanation. The most famous one is due to Pr¨ufer (in 1918). It establishes as follows a bijective correspondence between unrooted Cayley trees whose number is n n−2 for size n and sequences (a1 , . . . , an−2 ) with 1 ≤ a j ≤ n for each j. Given an unrooted tree τ , remove the endnode (and its incident edge) with the smallest label; let a1 denote the label of the node that was joined to the removed node. Continue with the pruned tree τ ′ to get a2 in a similar way. Repeat the construction of the sequence until the tree obtained only consists of a single edge. For instance: 3 1

4

2 8

7

5

−→

(4, 8, 4, 8, 8, 4).

6

It can be checked that the correspondence is bijective; see [67, p. 53] or [445, p. 5].

II.18. Forests. The number of unordered k–forests (i.e., k–sets of trees) is (k)

Fn

= n![z n ]

T (z)k (n − 1)! n−k u n n − 1 n−k = [u ](e ) = n , k! (k − 1)! k−1

as follows from B¨urmann’s form of Lagrange inversion, relative to powers (p. 66).

II.19. Labelled hierarchies. The class L of labelled hierarchies is formed of trees whose internal nodes are unlabelled and are constrained to have outdegree larger than 1, while their leaves have labels attached to them. As for other labelled structures, size is the number of labels (internal nodes do not contribute). Hierarchies satisfy the specification (compare with p. 72) L = Z + S ET≥2 (L),

H⇒

L = z + eL − 1 − L .

II. 5. LABELLED TREES, MAPPINGS, AND GRAPHS 13

4 12

23

7

22 15 24

21 14 16

20

6

10

5

17 19

26

9

1

129

8 11 3

25 2

18

Figure II.12. A functional graph of size n = 26 associated to the mapping ϕ such that ϕ(1) = 16, ϕ(2) = ϕ(3) = 11, ϕ(4) = 23, and so on. This happens to be solvable in terms of the Cayley function: L(z) = T ( 12 e z/2−1/2 ) + 2z − 1 2 . The first few values are 0, 1, 4, 26, 236, 2752 (EIS A000311): these numbers count phylogenetic trees, used to describe the evolution of a genetically-related group of organisms, and they correspond to Schr¨oder’s “fourth problem” [129, p. 224]. The asymptotic analysis is done in Example VII.12, p. 472. The class of binary (labelled) hierarchies defined by the additional fact that internal nodes can have degree 2 only is expressed by √ M = Z + S ET2 (M) H⇒ M(z) = 1 − 1 − 2z and Mn = 1 · 3 · · · (2n − 3), where the counting numbers are now, surprisingly perhaps, the odd factorials.

II. 5.2. Mappings and functional graphs. Let F be the class of mappings (or “functions”) from [1 . . n] to itself. A mapping f ∈ [1 . . n] 7→ [1 . . n] can be represented by a directed graph over the set of vertices [1 . . n] with an edge connecting x to f (x), for all x ∈ [1 . . n]. The graphs so obtained are called functional graphs and they have the characteristic property that the outdegree of each vertex is exactly equal to 1. Mappings and associated graphs. Given a mapping (or function) f , upon starting from any point x0 , the succession of (directed) edges in the graph traverses the vertices corresponding to iterated values of the mapping, x0 ,

f (x0 ),

f ( f (x0 )), . . . .

Since the domain is finite, each such sequence must eventually loop back on itself. When the operation is repeated, starting each time from an element not previously hit, the vertices group themselves into (weakly connected) components. This leads to a valuable characterization of functional graphs (Figure II.12): a functional graph is a set of connected functional graphs; a connected functional graph is a collection of rooted trees arranged in a cycle. (This decomposition is seen to extend the decomposition of permutations into cycles, p. 120.)

130

II. LABELLED STRUCTURES AND EGFS

Thus, with T being as before the class of all Cayley trees, and with K the class of all connected functional graphs, we have the specification: F(z) = e K (z) F = S ET (K) 1 K (z) = log H⇒ (48) K = C YC(T ) 1 − T (z) T (z) T = Z ⋆ S ET(T ) T (z) = ze . What is especially interesting here is a specification binding three types of related structures. From (48), the EGF F(z) is found to satisfy F = (1 − T )−1 . It can be checked from this, by Lagrange inversion once again (p. 733), that we have Fn = n n ,

(49)

as was to be expected (!) from the origin of the problem. More interestingly, Lagrange inversion also gives the number of connected functional graphs (expand log(1 − T )−1 and recover coefficients by B¨urmann’s form, p. 66): n − 1 (n − 1)(n − 2) + + ··· . n n2 The quantity Q(n) that appears in (50) is a famous one that surfaces in many problems of discrete mathematics (including the birthday paradox, Equation (27), p. 115). Knuth has proposed naming it “Ramanujan’s Q-function” as it already appears in the first letter of Ramanujan to Hardy in 1913. The asymptotic analysis is elementary and involves developing a continuous approximation of the general term and approximating the resulting Riemann sum by an integral: this is an instance of the Laplace method for sums briefly explained in Appendix B.6: Laplace’s method, p. 755 (see also [377, Sec. 1.2.11.3] and [538, Sec. 4.7]). In fact, very precise estimates come out naturally from an analysis of the singularities of the EGF K (z), as we shall see in Chapters VI (p. 416) and VII (p. 449). The net result is r π , Kn ∼ nn 2n √ so that a fraction about 1/ n of all the graphs consist of a single component. (50)

K n = n n−1 Q(n)

where

Q(n) := 1 +

Constrained mappings. As is customary with the symbolic method, basic constructions open the way to a large number of related counting results (Figure II.13). First, by an adaptation of (48), the mappings without fixed points, (∀x : f (x) 6= x) and those without 1, 2–cycles, (additionally, ∀x : f ( f (x)) 6= x), have EGFs, respectively, 2

e−T (z)−T (z)/2 . 1 − T (z)

e−T (z) , 1 − T (z)

The first term is consistent with what a direct count yields, namely (n − 1)n , which is asymptotic to e−1 n n , so that the fraction of mappings without fixed point is asymptotic to e−1 . The second one lends itself easily to complex asymptotic methods that give 2

e−T −T /2 n![z ] ∼ e−3/2 n n , 1−T n

II. 5. LABELLED TREES, MAPPINGS, AND GRAPHS

Mappings:

EGF 1 1−T

connected

log

no fixed-point

coefficient nn

1 1−T

∼ nn

e−T

1−T z

idempotent

e ze

partial

eT 1−T

131

r

(p. 130) π 2n

(pp. 130, 449)

∼ e−1 n n

(p. 130)

≈

(pp. 131, 571)

nn

(log n)n

∼ e nn

(p. 132)

Figure II.13. A summary of various counting results relative to mappings, with T ≡ T (z) the Cayley tree function. (Bijections, surjections, involutions, and injections are covered by previous constructions.)

and the proportion is asymptotic to e−3/2 . These two particular estimates are of the same form as that found for permutations (the generalized derangements, Equation (43)). Such facts are not quite obvious by elementary probabilistic arguments, but they are neatly explained by the singular theory of combinatorial schemas developed in Part B of this book. Next, idempotent mappings, i.e., ones satisfying f ( f (x)) = f (x) for all x, correspond to I ∼ = S ET(Z ⋆ S ET(Z)), so that n X n n−k z k . I (z) = e ze and In = k k=0

(The specification translates the fact that idempotent mappings can have only cycles of length 1 on which are grafted sets of direct antecedents.) The latter sequence is EIS A000248, which starts as 1,1,3,10,41,196,1057. An asymptotic estimate can be derived either from the Laplace method or, better, from the saddle-point method expounded in Chapter VIII (p. 571). Several analyses of this type are of relevance to cryptography and the study of random number generators. For √ instance, the fact that a random mapping over [1 . . n] tends to reach a cycle in O( n) steps (Subsection VII. 3.3, p. 462) led Pollard to design a surprising Monte Carlo integer factorization algorithm; see [378, p. 371] and [538, Sec 8.8], as well as our discussion in Example VII.11, p. 465. This algorithm, once suitably optimized, first led to the factorization of the Fermat number 8 F8 = 22 + 1 obtained by Brent in 1980.

II.20. Binary mappings. The class BF of binary mappings, where each point has either 0 or 2 preimages, is specified by BF = S ET(K), K = C YC(P), P = Z ⋆ B, B = Z ⋆ S ET0,2 (B) (planted trees P and binary trees B are needed), so that B F(z) = p

1 1 − 2z 2

,

B F2n =

((2n)!)2 . 2n (n!)2

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II. LABELLED STRUCTURES AND EGFS

The class BF is an approximate model of the behaviour of (modular) quadratic functions under iteration. See [18, 247] for a general enumerative theory of random mappings including degreerestricted ones.

II.21. Partial mappings. A partial mapping may be undefined at some points, and at those we consider it takes a special value, ⊥. The iterated preimages of ⊥ form a forest, while the remaining values organize themselves into a standard mapping. The class PF of partial mappings is thus specified by PF = S ET(T ) ⋆ F , so that

e T (z) and P Fn = (n + 1)n . 1 − T (z) This construction lends itself to all sorts of variations. For instance, the class P F I of injective partial maps is described as sets of chains of linear and circular graphs, P F I = S ET(C YC(Z)+ S EQ≥1 (Z)), so that 2 n X n 1 z/(1−z) e , P F In = i! . P F I (z) = 1−z i P F(z) =

i=0

(This is a symbolic rewriting of part of the paper [78]; see Example VIII.13, p. 596, for asymptotics.)

II. 5.3. Labelled graphs. Random graphs form a major chapter of the theory of random discrete structures [76, 355]. We examine here enumerative results concerning graphs of low “complexity”, that is, graphs which are very nearly trees. (Such graphs for instance play an essential rˆole in the analysis of early stages of the evolution of a random graph, when edges are successively added, as shown in [241, 354].) Unrooted trees and acyclic graphs. The simplest of all connected graphs are certainly the ones that are acyclic. These are trees, but contrary to the case of Cayley trees, no root is specified. Let U be the class of all unrooted trees. Since a rooted tree (rooted trees are, as we know, counted by Tn = n n−1 ) is an unrooted tree combined with a choice of a distinguished node (there are n such possible choices for trees of size n), one has Tn = nUn implying Un = n n−2 . At generating function level, this combinatorial equality translates into Z z dw , U (z) = T (w) w 0 which integrates to give (take T as the independent variable) 1 U (z) = T (z) − T (z)2 . 2 Since U (z) is the EGF of acyclic connected graphs, the quantity A(z) = eU (z) = e T (z)−T (z)

2 /2

is the EGF of all acyclic graphs. (Equivalently, these are unordered forests of unrooted trees; the sequence is EIS A001858: 1, 1, 2, 7, 38, 291, . . . ) Singularity analysis methods (Note VI.14, p. 406) imply the estimate An ∼ e1/2 n n−2 . Surprisingly, perhaps, there are barely more acyclic graphs than unrooted trees—such phenomena are easily explained by singularity analysis.

II. 5. LABELLED TREES, MAPPINGS, AND GRAPHS

133

Unicyclic graphs. The excess of a graph is defined as the difference between the number of edges and the number of vertices. For a connected graph, this quantity must be at least −1, this minimal value being precisely attained by unrooted trees. The class Wk is the class of connected graphs of excess equal to k; in particular U = W−1 . The successive classes W−1 , W0 , W1 , . . ., may be viewed as describing connected graphs of increasing complexity. The class W0 comprises all connected graphs with the number of edges equal to the number of vertices. Equivalently, a graph in W0 is a connected graph with exactly one cycle (a sort of “eye”), and for that reason, elements of W0 are sometimes referred to as “unicyclic components” or “unicycles”. In a way, such a graph looks very much like an undirected version of a connected functional graph. In precise terms, a graph of W0 consists of a cycle of length at least 3 (by definition, graphs have neither loops nor multiple edges) that is undirected (the orientation present in the usual cycle construction is killed by identifying cycles isomorphic up to reflection) and on which are grafted trees (these are implicitly rooted by the point at which they are attached to the cycle). With UC YC representing the (new) undirected cycle construction, one thus has W0 ∼ = UC YC≥3 (T ). We claim that this construction is reflected by the EGF equation

1 1 1 1 log − T (z) − T (z)2 . 2 1 − T (z) 2 4 Indeed one has the isomorphism W0 + W0 ∼ = C YC≥3 (T ), (51)

W0 (z) =

since we may regard the two disjoint copies on the left as instantiating two possible orientations of the undirected cycle. The result of (51) then follows from the usual translation of the cycle construction—it is originally due to the Hungarian probabilist R´enyi in 1959. Asymptotically, one finds (using methods of Chapter VI, p. 406): 1√ 2π n n−1/2 . (52) n![z n ]W0 ∼ 4 (The sequence starts as 0, 0, 1, 15, 222, 3660, 68295 and is EIS A057500.) Finally, the number of graphs made only of trees and unicyclic components has EGF 2 e T /2−3T /4 , e W−1 (z)+W0 (z) = √ 1−T which asymptotically yields n![z n ]e W−1 +W0 ∼ Ŵ(3/4)(2e)−1/4 π −1/2 n n−1/4 . Such graphs stand just next to acyclic graphs in order of structural complexity. They are the undirected counterparts of functional graphs encountered in the previous subsection.

II.22. 2–Regular graphs. This is based on Comtet’s account [129, Sec. 7.3]. A 2-regular graph is an undirected graph in which each vertex has degree exactly 2. Connected 2–regular graphs are thus undirected cycles of length n ≥ 3, so that their class R satisfies 2

(53)

R = S ET(UC YC≥3 (Z))

H⇒

e−z/2−z /4 R(z) = √ . 1−z

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II. LABELLED STRUCTURES AND EGFS

EGF

coefficient 2n(n−1)/2

Graphs: acyclic, connected acyclic (forest) unicycle

U ≡ W−1 = T − T 2 /2 A=e W0 =

T2

1 1 T log − − 2 1−T 2 4 2

e T /2−3T /4 √ 1−T Pk (T ) Wk = (1 − T )3k

set of trees & unicycles B = connected, excess k

n n−2

T −T 2 /2

∼ e1/2 n n−2 √ ∼ 14 2πn n−1/2 (2e)−1/4 n−1/4 n √ π √ Pk (1) 2π n+(3k−1)/2 ∼ 3k/2 n 2 Ŵ(3k/2)

∼ Ŵ(3/4)

Figure II.14. A summary of major enumeration results relative to labelled graphs. The asymptotic estimates result from singularity analysis (Note VI.14, p. 406).

Given n straight lines in general position in the plane, a cloud is defined to be a set of n intersection points, no three being collinear. Clouds and 2–regular graphs are equinumerous. [Hint: Use duality.] The asymptotic analysis will serve as a prime example of the singularity analysis process (Examples VI.1, p. 379 and VI.2, p. 395). The general enumeration of r –regular graphs becomes somewhat more difficult as soon as r > 2. Algebraic aspects are discussed in [289, 303] while Bender and Canfield [39] have determined the asymptotic formula (for r n even) √ (r 2 −1)/4 r r/2 r n/2 n , 2e er/2 r ! for the number of r –regular graphs of size n. (See also Example VIII.9, p. 583, for regular multigraphs.) (54)

(r )

Rn ∼

Graphs of fixed excess. The previous discussion suggests considering more generally the enumeration of connected graphs according to excess. E. M. Wright made important contributions in this area [620, 621, 622] that are revisited in the famous “giant paper on the giant component” by Janson, Knuth, Łuczak, and Pittel [354]. Wright’s result are summarized by the following proposition. Proposition II.6. The EGF Wk (z) of connected graphs with excess (of edges over vertices) equal to k is, for k ≥ 1, of the form (55)

Wk (z) =

Pk (T ) , (1 − T )3k

T ≡ T (z),

where Pk is a polynomial of degree 3k + 2. For any fixed k, as n → ∞, one has √ Pk (1) 2π n+(3k−1)/2 n (56) Wk,n = n![z ]Wk (z) = 3k/2 1 + O(n −1/2 ) . n 2 Ŵ (3k/2)

The combinatorial part of the proof (see Note II.23 below) is an interesting exercise in graph surgery and symbolic methods. The analytic part of the statement follows straightforwardly from singularity analysis. The polynomials P(T ) and the

II. 5. LABELLED TREES, MAPPINGS, AND GRAPHS

135

constants Pk (1) are determined by an explicit nonlinear recurrence; one finds for instance: W1 =

1 T 4 (6 − T ) , 24 (1 − T )3

W2 =

1 T 4 (2 + 28T − 23T 2 + 9T 3 − T 4 ) . 48 (1 − T )6

II.23. Wright’s surgery. The full proof of Proposition II.6 by symbolic methods requires the notion of pointing in conjunction with multivariate generating function techniques of Chapter III. It is convenient to define wk (z, y) := y k Wk (zy), which is a bivariate generating function with y marking the number of edges. Pick up an edge in a connected graph of excess k + 1, then remove it. This results either in a connected graph of excess k with two pointed vertices (and no edge in between) or in two connected components of respective excess h and k − h, each with a pointed vertex. Graphically (with connected components in grey): 11111111111 00000000000 00000000000 11111111111 00000000000 11111111111 00000000000 11111111111 00000000000 11111111111 00000000000 11111111111 00000000000 11111111111 00000000000 11111111111 00000000000 11111111111 00000000000 11111111111 00000000000 11111111111

+

=

This translates into the differential recurrence on the wk (∂x := ∂∂x ), k+1 X 2∂ y wk+1 = z 2 ∂z2 wk − 2y∂ y wk + (z∂z wh ) · z∂z wk−h , h=−1

and similarly for Wk (z) = wk (z, 1). From here, it can be verified by induction that each Wk is a rational function of T ≡ W−1 . (See Wright’s original papers [620, 621, 622] or [354] for details; constants related to the Pk (1) occur in Subsection VII. 10.1, p. 532.)

As explained in the giant paper [354], such results combined with complex analytic techniques provide, with great detail, information about a random graph Ŵ(n, m) with n nodes and m edges. In the sparse case where m is of the order of n, one finds the following properties to hold “with high probability” (w.h.p.)7; that is, with probability tending to 1 as n → ∞ .

• For m = µn, with µ < 21 , the random graph Ŵ(m, n) has w.h.p. only tree and unicycle components; the largest component is w.h.p. of size O(log n). • For m = 12 n + O(n 2/3 ), w.h.p. there appear one or several semi-giant components that have size O(n 2/3 ). • For m = µn, with µ > 21 , there is w.h.p. a unique giant component of size proportional to n.

In each case, refined estimates follow from a detailed analysis of corresponding generating functions, which is a main theme of [241] and especially [354]. Raw forms of these results were first obtained by Erd˝os and R´enyi who launched the subject in a famous series of papers dating from 1959–60; see the books [76, 355] for a probabilistic context and the paper [40] for the finest counting estimates available. In contrast, the enumeration of all connected graphs (irrespective of the number of edges, that is, without excess being taken into account) is a relatively easy problem treated in the 7 Synonymous expressions are “asymptotically almost surely” (a.a.s) and “in probability”. The term “almost surely” is sometimes used, though it lends itself to confusion with properties of continuous measures.

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II. LABELLED STRUCTURES AND EGFS

next section. Many other classical aspects of the enumerative theory of graphs are covered in the book Graphical Enumeration by Harary and Palmer [319].

II.24. Graphs are not specifiable. The class of all graphs does not admit a specification that starts from single atoms and involves only sums, products, sets and cycles. Indeed, the growth of G n is such that the EGF G(z) has radius of convergence 0, whereas EGFs of constructible classes must have a non-zero radius of convergence. (Section IV. 4, p. 249, provides a detailed proof of this fact for iterative structures; for recursively specified classes, this is a consequence of the analysis of inverse functions, p. 402, and systems, p. 489, with suitable adaptations based on the technique of majorant series. p. 250.) II. 6. Additional constructions As in the unlabelled case, pointing and substitution are available in the world of labelled structures (Subsection II. 6.1), and implicit definitions enlarge the scope of the symbolic method (Subsection II. 6.2). The inversion process needed to enumerate implicit structures is even simpler, since in the labelled universe sets and cycles have more concise translations as operators over EGF. Finally, and this departs significantly from Chapter I, the fact that integer labels are naturally ordered makes it possible to take into account certain order properties of combinatorial structures (Subsection II. 6.3). II. 6.1. Pointing and substitution. The pointing operation is entirely similar to its unlabelled counterpart since it consists in distinguishing one atom among all the ones that compose an object of size n. The definition of composition for labelled structures is however a bit more subtle as it requires singling out “leaders” in components. Pointing. The pointing of a class B is defined by A = 2B

iff

An = [1 . . n] × Bn .

In other words, in order to generate an element of A, select one of the n labels and point at it. Clearly d An = n · Bn H⇒ A(z) = z B(z). dz Substitution (composition). Composition or substitution can be introduced so that it corresponds a priori to composition of generating functions. It is formally defined as ∞ X Bk × S ETk (C), B◦C = k=0

so that its EGF is

∞ X k=0

Bk

(C(z))k = B(C(z)). k!

A combinatorial way of realizing this definition and forming an arbitrary object of B ◦ C, is as follows. First select an element of β ∈ B called the “base” and let k = |β| be its size; then pick up a k–set of elements of C; the elements of the k–set are naturally ordered by the value of their “leader” (the leader of an object being by convention the value of its smallest label); the element with leader of rank r is then substituted to the node labelled by value r of β. Gathering the above, we obtain:

II. 6. ADDITIONAL CONSTRUCTIONS

137

Theorem II.3. The combinatorial constructions of pointing and substitution are admissible d A = 2B H⇒ A(z) = z∂z B(z), ∂z ≡ dz A = B ◦ C H⇒ A(z) = B(C(z)). For instance, the EGF of (relabelled) pairings of elements drawn from C is eC(z)

2 /2

,

since the EGF of involutions without fixed points is e z

2 /2

.

II.25. Standard constructions based on substitutions. The sequence class of A may be defined by composition as P ◦ A where P is the set of all permutations. The set class of A may be defined as U ◦ A where U is the class of all urns. Similarly, cycles are obtained by substitution into circular graphs. Thus, ∼ U ◦ A, ∼ P ◦ A, C YC(A) ∼ S ET(A) = S EQ(A) = = C ◦ A.

In this way, permutation, urns and circle graphs appear as archetypal classes in a development of combinatorial analysis based on composition. (Joyal’s “theory of species” [359] and the book by Bergeron, Labelle, and Leroux [50] show that a far-reaching theory of combinatorial enumeration can be based on the concept of substitution.)

II.26. Distinct component sizes. The EGFs of permutations with cycles of distinct lengths and of set partitions with parts of distinct sizes are ∞ ∞ Y Y zn zn , . 1+ 1+ n n! n=1

n=1

The probability that a permutation of Pn has distinct cycle sizes tends to e−γ ; see [309, Sec. 4.1.6] for a Tauberian argument and [495] for precise asymptotics. The corresponding analysis for set partitions is treated in the seven-author paper [368].

II. 6.2. Implicit structures. Let X be a labelled class implicitly characterized by either of the combinatorial equations A = B + X,

A = B ⋆ X.

Then, solving the corresponding EGF equations leads to X (z) = A(z) − B(z),

X (z) =

A(z) , B(z)

respectively. For the composite labelled constructions S EQ, S ET, C YC, the algebra is equally easy. Theorem II.4 (Implicit specifications). The generating functions associated with the implicit equations in X A = S EQ(X ),

A = S ET(X ),

A = C YC(X ),

are, respectively, X (z) = 1 −

1 , A(z)

X (z) = log A(z),

X (z) = 1 − e−A(z) .

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II. LABELLED STRUCTURES AND EGFS

Example II.15. Connected graphs. In the context of graphical enumerations, the labelled set construction takes the form of an enumerative formula relating a class G of graphs and the subclass K ⊂ G of its connected graphs: G = S ET(K)

H⇒

G(z) = e K (z) .

This basic formula is known in graph theory [319] as the exponential formula. Consider the class G of all (undirected) labelled graphs, the size of a graph being the number of its nodes. Since a graph is determined by the choice of its set of edges, there are n2 n potential edges each of which may be taken in or out, so that G = 2(2) . Let K ⊂ G be the n

subclass of all connected graphs. The exponential formula determines K (z) implicitly: X n zn K (z) = log 1 + 2(2) n! n≥1 (57) 3 2 z z4 z5 z + 4 + 38 + 728 + · · · , = z+ 2! 3! 4! 5! where the sequence is EIS A001187. The series is divergent, that is, it has radius of convergence 0. It can nonetheless be manipulated as a formal series (Appendix A.5: Formal power series, p. 730). Expanding by means of log(1 + u) = u − u 2 /2 + · · · , yields a complicated convolution expression for K n : n3 n2 n1 n1 n n 1X 1X n n +( 22 ) ( ) ( ) 2 2 Kn = 2 − 2 + 2( 2 )+( 2 )+( 2 ) − · · · . 2 3 n1, n2 n1, n2, n3

(The kth term is a sum over n 1 + · · · + n k = n, with 0 < n j < n.) Given the very fast increase of G n with n, for instance n+1 n 2( 2 ) = 2n 2(2) ,

a detailed analysis of the various terms of the expression of K n shows predominance of the first sum, and, in that sum itself, the extreme terms corresponding to n 1 = n − 1 or n 2 = n − 1 predominate, so that n (58) K n = 2(2) 1 − 2n2−n + o(2−n ) . Thus: almost all labelled graphs of size n are connected. In addition, the error term decreases very quickly: for instance, for n = 18, an exact computation based on the generating function formula reveals that a proportion only 0.0001373291074 of all the graphs are not connected— this is extremely close to the value 0.0001373291016 predicted by the main terms in the asymptotic formula (58). Notice that good use could be made here of a purely divergent generating function for asymptotic enumeration purposes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

II.27. Bipartite graphs. A plane bipartite graph is a pair (G, ω) where G is a labelled graph, ω = (ωW , ω E ) is a bipartition of the nodes (into West and East categories), and the edges are such that they only connect nodes from ωW to nodes of ω E . A direct count shows that the EGF of plane bipartite graphs is X zn X n γn Ŵ(z) = 2k(n−k) . with γn = k n! n k

The EGF of plane bipartite graphs that are connected is log Ŵ(z). A bipartite graph is a labelled graph whose nodes can be partitioned into two groups so that edges only connect nodes of different groups. The EGF of bipartite graphs is p 1 log Ŵ(z) = Ŵ(z). exp 2

II. 6. ADDITIONAL CONSTRUCTIONS

139

[Hint. The EGF of a connected bipartite graph is 21 log Ŵ(z), since a factor of 21 kills the East– West orientation present in a connected plane bipartite graph. See Wilf’s book [608, p. 78] for details.]

II.28. Do two permutations generate the symmetric group? To two permutations σ, τ of the same size, associate a graph Ŵσ,τ whose set vertices is V = [1 . . n], if n = |σ | = |τ |, and set of edges is formed of all the pairs (x, σ (x)), (x, τ (x)), for x ∈ V . The probability that a random Ŵσ,τ is connected is X 1 n n n!z . πn = [z ] log n! n≥0

This represents the probability that two permutations generate a transitive group (that is for all x, y ∈ [0 . . n], there exists a composition of σ, σ −1 , τ, τ −1 that maps x to y). One has 4 23 171 1542 1 1 (59) πn ∼ 1 − − 2 − 3 − 4 − 5 − 6 − · · · , n n n n n n Surprisingly, the coefficients 1, 1, 4, 23, . . . (EIS A084357) in the asymptotic formula (59) enumerate a “third-level” structure (Subsection II. 4.2, p. 124 and Note VIII.15, p. 571), namely: S ET(S ET≥1 (S EQ≥1 (Z))). In addition, one has n!2 πn = (n − 1)!In , where In+1 is the number of indecomposable permutations (Example I.19, p. 89). Let πn⋆ be the probability that two random permutations generate the whole symmetric group. Then, by a result of Babai based on the classification of groups, the quantity πn − πn⋆ is exponentially small, so that (59) also applies to πn⋆ ; see Dixon [167].

II. 6.3. Order constraints. A construction well-suited to dealing with many of the order properties of combinatorial structures is the modified labelled product: A = (B 2 ⋆ C). This denotes the subset of the product B⋆C formed with elements such that the smallest label is constrained to lie in the B component. (To make this definition consistent, it must be assumed that B0 = 0.) We call this binary operation on structures the boxed product. Theorem II.5. The boxed product is admissible: Z z d 2 (60) A = (B ⋆ C) H⇒ A(z) = ∂t ≡ . (∂t B(t)) · C(t) dt, dt 0 Proof. The definition of boxed products implies the coefficient relation n X n−1 Bk Cn−k . An = k−1 k=1

The binomial coefficient that appears in the standard convolution, Equation (2), p. 100, is to be modified since only n −1 labels need to be distributed between the two components: k − 1 go to the B component (that is already constrained to contain the label 1) and n − k to the C component. From the equivalent form n 1X n An = (k Bk ) Cn−k , k n k=0

the result follows by taking EGFs, via A(z) = (∂z B(z)) · C(z).

140

II. LABELLED STRUCTURES AND EGFS

2.5

2

1.5

1

0.5 0

20

40

60

80

100

Figure II.15. A numerical sequence of size 100 with records marked by circles: there are 7 records that occur at times 1, 3, 5, 11, 60, 86, 88.

A useful special case is the min-rooting operation, A = Z2 ⋆ C ,

for which a variant definition goes as follows: take in all possible ways elements γ ∈ C, prepend an atom with a label, for instance 0, smaller than the labels of γ , and relabel in the canonical way over [1 . . (n +1)] by shifting all label values by 1. Clearly An+1 = Cn , which yields Z z

A(z) =

C(t) dt,

0

a result which is also consistent with the general formula (60) of boxed products. For some applications, it is convenient to impose constraints on the maximal label rather than the minimum. The max-boxed product written A = (B ⋆ C),

is then defined by the fact the maximum is constrained to lie in the B–component of the labelled product. Naturally, translation by an integral in (60) remains valid for this trivially modified boxed product.

II.29. Combinatorics of integration. In the perspective of this book, integration by parts has an immediate interpretation. Indeed, the equality Z z Z z A′ (t) · B(t) dt + A(t) · B ′ (t) dt = A(z) · B(z) 0

0

reads: “The smallest label in an ordered pair appears either on the left or on the right.”

Example II.16. Records in permutations. Given a sequence of numbers x = (x1 , . . . , xn ), assumed all distinct, a record is defined to be an element x j such that xk < x j for all k < j. (A record is an element “better” than its predecessors!) Figure II.15 displays a numerical sequence of length n = 100 that has 7 records. Confronted by such data, a statistician will typically want to determine whether the data obey purely random fluctuations or if there could be some indications of a “trend” or of a “bias” [139, Ch. 10]. (Think of the data as reflecting share prices or athletic records, say.) In particular, if the x j are independently drawn from a continuous distribution, then the number of records obeys the same laws as in a random permutation of

II. 6. ADDITIONAL CONSTRUCTIONS

141

[1 . . n]. This statistical preamble then invites the question: How many permutations of n have k records? First, we start with a special brand of permutations, the ones that have their maximum at the beginning. Such permutations are defined as (“” indicates the boxed product based on the maximum label) Q = (Z ⋆ P), where P is the class of all permutations. Observe that this gives the EGF Z z d 1 1 Q(z) = t · dt = log , dt 1−t 1−z 0 implying the obvious result Q n = (n − 1)! for all n ≥ 1. These are exactly the permutations with one record. Next, consider the class P (k) = S ETk (Q). The elements of P (k) are unordered sets of cardinality k with elements of type Q. Define the max–leader (“el lider m´aximo”) of any component of P (k) as the value of its maximal element. Then, if we place the components in sequence, ordered by increasing values of their leaders, then read off the whole sequence, we obtain a permutation with exactly k records. The correspondence8 is clearly revertible. Here is an illustration, with leaders underlined:

(7, 2, 6, 1), (4, 3), (9, 8, 5)

∼ =

∼ =

(4, 3), (7, 2, 6, 1), (9, 8, 5) ]

4, 3, 7, 2, 6, 1, 9, 8, 5.

Thus, the number of permutations with k records is determined by 1 P (k) (z) =

k!

log

k 1 , 1−z

n (k) , Pn = k

where we recognize Stirling cycle numbers from Example II.12, p. 121. In other words: The number of permutations of size n having k records is counted by the Stirling “cycle” number nk .

Returning to our statistical problem, the treatment of Example II.12 p. 121 (to be revisited in Chapter III, p. 189) shows that the expected number of records in a random permutation of . size n equals Hn , the harmonic number. One has H100 = 5.18, so that for 100 data items, a little more than 5 records are expected on average. The probability of observing 7 records or more is still about 23%, an altogether not especially rare event. In contrast, observing twice as many records as we did, namely 14, would be a fairly strong indication of a bias—on random data, the event has probability very close to 10−4 . Altogether, the present discussion is consistent with the hypothesis for the data of Figure II.15 to have been generated independently at random (and indeed they were). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

8This correspondence can also be viewed as a transformation on permutations that maps the number

of records to the number of cycles—it is known as Foata’s fundamental correspondence [413, Sec. 10.2].

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II. LABELLED STRUCTURES AND EGFS

It is possible to base a fair part of the theory of labelled constructions on sums and products in conjunction with the boxed product. In effect, consider the three relations 1 , 1 − g(z)

F = S EQ(G)

H⇒

f (z) =

F = S ET(G)

H⇒

f (z) = e g(z) ,

F = C YC(G) H⇒

f (z) = log

1 , 1 − g(z)

f = 1 + gf Z f = 1 + g′ f Z 1 f = g′ . 1−g

The last column is easily checked, by standard calculus, to provide an alternative form of the standard operator corresponding to sequences, sets, and cycles. Each case can in fact be deduced directly from Theorem II.5 and the labelled product rule as follows. (i) Sequences: they obey the recursive definition F = S EQ(G)

H⇒

F∼ = {ǫ) + (G ⋆ F).

(ii) Sets: we have F = S ET(G)

H⇒

F∼ = {ǫ} + (G ⋆ F),

which means that, in a set, one can always single out the component with the largest label, the rest of the components forming a set. In other words, when this construction is repeated, the elements of a set can be canonically arranged according to increasing values of their largest labels, the “leaders”. (We recognize here a generalization of the construction used for records in permutations.) (iii) Cycles: The element of a cycle that contains the largest label can be taken canonically as the cycle “starter”, which is then followed by an arbitrary sequence of elements upon traversing the cycle in cyclic order. Thus F = C YC(G)

H⇒

F∼ = (G ⋆ S EQ(G)).

Greene [308] has developed a complete framework of labelled grammars based on standard and boxed labelled products. In its basic form, its expressive power is essentially equivalent to ours, because of the above relations. More complicated order constraints, dealing simultaneously with a collection of larger and smaller elements, can be furthermore taken into account within this framework.

II.30. Higher order constraints, after Greene. Let the symbols , ⊡, represent smallest, d ) second smallest, and largest labels, respectively. One has the correspondences (with ∂z = dz A = B2 ⋆ C ∂z2 A(z) = (∂z B(z)) · (∂z C(z)) A = B2 ⋆ C ∂z2 A(z) = ∂z2 B(z) · C(z) A = B2 ⋆ C ⊡ ⋆ D ∂ 3 A(z) = (∂ B(z)) · (∂ C(z)) · (∂ D(z)) , z

z

z

z

and so on. These can be transformed into (iterated) integral representations. (See [308] for more.)

The next three examples demonstrate the utility of min/max-rooting used in conjunction with recursion. Examples II.17 and II.18 introduce two important classes of

II. 6. ADDITIONAL CONSTRUCTIONS

143

1 3

5

2

6

4

7

5

7

3

4

1

6

2

Figure II.16. A permutation of size 7 and its increasing binary tree lifting.

trees that are tightly linked to permutations. Example II.19 provides a simple symbolic solution to a famous parking problem, on which many analyses can be built. Example II.17. Increasing binary trees and alternating permutations. To each permutation, one can associate bijectively a binary tree of a special type called an increasing binary tree and sometimes a heap-ordered tree or a tournament tree. This is a plane rooted binary tree in which internal nodes bear labels in the usual way, but with the additional constraint that node labels increase along any branch stemming from the root. Such trees are closely related to many classical data structures of computer science, such as heaps and binomial queues. The correspondence (Figure II.16) is as follows: Given a permutation written as a word, σ = σ1 σ2 . . . σn , factor it into the form σ = σ L · min(σ ) · σ R , with min(σ ) the smallest label value in the permutation, and σ L , σ R the factors left and right of min(σ ). Then the binary tree β(σ ) is defined recursively in the format hroot, left, righti by β(σ ) = hmin(σ ), β(σ L ), β(σ R )i,

β(ǫ) = ǫ.

The empty tree (consisting of a unique external node of size 0) goes with the empty permutation ǫ. Conversely, reading the labels of the tree in symmetric (infix) order gives back the original permutation. (The correspondence is described for instance in Stanley’s book [552, p. 23–25] who says that “it has been primarily developed by the French”, pointing at [267].) Thus, the family I of binary increasing trees satisfies the recursive definition (61) I = {ǫ} + Z 2 ⋆ I ⋆ I , which implies the nonlinear integral equation for the EGF Z z I (z) = 1 + I (t)2 dt.

0 ′ 2 This equation reduces to I (z) = I (z) and, under the initial condition I (0) = 1, it admits the solution I (z) = (1 − z)−1 . Thus In = n!, which is consistent with the fact that there are as

many increasing binary trees as there are permutations. The construction of increasing trees is instrumental in deriving EGFs relative to various local order patterns in permutations. We illustrate its use here by counting the number of up-and-down (or zig-zag) permutations, also known as alternating permutations. The result,

144

II. LABELLED STRUCTURES AND EGFS

already mentioned in our Invitation chapter (p. 2) was first derived by D´esir´e Andr´e in 1881 by means of a direct recurrence argument. A permutation σ = σ1 σ2 · · · σn is an alternating permutation if σ1 > σ2 < σ3 > σ4 < · · · ,

(62)

so that pairs of consecutive elements form a succession of ups and downs; for instance, 7

6

2 6

2

4

3

5

1 3

1

7

4

5

Consider first the case of an alternating permutation of odd size. It can be checked that the corresponding increasing trees have no one-way branching nodes, so that they consist solely of binary nodes and leaves. Thus, the corresponding specification is J = Z + Z2 ⋆ J ⋆ J , so that

Z z

d J (z) = 1 + J (z)2 . dz The equation admits separation of variables, which implies, since J (0) = 0, that arctan(J (z)) = z, hence: z5 z7 z3 J (z) = tan(z) = z + 2 + 16 + 272 + · · · . 3! 5! 7! The coefficients J2n+1 are known as the tangent numbers or the Euler numbers of odd index (EIS A000182). Alternating permutations of even size defined by the constraint (62) and denoted by K can be determined from K = {ǫ} + Z 2 ⋆ J ⋆ K , J (z) = z +

J (t)2 dt

and

0

since now all internal nodes of the tree representation are binary, except for the right-most one that only branches on the left. Thus, K ′ (z) = tan(z)K (z), and the EGF is K (z) =

1 z2 z4 z6 z8 = 1 + 1 + 5 + 61 + 1385 + · · · , cos(z) 2! 4! 6! 8!

where the coefficients K 2n are the secant numbers also known as Euler numbers of even index (EIS A000364). Use will be made later in this book (Chapter III, p. 202) of this important tree representation of permutations as it opens access to parameters such as the number of descents, runs, and (once more!) records in permutations. Analyses of increasing trees also inform us of crucial performance issues regarding binary search trees, quicksort, and heap-like priority queue structures [429, 538, 598, 600]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . z , tan tan z, tan(e z − 1) as EGFs of II.31. Combinatorics of trigonometrics. Interpret tan 1−z

combinatorial classes.

II. 6. ADDITIONAL CONSTRUCTIONS

145

8

1 x 5

2

6

3

5

x x

4 8

4

x

3

x 7

2

6 x

x

1

x

−

x

9

7

1

2

3

4

5

6

7 8

9

Figure II.17. An increasing Cayley tree (left) and its associated regressive mapping (right).

Example II.18. Increasing Cayley trees and regressive mappings. An increasing Cayley tree is a Cayley tree (i.e., it is labelled, non-plane, and rooted) whose labels along any branch stemming from the root form an increasing sequence. In particular, the minimum must occur at the root, and no plane embedding is implied. Let L be the class of such trees. The recursive specification is now L = Z 2 ⋆ S ET(L) .

The generating function thus satisfies the functional relations Z z L(z) = e L(t) dt, L ′ (z) = e L(z) ,

0 ′ −L with L(0) = 0. Integration of L e = 1 shows that e−L = 1 − z, hence

1 and L n = (n − 1)!. 1−z Thus the number of increasing Cayley trees is (n−1)!, which is also the number of permutations of size n − 1. These trees have been studied by Meir and Moon [435] under the name of “recursive trees”, a terminology that we do not, however, retain here. The simplicity of the formula L n = (n − 1)! certainly calls for a combinatorial interpretation. In fact, an increasing Cayley tree is fully determined by its child–parent relationship (Figure II.17). In other words, to each increasing Cayley tree τ , we associate a partial map φ = φτ such that φ(i) = j iff the label of the parent of i is j. Since the root of tree is an orphan, the value of φ(1) is undefined, φ(1) =⊥; since the tree is increasing, one has φ(i) < i for all i ≥ 2. A function satisfying these last two conditions is called a regressive mapping. The correspondence between trees and regressive mappings is then easily seen to be bijective. Thus regressive mappings on the domain [1 . . n] and increasing Cayley trees are equinumerous, so that we may as well use L to denote the class of regressive mappings. Now, a regressive mapping of size n is evidently determined by a single choice for φ(2) (since φ(2) = 1), two possible choices for φ(3) (either of 1, 2), and so on. Hence the formula L(z) = log

L n = 1 × 2 × 3 × · · · × (n − 1) receives a natural interpretation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

II.32. Regressive mappings and permutations. Regressive mappings can be related directly to permutations. The construction that associates a regressive mapping to a permutation is

146

II. LABELLED STRUCTURES AND EGFS

called the “inversion table” construction; see [378, 538]. Given a permutation σ = σ1 , . . . , σn , associate to it a function ψ = ψσ from [1 . . n] to [0 . . n − 1] by the rule ψ( j) = card k < j σk > σ j . The function ψ is a trivial variant of a regressive mapping.

II.33. Rotations and increasing trees. An increasing Cayley tree can be canonically drawn by ordering descendants of each node from left to right according to their label values. The rotation correspondence (p. 73) then gives rise to a binary increasing tree. Hence, increasing Cayley trees and increasing binary trees are also directly related. Summarizing this note and the previous one, we have a quadruple combinatorial connection, ∼ Regressive mappings = ∼ Permutations ∼ Increasing Cayley trees = = Increasing binary trees, which opens the way to yet more permutation enumerations. Example II.19. A parking problem. Here is Knuth’s introduction to the problem, dating back from 1973 (see [378, p. 545]), which nowadays might be regarded by some as politically incorrect: “A certain one-way street has m parking spaces in a row numbered 1 to m. A man and his dozing wife drive by, and suddenly, she wakes up and orders him to park immediately. He dutifully parks at the first available space [. . . ].”

Consider n = m − 1 cars and condition by the fact that everybody eventually finds a parking space and the last space remains empty. There are m n = (n + 1)n possible sequences of “wishes”, among which only a certain number Fn satisfy the condition—this number is to be determined. (An important motivation for this problem is the analysis of hashing algorithms examined in Note III.11, p. 178, under the “linear probing” strategy.) A sequence satisfying the condition called an almost-full allocation, its size n being the number of cars involved. Let F represent the class of almost-full allocations. We claim the decomposition: h i (63) F = (2F + F ) ⋆ Z ⋆ F .

Indeed, consider the car that arrived last, before it will eventually land in some position k + 1 from the left. Then, there are two islands, which are themselves almost-full allocations (of respective sizes k and n − k − 1). This last car’s intended parking wish must have been either one of the first k occupied cells on the left (the factor 2F in (63)) or the last empty cell of the first island (the term F in the left factor); the right island is not affected (the factor F on the right). Finally, the last car is inserted into the street (the factor Z ). Pictorially, we have a sort of binary tree decomposition of almost-full allocations:

Analytically, the translation of (63) into EGF is Z z (64) F(z) = (w F ′ (w) + F(w))F(w) dw, 0

which, through differentiation gives (65)

F ′ (z) = (z F(z))′ · F(z).

II. 7. PERSPECTIVE

147

Simple manipulations do the rest: we have F ′ /F = (z F)′ , which by integration gives log F = (z F) and F = e z F . Thus F(z) satisfies a functional equation strangely similar to that of the Cayley tree function T (z); indeed, it is not hard to see that one has 1 (66) F(z) = T (z) and Fn = (n + 1)n−1 , z which solves the original counting problem. The derivation above is based on articles by Flajolet, Poblete, Viola, and Knuth [249, 380], who show that probabilistic properties of parking allocations can be precisely analysed (for instance, total displacement, examined in Note VII.54, p. 534, is found to be governed by an Airy distribution). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

II. 7. Perspective Together with the previous chapter and Figure I.18, this chapter and Figure II.18 provide the basis for the symbolic method that is at the core of analytic combinatorics. The translations of the basic constructions for labelled classes to EGFs could hardly be simpler, but, as we have seen, they are sufficiently powerful to embrace numerous classical results in combinatorics, ranging from the birthday and coupon collector problems to tree and graph enumeration. The examples that we have considered for second-level structures, trees, mappings, and graphs lead to EGFs that are simple to express and natural to generalize. (Often, the simple form is misleading—direct derivations of many of these EGFs that do not appeal to the symbolic method can be rather intricate.) Indeed, the symbolic method provides a framework that allows us to understand the nature of many of these combinatorial classes. From here, numerous seemingly scattered counting problems can be organized into broad structural categories and solved in an almost mechanical manner. Again, the symbolic method is only half of the story (the “combinatorics” in analytic combinatorics), leading to EGFs for the counting sequences of numerous interesting combinatorial classes. While some of these EGFs lead immediately to explicit counting results, others require classical techniques in complex analysis and asymptotic analysis that are covered in Part B (the “analytic” part of analytic combinatorics) to deliver asymptotic estimates. Together with these techniques, the basic constructions, translations, and applications that we have discussed in this chapter reinforce the overall message that the symbolic method is a systematic approach that is successful for addressing classical and new problems in combinatorics, generalizations, and applications. We have been focusing on enumeration problems—counting the number of objects of a given size in a combinatorial class. In the next chapter, we shall consider how to extend the symbolic method to help analyse other properties of combinatorial classes. Bibliographic notes. The labelled set construction and the exponential formula were recognized early by researchers working in the area of graphical enumerations [319]. Foata [265] proposed a detailed formalization in 1974 of labelled constructions, especially sequences and sets, under the names of partitional complex; a brief account is also given by Stanley in his survey [550]. This is parallel to the concept of “prefab” due to Bender and Goldman [42]. The

148

II. LABELLED STRUCTURES AND EGFS

1. The main constructions of union, and product, sequence, set, and cycle for labelled structures together with their translation into exponential generating functions. Construction

EGF

Union

A=B+C

A(z) = B(z) + C(z)

Product

A=B⋆C

Sequence

A = S EQ(B)

Set

A = S ET(B)

Cycle

A = C YC(B)

A(z) = B(z) · C(z) 1 A(z) = 1 − B(z) A(z) = exp(B(z)) 1 A(z) = log 1 − B(z)

2. Sets, multisets, and cycles of fixed cardinality. Construction

EGF

Sequence

A = S EQk (B)

Set

A = S ETk (B)

Cycle

A = C YCk (B)

A(z) = B(z)k 1 A(z) = B(z)k k! 1 A(z) = B(z)k k

3. The additional constructions of pointing and substitution. Construction

EGF

Pointing

A = 2B

d B(z) A(z) = z dz

Substitution

A=B◦C

A(z) = B(C(z))

4. The “boxed” product. A = (B2 ⋆ C) H⇒ A(z) =

Z z d B(t) · C(t) dt. dt 0

Figure II.18. A “dictionary” of labelled constructions together with their translation into exponential generating functions (EGFs). The first constructions are counterparts of the unlabelled constructions of the previous chapter (the multiset construction is not meaningful here). Translation for composite constructions of bounded cardinality appears to be simple. Finally, the boxed product is specific to labelled structures. (Compare with the unlabelled counterpart, Figure I.18, p. 18.)

books by Comtet [129], Wilf [608], Stanley [552], or Goulden and Jackson [303] have many examples of the use of labelled constructions in combinatorial analysis. Greene [308] has introduced in his 1983 dissertation a general framework of “labelled grammars” largely based on the boxed product with implications for the random generation of combinatorial structures. Joyal’s theory of species dating from 1981 (see [359] for the original

II. 7. PERSPECTIVE

149

article and the book by Bergeron, Labelle, and Leroux [50] for a rich exposition) is based on category theory; it presents the advantage of uniting in a common framework the unlabelled and the labelled worlds. Flajolet, Salvy, and Zimmermann have developed a specification language closely related to the system expounded here. They show in [255] how to compile automatically specifications into generating functions; this is complemented by a calculus that produces fast random generation algorithms [264].

I can see looming ahead one of those terrible exercises in probability where six men have white hats and six men have black hats and you have to work it out by mathematics how likely it is that the hats will get mixed up and in what proportion. If you start thinking about things like that, you would go round the bend. Let me assure you of that! —AGATHA C HRISTIE (The Mirror Crack’d. Toronto, Bantam Books, 1962.)

III

Combinatorial Parameters and Multivariate Generating Functions Generating functions find averages, etc. — H ERBERT W ILF [608]

III. 1. III. 2. III. 3. III. 4. III. 5. III. 6. III. 7. III. 8. III. 9.

An introduction to bivariate generating functions (BGFs) 152 Bivariate generating functions and probability distributions 156 Inherited parameters and ordinary MGFs 163 Inherited parameters and exponential MGFs 174 Recursive parameters 181 Complete generating functions and discrete models 186 Additional constructions 198 Extremal parameters 214 Perspective 218

Many scientific endeavours demand precise quantitative information on probabilistic properties of parameters of combinatorial objects. For instance, when designing, analysing, and optimizing a sorting algorithm, it is of interest to determine the typical disorder of data obeying a given model of randomness, and to do so in the mean, or even in distribution, either exactly or asymptotically. Similar situations arise in a broad variety of fields, including probability theory and statistics, computer science, information theory, statistical physics, and computational biology. The exact problem is then a refined counting problem with two parameters, namely, size and an additional characteristic: this is the subject addressed in this chapter and treated by a natural extension of the generating function framework. The asymptotic problem can be viewed as one of characterizing in the limit a family of probability laws indexed by the values of the possible sizes: this is a topic to be discussed in Chapter IX. As demonstrated here, the symbolic methods initially developed for counting combinatorial objects adapt gracefully to the analysis of various sorts of parameters of constructible classes, unlabelled and labelled alike. Multivariate generating functions (MGFs)—ordinary or exponential—can keep track of a collection of parameters defined over combinatorial objects. From the knowledge of such generating functions, there result either explicit probability distributions or, at least, mean and variance evaluations. For inherited parameters, all the combinatorial classes discussed so far are amenable to such a treatment. Technically, the translation schemes that relate combinatorial constructions and multivariate generating functions present no major difficulty—they appear to be natural (notational, even) refinements of the paradigm developed in Chapters I and II for the univariate case. Typical applications from classical combinatorics are the number of summands 151

152

III. PARAMETERS AND MULTIVARIATE GFS

in a composition, the number of blocks in a set partition, the number of cycles in a permutation, the root degree or path length of a tree, the number of fixed points in a permutation, the number of singleton blocks in a set partition, the number of leaves in trees of various sorts, and so on. Beyond its technical aspects anchored in symbolic methods, this chapter also serves as a first encounter with the general area of random combinatorial structures. The general question is: What does a random object of large size look like? Multivariate generating functions first provide an easy access to moments of combinatorial parameters—typically the mean and variance. In addition, when combined with basic probabilistic inequalities, moment estimates often lead to precise characterizations of properties of large random structures that hold with high probability. For instance, a large integer partition conforms with high probability to a deterministic profile, a large random permutation almost surely has at least one long cycle and a few short ones, and so on. Such a highly constrained behaviour of large objects may in turn serve to design dedicated algorithms and optimize data structures; or it may serve to build statistical tests—when does one depart from randomness and detect a “signal” in large sets of observed data? Randomness forms a recurrent theme of the book: it will be developed much further in Chapter IX, where the complex asymptotic methods of Part B are grafted on the exact modelling by multivariate generating functions presented in this chapter. This chapter is organized as follows. First a few pragmatic developments related to bivariate generating functions are presented in Section III. 1. Next, Section III. 2 presents the notion of bivariate enumeration and its relation to discrete probabilistic models, including the determination of moments, since the language of elementary probability theory does indeed provide an intuitively appealing way to conceive of bivariate counting data. The symbolic method per se, declined in its general multivariate version, is centrally developed in Sections III. 3 and III. 4: with suitable multi-index notations, the extension of the symbolic method to the multivariate case is almost immediate. Recursive parameters that often arise in particular from tree statistics form the subject of Section III. 5, while complete generating functions and associated combinatorial models are discussed in Section III. 6. Additional constructions such as pointing, substitution, and order constraints lead to interesting developments, in particular, an original treatment of the inclusion–exclusion principle in Section III. 7. The chapter concludes, in Section III. 8, with a brief abstract discussion of extremal parameters like height in trees or smallest and largest components in composite structures— such parameters are best treated via families of univariate generating functions. III. 1. An introduction to bivariate generating functions (BGFs) We have seen in Chapters I and II that a number sequence ( f n ) can be encoded by means of a generating function in one variable, either ordinary or exponential: X f n z n (ordinary GF) n ( fn ) ; f (z) = X zn (exponential GF). fn n! n

III. 1. AN INTRODUCTION TO BIVARIATE GENERATING FUNCTIONS (BGFS)

f 00 f 10

f 11

f 20 .. .

f 21 .. .

f 22 .. .

↓

↓

↓

f h0i (z)

f h1i (z)

−→

f 0 (u)

−→

f 1 (u)

−→

f 2 (u)

153

f h2i (z)

Figure III.1. An array of numbers and its associated horizontal and vertical GFs.

This encoding is powerful, since many combinatorial constructions admit a translation as operations over such generating functions. In this way, one gains access to many useful counting formulae. Similarly, consider a sequence of numbers ( f n,k ) depending on two integer-valued indices, n and k. Usually, in this book, ( f n,k ) will be an array of numbers (often a triangular array), where f n,k is the number of objects ϕ in some class F, such that |ϕ| = n and some parameter χ (ϕ) is equal to k. We can encode this sequence by means of a bivariate generating function (BGF) involving two variables: a primary variable z attached to n and a secondary u attached to k. Definition III.1. The bivariate generating functions (BGFs), either ordinary or exponential, of an array ( f n,k ) are the formal power series in two variables defined by X f n,k z n u k n,k f (z, u) = X zn k u f n,k n!

(ordinary BGF) (exponential BGF).

n,k

n

k

(The “double exponential” GF corresponding to zn! uk! is not used in the book.) As we shall see shortly, parameters of constructible classes become accessible through such BGFs. According to the point of view adopted for the moment, one starts with an array of numbers and forms a BGF by a double summation process. We present here two examples related to binomial coefficients and Stirling cycle numbers illustrating how such BGFs can be determined, then manipulated. In what follows it is convenient to refer to the horizontal and vertical generating functions (Figure III.1) that are each a one-parameter family of GFs in a single variable defined by horizontal GF: vertical GF:

f n (u)

:=

f hki (z) := f

hki

(z) :=

P

P

P

k n

f n,k u k ; f n,k z n

zn n f n,k n!

(ordinary case) (exponential case).

154

III. PARAMETERS AND MULTIVARIATE GFS

(0) (1)

(2)

(3)

(4) (5)

Figure III.2. The set W5 of the 32 binary words over the alphabet {, } enumerated according to the number of occurrences of the letter ‘’ gives rise to the bivariate counting sequence {W5, j } = 1, 5, 10, 10, 5, 1.

The terminology is transparently explained if the elements ( f n,k ) are arranged as an infinite matrix, with f n,k placed in row n and column k, since the horizontal and vertical GFs appear as the GFs of the rows and columns respectively. Naturally, one has X f n (u)z n (ordinary BGF) X n f (z, u) = u k f hki (z) = X zn f n (u) (exponential BGF). k n! n Example III.1. The ordinary BGF of binomial coefficients. The binomial coefficient nk counts binary words of length n having k occurrences of a designated letter; see Figure III.2. In order to compose the bivariate GF, start from the simplest case of Newton’s binomial theorem and directly form the horizontal GFs corresponding to a fixed n: n X n k (1) Wn (u) := u = (1 + u)n , k k=0

Then a summation over all values of n gives the ordinary BGF X n X 1 . (2) W (z, u) = uk zn = (1 + u)n z n = 1 − z(1 + u) k k,n≥0

n≥0

Such calculations are typical of BGF manipulations. What we have done amounts to starting from a sequence of numbers, Wn,k , determining the horizontal GFs Wn (u) in (1), then the bivariate GF W (z, u) in (2), according to the scheme: Wn,k

;

Wn (u)

;

W (z, u).

The BGF in (2) reduces to the OGF (1 − 2z)−1 of all words, as it should, upon setting u = 1. In addition, one can deduce from (2) the vertical GFs of the binomial coefficients corresponding to a fixed value of k X n zk W hki (z) = zn = , k (1 − z)k+1 n≥0

III. 1. AN INTRODUCTION TO BIVARIATE GENERATING FUNCTIONS (BGFS)

155

from an expansion of the BGF with respect to u W (z, u) =

(3)

X zk 1 1 = uk , z 1 − z 1 − u 1−z (1 − z)k+1 k≥0

and the result naturally matches what a direct calculation would give. . . . . . . . . . . . . . . . . . . . .

III.1. The exponential BGF of binomial coefficients. This is (4)

e (z, u) = W

X n k,n

k

uk

X zn zn = (1 + u)n = e z(1+u) . n! n!

The vertical GFs are e z z k /k!. The horizontal GFs are (1 + u)n , as in the ordinary case.

Example III.2. The exponential BGF of Stirling cycle numbers. As seen Example II.12, p. 121, the number Pn,k of permutations of size n having k cycles equals the Stirling cycle number nk , a vertical EGF being X n z n L(z)k 1 = , L(z) := log . P hki (z) := k n! k! 1 − z n From this, the exponential BGF is formed as follows (this revisits the calculations on p. 121): (5)

P(z, u) :=

X k

P hki (z)u k =

X uk L(z)k = eu L(z) = (1 − z)−u . k! k

The simplification is quite remarkable but altogether quite typical, as we shall see shortly, in the context of a labelled set construction. The starting point is thus a collection of vertical EGFs and the scheme is now hki

Pn

;

P hki (z)

;

P(z, u).

The BGF in (5) reduces to the EGF (1 − z)−1 of all permutations, upon setting u = 1. Furthermore, an expansion of the BGF in terms of the variable z provides useful information; namely, the horizontal GF is obtained by Newton’s binomial theorem: X n + u − 1 X zn P(z, u) = zn = Pn (u) , n! n (6) n≥0 n≥0 where

Pn (u)

=

u(u + 1) · · · (u + n − 1).

This last polynomial is called the Stirling cycle polynomial of index n and it describes completely the distribution of the number of cycles in all permutations of size n. In addition, the relation Pn (u) = Pn−1 (u)(u + (n − 1)), is equivalent to the recurrence n n−1 n−1 = (n − 1) + , k k k−1 by which Stirling numbers are often defined and easily evaluated numerically; see also Appendix A.8: Stirling numbers, p. 735. (The recurrence is susceptible to a direct combinatorial interpretation—add n either to an existing cycle or as a “new” singleton.) . . . . . . . . . . . . . . . .

156

III. PARAMETERS AND MULTIVARIATE GFS

Numbers n k

Horizontal GFs

Vertical OGFs zk (1 − z)k+1

Ordinary BGF 1 1 − z(1 + u)

(1 + u)n

Numbers n k Vertical EGFs k 1 1 log k! 1−z

Horizontal GFs u(u + 1) · · · (u + n − 1) Exponential BGF (1 − z)−u

Figure III.3. The various GFs associated with binomial coefficients (left) and Stirling cycle numbers (right).

Concise expressions for BGFs, like (2), (3), (5), or (18), are summarized in Figure III.3; they are invaluable for deriving moments, variance, and even finer characteristics of distributions, as we see next. The determination of such BGFs can be covered by a simple extension of the symbolic method, as will be detailed in Sections III. 3 and III. 4. III. 2. Bivariate generating functions and probability distributions Our purpose in this book is to analyse characteristics of a broad range of combinatorial types. The eventual goal of multivariate enumeration is the quantification of properties present with high regularity in large random structures. We shall be principally interested in enumeration according to size and an auxiliary parameter, the corresponding problems being naturally treated by means of BGFs. In order to avoid redundant definitions, it proves convenient to introduce the sequence of fundamental factors (ωn )n≥0 , defined by (7)

ωn = 1

for ordinary GFs,

ωn = n! for exponential GFs.

Then, the OGF and EGF of a sequence ( f n ) are jointly represented as X zn f (z) = fn and f n = ωn [z n ] f (z). ωn

Definition III.2. Given a combinatorial class A, a (scalar) parameter is a function from A to Z≥0 that associates to any object α ∈ A an integer value χ (α). The sequence An,k = card {α ∈ A |α| = n, χ (α) = k} , is called the counting sequence of the pair A, χ . The bivariate generating function (BGF) of A, χ is defined as X zn A(z, u) := An,k u k , ωn n,k≥0

and is ordinary if ωn ≡ 1 and exponential if ωn ≡ n!. One says that the variable z marks size and the variable u marks the parameter χ .

III. 2. BIVARIATE GENERATING FUNCTIONS AND PROBABILITY DISTRIBUTIONS

157

Naturally A(z, 1) reduces to the usual counting generating function A(z) associated with A, and the cardinality of An is expressible as An = ωn [z n ]A(z, 1). III. 2.1. Distributions and moments. Within this subsection, we examine the relationship between probabilistic models needed to interpret bivariate counting sequences and bivariate generating functions. The elementary notions needed are recalled in Appendix A.3: Combinatorial probability, p. 727. Consider a combinatorial class A. The uniform probability distribution over An assigns to any α ∈ An a probability equal to 1/An . We shall use the symbol P to denote probability and occasionally subscript it with an indication of the probabilistic model used, whenever this model needs to be stressed: we shall then write PAn (or simply Pn if A is understood) to indicate probability relative to the uniform distribution over An . Probability generating functions. Consider a parameter χ . It determines over each An a discrete random variable defined over the discrete probability space An : (8)

PAn (χ = k) =

An,k An,k . =P An k An,k

Given a discrete random variable X , typically, a parameter χ taken over a subclass An , we recall that its probability generating function (PGF) is by definition the quantity X (9) p(u) = P(X = k)u k . k

From (8) and (9), one has immediately: Proposition III.1 (PGFs from BGFs). Let A(z, u) be the bivariate generating function of a parameter χ defined over a combinatorial class A. The probability generating function of χ over An is given by X [z n ]A(z, u) , PAn (χ = k)u k = n [z ]A(z, 1) k

and is thus a normalized version of a horizontal generating function. The translation into the language of probability enables us to make use of whichever intuition might be available in any particular case, while allowing for a natural interpretation of data (Figure III.4). Indeed, instead of noting that the quantity 381922055502195 represents the number of permutations of size 20 that have 10 cycles, it is perhaps more informative to state the probability of the event, which is 0.00015, i.e., about 1.5 per 10 000. Discrete distributions are conveniently represented by histograms or “bar charts”, where the height of the bar at abscissa k indicates the value of P{X = k}. Figure III.4 displays two classical combinatorial distributions in this way. Given the uniform probabilistic model that we have been adopting, such histograms are eventually nothing but a condensed form of the “stacks” corresponding to exhaustive listings, like the one displayed in Figure III.2.

158

III. PARAMETERS AND MULTIVARIATE GFS

0.1

0.2

0.08 0.15 0.06 0.1 0.04 0.05

0.02 0

10

20

30

40

50

0

10

20

30

40

50

Figure III.4. Histograms of two combinatorial distributions. Left: the number of occurrences of a designated letter in a random binary word of length 50 (binomial distribution). Right: the number of cycles in a random permutation of size 50 (Stirling cycle distribution).

Moments. Important information is conveyed by moments. Given a discrete random variable X , the expectation of f (X ) is by definition the linear functional X E( f (X )) := P{X = k} · f (k). k

The (power) moments are E(X r ) :=

X k

P{X = k} · k r .

Then the expectation (or average, mean) of X , its variance, and its standard deviation, respectively, are expressed as p E(X ), V(X ) = E(X 2 ) − E(X )2 , σ (X ) = V(X ).

The expectation corresponds to what is typically seen when forming the arithmetic mean value of a large number of observations: this property is the weak law of large numbers [205, Ch X]. The standard deviation then measures the dispersion of values observed from the expectation and it does so in a mean-quadratic sense. The factorial moment defined for order r as (10)

E (X (X − 1) · · · (X − r + 1))

is also of interest for computational purposes, since it is obtained plainly by differentiation of PGFs (Appendix A.3: Combinatorial probability, p. 727). Power moments are then easily recovered as linear combinations of factorial moments, see Note III.9 of Appendix A. In summary: Proposition III.2 (Moments from BGFs). The factorial moment of order r of a parameter χ is determined from the BGF A(z, u) by r -fold differentiation followed by evaluation at 1: [z n ]∂ur A(z, u) u=1 EAn (χ (χ − 1) · · · (χ − r + 1)) = . [z n ]A(z, 1)

III. 2. BIVARIATE GENERATING FUNCTIONS AND PROBABILITY DISTRIBUTIONS

159

In particular, the first two moments satisfy EAn (χ )

[z n ]∂u A(z, u)|u=1 [z n ]A(z, 1) [z n ]∂ 2 A(z, u)

=

[z n ]∂u A(z, u)|u=1 , [z n ]A(z, 1) the variance and standard deviation being determined by EAn (χ 2 )

u u=1 [z n ]A(z, 1)

=

+

V(χ ) = σ (χ )2 = E(χ 2 ) − E(χ )2 . Proof. The PGF pn (u) of χ over An is given by Proposition III.1. On the other hand, factorial moments are on general grounds obtained by differentiation and evaluation at u = 1. The result follows. In other words, the quantities

n k (k) := ω · [z ] ∂ A(z, u) n n u

u=1

give, after a simple normalization (by ωn · [z n ]A(z, 1)), the factorial moments: 1 (k) E (χ (χ − 1) · · · (χ − k + 1)) = . An n Most notably, (1) n is the cumulated value of χ over all objects of An : X (1) n ≡ ωn · [z n ] ∂u A(z, u)|u=1 = χ (α) ≡ An · EAn (χ ). α∈An

(1)

Accordingly, the GF (ordinary or exponential) of the n is sometimes named the cumulative generating function. It can be viewed as an unnormalized generating function of the sequence of expected values. These considerations explain Wilf’s suggestive motto quoted on p. 151: “Generating functions find averages, etc”. (The “etc” can be interpreted as a token for higher moments and probability distributions.)

III.2. A combinatorial form of cumulative GFs. One has (1) (z) ≡

X

EAn (χ )An

n

X z |α| zn = , χ (α) ωn ω|α| α∈A

where ωn = 1 (ordinary case) or ωn = n! (exponential case).

Example III.3. Moments of the binomial distribution. The binomial distribution of index n can be defined as the distribution of the number of as in a random word of length n over the binary alphabet {a, b}. The determination of moments results easily from the ordinary BGF, W (z, u) = By differentiation, one finds

1 . 1 − z − zu

r !zr ∂r = W (z, u) . r ∂u (1 − 2z)r +1 u=1

Coefficient extraction then gives the form of the factorial moments of orders 1, 2, 3, . . . , r as n(n − 1) n(n − 1)(n − 2) r! n n . , , ,..., 2 4 8 2r r

160

III. PARAMETERS AND MULTIVARIATE GFS

√ In particular, the mean and the variance are 12 n and 14 n. The standard deviation is thus 21 n which is of a smaller order than the mean: this indicates that the distribution is somehow concentrated around its mean value, as suggested by Figure III.4. . . . . . . . . . . . . . . . . . . . . . . . . . . .

III.3. De Moivre’s approximation of the binomial coefficients. The fact that the mean and √ the standard deviation of the binomial distribution are respectively 12 n and 21 n suggests we examine what goes on at a distance of x standard deviations from the mean. Consider for simplicity the case of n = 2ν even. From the ratio 2ν (1 − ν1 )(1 − ν2 ) · · · (1 − k−1 ν+ℓ ν ), r (ν, ℓ) := 2ν = 1 2 (1 + ν )(1 + ν ) · · · (1 + νk ) ν the approximation log(1 + x) = x + O(x 2 ) shows that, for any fixed y ∈ R, 2ν lim √ n→∞, ℓ=ν+y ν/2

2 ν+ℓ = e−y /2 . 2ν ν

(Alternatively, Stirling’s formula can be employed.) This Gaussian approximation for the binomial distribution was discovered by Abraham de Moivre (1667–1754), a close friend of Newton. General methods for establishing such approximations are developed in Chapter IX. Example III.4. Moments of the Stirling cycle distribution. Let us return to the example of cycles in permutations which is of interest in connection with certain sorting algorithms like bubble sort or insertion sort, maximum finding, and in situ rearrangement [374]. We are dealing with labelled objects, hence exponential generating functions. As seen earlier on p. 155, the BGF of permutations counted according to cycles is P(z, u) = (1 − z)−u . By differentiating the BGF with respect to u, then setting u = 1, we next get the expected number of cycles in a random permutation of size n as a Taylor coefficient: 1 1 1 1 log = 1 + + ··· + , 1−z 1−z 2 n which is the harmonic number Hn . Thus, on average, a random permutation of size n has about log n + γ cycles, a well-known fact of discrete probability theory, derived on p. 122 by means of horizontal generating functions. For the variance, a further differentiation of the bivariate EGF gives 2 X 1 1 n log . (12) En (χ (χ − 1))z = 1−z 1−z

(11)

En (χ ) = [z n ]

n≥0

From this expression and Note III.4 (or directly from the Stirling cycle polynomials of p. 155), a calculation shows that n n X X 1 π2 1 1 2 (13) σn = = log n + γ − − . +O k 6 n k2 k=1

k=1

Thus, asymptotically,

σn ∼

p log n.

The standard deviation is of an order smaller than the mean, and therefore large deviations from the mean have an asymptotically negligible probability of occurrence (see below the discussion of moment inequalities). Furthermore, the distribution is asymptotically Gaussian, as we shall see in Chapter IX, p. 644. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

III. 2. BIVARIATE GENERATING FUNCTIONS AND PROBABILITY DISTRIBUTIONS

161

III.4. Stirling cycle numbers and harmonic numbers. By the “exp–log trick” of Chapter I, p. 29, the PGF of the Stirling cycle distribution satisfies ! 1 v 2 (2) v 3 (3) u =1+v u(u + 1) · · · (u + n − 1) = exp v Hn − Hn + Hn + · · · , n! 2 3 P (r ) where Hn is the generalized harmonic number nj=1 j −r . Consequently, any moment of the distribution is a polynomial in generalized harmonic numbers; compare (11) and (13). Furthermore, the kth moment satisfies EPn (χ k ) ∼ (log n)k . (The same technique expresses the (r ) Stirling cycle number nk as a polynomial in generalized harmonic numbers Hn−1 .) Alternatively, start from the expansion of (1 − z)−α and differentiate repeatedly with respect to α; for instance, one has X1 1 1 n+α−1 n 1 z , = + + ··· + (1 − z)−α log 1−z α α+1 n−1+α n n≥0

which provides (11) upon setting α = 1, while the next differentiation gives (13).

The situation encountered with cycles in permutations is typical of iterative (nonrecursive) structures. In many other cases, especially when dealing with recursive structures, the bivariate GF may satisfy complicated functional equations in two variables (see the example of path length in trees, Section III. 5 below), which means we do not know them explicitly. However, asymptotic laws can be determined in a large number of cases (Chapter IX). In all cases, the BGFs are the central tool in obtaining mean and variance estimates, since their derivatives evaluated at u = 1 become univariate GFs that usually satisfy much simpler relations than the BGFs themselves. III. 2.2. Moment inequalities and concentration of distributions. Qualitatively speaking, families of distributions can be classified into two categories: (i) distributions that are spread, i.e., the standard deviation is of order at least as large as the mean (e.g.the uniform distributions over [0 . . n], which have totally flat histograms); (ii) distributions for which the standard deviation is of an asymptotic order smaller than the mean (e.g., the Stirling cycle distribution, Figure III.4, and the binomial distribution, Figure III.5.) Such informal observations are indeed supported by the Markov– Chebyshev inequalities, which take advantage of information provided by the first two moments. (A proof is found in Appendix A.3: Combinatorial probability, p. 727.) Markov–Chebyshev inequalities. Let X be a non-negative random variable and Y an arbitrary real variable. One has for any t > 0: P {X ≥ tE(X )}

≤

P {|Y − E(Y )| ≥ tσ (Y )}

≤

1 t 1 t2

(Markov inequality) (Chebyshev inequality).

This result informs us that the probability of being much larger than the mean must decay (Markov) and that an upper bound on the decay is measured in units given by the standard deviation (Chebyshev). The next proposition formalizes a concentration property of distributions. It applies to a family of distributions indexed by the integers.

162

III. PARAMETERS AND MULTIVARIATE GFS

0.3 0.25 0.2 0.15 0.1 0.05 0

0.2

0.4

0.6

0.8

1

Figure III.5. Plots of the binomial distributions for n = 5, . . . , 50. The horizontal axis (by a factor of 1/n) and rescaled to 1, so that the curves display n is normalized o P( Xnn = x) , for x = 0, n1 , n2 , . . . .

Proposition III.3 (Concentration of distribution). Consider a family of random variables X n , typically, a scalar parameter χ on the subclass An . Assume that the means µn = E(X n ) and the standard deviations σn = σ (X n ) satisfy the condition lim

n→+∞

σn = 0. µn

Then the distribution of X n is concentrated in the sense that, for any ǫ > 0, there holds Xn ≤ 1 + ǫ = 1. (14) lim P 1 − ǫ ≤ n→+∞ µn Proof. The result is a direct consequence of Chebyshev’s inequality.

The concentration property (14) expresses the fact that values of X n tend to become closer and closer (in relative terms) to the mean µn as n increases. Another figurative way of describing concentration, much used in random combinatorics, is to say that “X n /µn tends to 1 in probability”; in symbols: Xn P −→ 1. µn When this property is satisfied, the expected value is in a strong sense a typical value— this fact is an extension of the weak law of large numbers of probability theory. Concentration properties of the binomial and Stirling cycle distributions. The binomial distribution is concentrated, since the mean of the distribution is n/2 and √ the standard deviation is n/4, a much smaller quantity. Figure III.5 illustrates concentration by displaying the graphs (as polygonal lines) associated to the binomial distributions for n = 5, . . . , 50. Concentration is also quite perceptible on simulations as n gets large: the table below describes the results of batches of ten (sorted)

III. 3. INHERITED PARAMETERS AND ORDINARY MGFS

simulations from the binomial distribution n n n n

= 100 = 1000 = 10 000 = 100 000

n

on n

1 2n k

k=0

163

:

39, 42, 43, 49, 50, 52, 54, 55, 55, 57 487, 492, 494, 494, 506, 508, 512, 516, 527, 545 4972, 4988, 5000, 5004, 5012, 5017, 5023, 5025, 5034, 5065 49798, 49873, 49968, 49980, 49999, 50017, 50029, 50080, 50101, 50284;

the maximal deviations from the mean observed on such samples are 22% (n = 102 ), 9% (n = 103 ), 1.3% (n = 104 ), and 0.6% (n = 105 ). Similarly, the mean and variance computations of (11) and (13) imply that the number of cycles in a random permutation of large size is concentrated. Finer estimates on distributions form the subject of our Chapter IX dedicated to limit laws. The reader may get a feeling of some of the phenomena at stake when examining Figure III.5 and Note III.3, p. 160: the visible emergence of a continuous curve (the bell-shaped curve) corresponds to a common asymptotic shape for the whole family of distributions—the Gaussian law. III. 3. Inherited parameters and ordinary MGFs In this section and the next, we address the question of determining BGFs directly from combinatorial specifications. The answer is provided by a simple extension of the symbolic method, which is formulated in terms of multivariate generating functions (MGFs). Such generating functions have the capability of taking into account a finite collection (equivalently, a vector) of combinatorial parameters. Bivariate generating functions discussed earlier appear as a special case. III. 3.1. Multivariate generating functions (MGFs). The theory is best developed in full generality for the joint analysis of a fixed finite collection of parameters. Definition III.3. Consider a combinatorial class A. A (multidimensional) parameter χ = (χ1 , . . . , χd ) on the class is a function from A to the set Zd≥0 of d–tuples of natural numbers. The counting sequence of A with respect to size and the parameter χ is then defined by An,k ,...,k = card α |α| = n, χ1 (α) = k1 , . . . , χd (α) = kd . 1

d

We sometimes refer to such a parameter as a “multiparameter” when d > 1, and a “simple” or “scalar” parameter otherwise. For instance, one may take the class P of all permutations σ , and for χ j ( j = 1, 2, 3) the number of cycles of length j in σ . Alternatively, we may consider the class W of all words w over an alphabet with four letters, {α1 , . . . , α4 } and take for χ j ( j = 1, . . . , 4) the number of occurrences of the letter α j in w, and so on. The multi-index convention employed in various branches of mathematics greatly simplifies notations: let x = (x1 , . . . , xd ) be a vector of d formal variables and k = (k1 , . . . , kd ) be a vector of integers of the same dimension; then, the multipower xk is defined as the monomial (15) With this notation, we have:

xk := x1k1 x2k2 · · · xdkd .

164

III. PARAMETERS AND MULTIVARIATE GFS

Definition III.4. Let An,k be a multi-index sequence of numbers, where k ∈ Nd . The multivariate generating function (MGF) of the sequence of either ordinary or exponential type is defined as the formal power series X A(z, u) = An,k uk z n (ordinary MGF) n,k

(16)

A(z, u)

=

X

An,k uk

n,k

zn n!

(exponential MGF).

Given a class A and a parameter χ , the MGF of the pair hA, χ i is the MGF of the corresponding counting sequence. In particular, one has the combinatorial forms: X A(z, u) = uχ (α) z |α| (ordinary MGF; unlabelled case) α∈A

(17)

A(z, u)

=

X

α∈A

uχ (α)

z |α| |α|!

(exponential MGF; labelled case).

One also says that A(z, u) is the MGF of the combinatorial class with the formal variable u j marking the parameter χ j and z marking size. From the very definition, with 1 a vector of all 1’s, the quantity A(z, 1) coincides with the generating function of A, either ordinary or exponential as the case may be. One can then view an MGF as a deformation of a univariate GF by way of a vector u, with the property that the multivariate GF reduces to the univariate GF at u = 1. If all but one of the u j are set to 1, then a BGF results; in this way, the symbolic calculus that we are going to develop gives full access to BGFs (and, from here, to moments).

III.5. Special cases of MGFs. The exponential MGF of permutations with u 1 , u 2 marking

the number of 1–cycles and 2–cycles respectively is 2 exp (u 1 − 1)z + (u 2 − 1) z2 (18) P(z, u 1 , u 2 ) = . 1−z (This will be proved later in this chapter, p. 187.) The formula is checked to be consistent with three already known special cases derived in Chapter II: (i) setting u 1 = u 2 = 1 gives back the counting of all permutations, P(z, 1, 1) = (1 − z)−1 , as it should; (ii) setting u 1 = 0 and u 2 = 1 gives back the EGF of derangements, namely e−z /(1 − z); (iii) setting u 1 = u 2 = 0 gives back the EGF of permutations with cycles all of length greater than 2, P(z, 0, 0) = 2 e−z−z /2 /(1 − z), a generalized derangement GF. In addition, the particular BGF

e(u−1)z , 1−z enumerates permutations according to singleton cycles. This last BGF interpolates between the EGF of derangements (u = 0) and the EGF of all permutations (u = 1). P(z, u, 1) =

III. 3.2. Inheritance and MGFs. Parameters that are inherited from substructures (definition below) can be taken into account by a direct extension of the symbolic method. With a suitable use of the multi-index conventions, it is even the case that the translation rules previously established in Chapters I and II can be copied verbatim. This approach provides a large quantity of multivariate enumeration results that follow automatically by the symbolic method.

III. 3. INHERITED PARAMETERS AND ORDINARY MGFS

165

Definition III.5. Let hA, χ i, hB, ξ i, hC, ζ i be three combinatorial classes endowed with parameters of the same dimension d. The parameter χ is said to be inherited in the following cases. • Disjoint union: when A = B + C, the parameter χ is inherited from ξ, ζ iff its value is determined by cases from ξ, ζ : ξ(ω) if ω ∈ B χ (ω) = ζ (ω) if ω ∈ C. • Cartesian product: when A = B × C, the parameter χ is inherited from ξ, ζ iff its value is obtained additively from the values of ξ, ζ : χ (β, γ ) = ξ(β) + ζ (γ ). • Composite constructions: when A = K{B}, where K is a metasymbol representing any of S EQ, MS ET, PS ET, C YC, the parameter χ is inherited from ξ iff its value is obtained additively from the values of ξ on components; for instance, for sequences: χ (β1 , . . . , βr ) = ξ(β1 ) + · · · + ξ(βr ). With a natural extension of the notation used for constructions, we shall write hA, χ i = hB, ξ i + hC, ζ i,

hA, χ i = hB, ξ i × hC, ζ i,

hA, χ i = K {hB, ξ i} .

This definition of inheritance is seen to be a natural extension of the axioms that size itself has to satisfy (Chapter I): size of a disjoint union is defined by cases; size of a pair, and similarly of a composite construction, is obtained by addition. Next, we need a bit of formality. Consider a pair hA, χ i, where A is a combinatorial class endowed with its usual size function | · | and χ = (χ1 , . . . , χd ) is a d-dimensional (multi)parameter. Write χ0 for size and z 0 for the variable marking size (previously denoted by z). The key point is to define an extended multiparameter χ = (χ0 , χ1 , . . . , χd ); that is, we treat size and parameters on an equal opportunity basis. Then the ordinary MGF in (16) assumes an extremely simple and symmetrical form: X X (19) A(z) = Ak zk = zχ (α) . α∈A

k

Here, the indeterminates are the vector z = (z 0 , z 1 , . . . , z d ), the indices are k = (k0 , k1 , . . . , kd ), where k0 indexes size (previously denoted by n) and the usual multiindex convention introduced in (15) is in force: (20)

k

zk := z 00 z 1k1 · · · z d kd ,

but it is now applied to (d + 1)-dimensional vectors. With this convention, we have: Theorem III.1 (Inherited parameters and ordinary MGFs). Let A be a combinatorial class constructed from B, C, and let χ be a parameter inherited from ξ defined on B and (as the case may be) from ζ on C. Then the translation rules of admissible constructions stated in Theorem I.1, p. 27, are applicable, provided the multi-index

166

III. PARAMETERS AND MULTIVARIATE GFS

convention (19) is used. The associated operators on ordinary MGFs are then (ϕ(k) is the Euler totient function, defined on p. 721): Union:

A=B+C

H⇒

A(z) = B(z) + C(z),

Product:

A=B×C

H⇒

Sequence:

A = S EQ(B)

H⇒

Powerset:

A = PS ET(B)

H⇒

Multiset:

A = MS ET(B) H⇒

Cycle:

A = C YC(B)

A(z) = B(z) · C(z), 1 A(z) = , 1 − B(z) X ∞ (−1)ℓ−1 B(zℓ ) . A(z) = exp ℓ ℓ=1 X ∞ 1 A(z) = exp B(zℓ ) , ℓ ℓ=1 ∞ X ϕ(ℓ) 1 A(z) = log , ℓ 1 − B(zℓ )

H⇒

ℓ=1

Proof. For disjoint unions, one has X X X A(z) = zχ (α) = zξ (β) + zζ (γ ) , α∈A

β∈B

γ ∈C

since inheritance is defined by cases on unions. For cartesian products, one has X X X zξ (β) × zζ (γ ) , A(z) = zχ (α) = α∈A

β∈B

γ ∈C

since inheritance corresponds to additivity on products. The translation of composite constructions in the case of sequences, powersets, and multisets is then built up from the union and product schemes, in exactly the same manner as in the proof of Theorem I.1. Cycles are dealt with by the methods of Appendix A.4: Cycle construction, p. 729. The multi-index notation is a crucial ingredient for developing the general theory of multivariate enumerations. When we work with only a small number of parameters, typically one or two, we will however often find it convenient to return to vectors of variables like (z, u) or (z, u, v). In this way, unnecessary subscripts are avoided. The reader is especially encouraged to study the treatment of integer compositions in Examples III.5 and III.6 below carefully, since it illustrates the power of the multivariate symbolic method, in its bare bones version. Example III.5. Integer compositions and MGFs I. The class C of all integer compositions (Chapter I) is specified by C = S EQ(I),

I = S EQ≥1 (Z),

where I is the set of all positive numbers. The corresponding OGFS are 1 z C(z) = , I (z) = , 1 − I (z) 1−z

so that Cn = 2n−1 (n ≥ 1). Say we want to enumerate compositions according to the number χ of summands. One way to proceed, in accordance with the formal definition of inheritance, is

III. 3. INHERITED PARAMETERS AND ORDINARY MGFS

167

as follows. Let ξ be the parameter that takes the constant value 1 on all elements of I. The parameter χ on compositions is inherited from the (almost trivial) parameter ξ ≡ 1 defined on summands. The ordinary MGF of hI, ξ i is I (z, u) = zu + z 2 u + z 3 u + · · · =

zu . 1−z

Let C(z, u) be the BGF of hC, χ i. By Theorem III.1, the schemes translating admissible constructions in the univariate case carry over to the multivariate case, so that (21)

C(z, u) =

1−z 1 1 = z = 1 − z(u + 1) . 1 − I (z, u) 1 − u 1−z

Et voil`a! . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Markers. There is an alternative way of arriving at MGFs, as in (21), which is important and will be of much use thoughout this book. A marker (or mark) in a specification 6 is a neutral object (i.e., an object of size 0) attached to a construction or an atom by a product. Such a marker does not modify size, so that the univariate counting sequence associated to 6 remains unaffected. On the other hand, the total number of markers that an object contains determines by design an inherited parameter, so that Theorem III.1 is automatically applicable. In this way, one may decorate specifications so as to keep track of “interesting” substructures and get BGFs automatically. The insertion of several markers similarly gives MGFs. For instance, say we are interested in the number of summands in compositions, as in Example III.5 above. Then, one has an enriched specification, and its translation into MGF, (22)

C = S EQ µ S EQ≥1 (Z)

H⇒

C(z, u) =

1 , 1 − u I (z)

based on the correspondence: Z 7→ z, µ 7→ u. Example III.6. Integer compositions and MGFs II. Consider the double parameter χ = (χ1 , χ2 ) where χ1 is the number of parts equal to 1 and χ2 the number of parts equal to 2. One can write down an extended specification, with µ1 a combinatorial mark for summands equal to 1 and µ2 for summands equal to 2, 2 C = S EQ µ1 Z + µ2 Z + S EQ≥3 (Z) (23) 1 , H⇒ C(z, u 1 , u 2 ) = 2 1 − (u 1 z + u 2 z + z 3 (1 − z)−1 ) where u j ( j = 1, 2) records the number of marks of type µ j . Similarly, let µ mark each summand and µ1 mark summands equal to 1. Then, one has, 1 (24) C = S EQ µµ1 Z + µ S EQ≥2 (Z) H⇒ C(z, u 1 , u) = , 1 − (uu 1 z + uz 2 (1 − z)−1 ) where u keeps track of the total number of summands and u 1 records the number of summands equal to 1.

168

III. PARAMETERS AND MULTIVARIATE GFS

MGFs obtained in this way via the multivariate extension of the symbolic method can then provide explicit counts, after suitable series expansions. For instance, the number of compositions of n with k parts is, by (21), 1−z n n−1 n−1 [z n u k ] = − = , 1 − (1 + u)z k k k−1 a result otherwise obtained in Chapter I by direct combinatorial reasoning (the balls-and-bars model). The number of compositions of n containing k parts equal to 1 is obtained from the special case u 2 = 1 in (23), 1

[z n u k ]

2

= [z n−k ]

(1 − z)k+1 , (1 − z − z 2 )k+1

z 1 − uz − (1−z) where the last OGF closely resembles a power of the OGF of Fibonacci numbers. Following the discussion of Section III. 2, such MGFs also carry complete information about moments. In particular, the cumulated value of the number of parts in all compositions of n has OGF z(1 − z) ∂u C(z, u)|u=1 = , (1 − 2z)2 since cumulated values are obtained via differentiation of a BGF. Therefore, the expected number of parts in a random composition of n is exactly (for n ≥ 1) 1 z(1 − z) 1 [z n ] = (n + 1). n−1 2 2 2 (1 − 2z) One further differentiation will give rise to the variance. The standard deviation is found to √ be 21 n − 1, which is of an order (much) smaller than the mean. Thus, the distribution of the number of summands in a random composition satisfies the concentration property as n → ∞. In the same vein, the number of parts equal to a fixed number r in compositions is determined by −1 z r r + (u − 1)z . C = S EQ µZ + S EQ6=r (Z) H⇒ C(z, u) = 1 − 1−z It is then easy to pull out the expected number of r -summands in a random composition of size n. The differentiated form

∂u C(z, u)|u=1 = gives, by partial fraction expansion,

zr (1 − z)2 (1 − 2z)2

2−r −1 − r 2−r −2 2−r −2 + + q(z), 2 1 − 2z (1 − 2z) for a polynomial q(z) that we do not need to make explicit. Extracting the nth coefficient of the cumulative GF ∂u C(z, 1) and dividing by 2n−1 yields the mean number of r –parts in a random composition. Another differentiation gives access to the second moment. One obtains the following proposition. Proposition III.4 (Summands in integer compositions). The total number of summands in a random composition of size n has mean 12 (n + 1) and a distribution that is concentrated around the mean. The number of r summands in a composition of size n has mean n + O(1); r 2 +1 √ and a standard deviation of order n, which also ensures concentration of distribution. ∂u C(z, u)|u=1 =

III. 3. INHERITED PARAMETERS AND ORDINARY MGFS

10 8 6 4 2 0

10

20

30

40

10 8 6 4 2 0

10

20

30

40

169

Figure III.6. A random composition of n = 100 represented as a ragged landscape (top); its associated profile 120 212 310 41 51 71 101 , defined as the partition obtained by sorting the summands (bottom).

Results of a simulation illustrating the proposition are displayed in Figure III.6 to which Note III.6 below adds further comments. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

III.6. The profile of integer compositions. From the point of view of random structures, Proposition III.4 shows that random compositions of large size tend to conform to a global “profile”. With high probability, a composition of size n should have about n/4 parts equal to 1, n/8 parts equal to 2, and so on. Naturally, there are statistically unavoidable fluctuations, and for any finite n, the regularity of this law cannot be perfect: it tends to fade away, especially with regard to largest summands that are log2 (n) + O(1) with high probability. (In this region mean and standard deviation both become of the same order and are O(1), so that concentration no longer holds.) However, such observations do tell us a great deal about what a typical random composition must (probably) look like—it should conform to a “geometric profile”, 1n/4 2n/8 3n/16 4n/32 · · · .

Here are for instance the profiles of two compositions of size n = 1024 drawn uniformly at random: 1250 2138 370 429 515 610 74 80 , 91

and

1253 2136 368 431 513 68 73 81 91 102 .

These are to be compared with the “ideal” profile 1256 2128 364 432 516 68 74 82 91 . It is a striking fact that samples of a very few elements or even just one element (this would be ridiculous by the usual standards of statistics) are often sufficient to illustrate asymptotic properties of large random structures. The reason is once more to be attributed to concentration of distributions whose effect is manifest here. Profiles of a similar nature present themselves among objects defined by the sequence construction, as we shall see throughout this book. (Establishing such general laws is usually not difficult but it requires the full power of complex analytic methods developed in Chapters IV–VIII.)

III.7. Largest summands in compositions. For any ǫ > 0, with probability tending to 1 as n → ∞, the largest summand in a random integer composition of size n is in the interval [(1 − ǫ) log2 n, (1 + ǫ) log2 n]. (Hint: use the first and second moment methods. More precise estimates are obtained by the methods of Example V.4, p. 308.)

170

III. PARAMETERS AND MULTIVARIATE GFS

K S EQ :

PS ET :

MS ET :

BGF (A(z, u))

cumulative GF ((z))

1 1 − u B(z) ∞ k X u (−1)k−1 B(z k ) exp k

A(z)2 · B(z) =

∞ Y

k=1

(1 + uz n ) Bn n=1 ∞ k X u exp B(z k ) k k=1

∞ Y (1 − uz n )−Bn n=1

C YC :

∞ X 1 ϕ(k) log k 1 − u k B(z k )

k=1

A(z) ·

A(z) · ∞ X

k=1

B(z) (1 − B(z))2

∞ X

(−1)k−1 B(z k )

∞ X

B(z k )

k=1

k=1

ϕ(k)

B(z k ) . 1 − B(z k )

Figure III.7. Ordinary GFs relative to the number of components in A = K(B).

Simplified notation for markers. It proves highly convenient to simplify notations, much in the spirit of our current practice, where the atom Z is reflected by the name of the variable z in GFs. The following convention will be systematically adopted: the same symbol (usually u, v, u 1 , u 2 . . .) is freely employed to designate a combinatorial marker (of size 0) and the corresponding marking variable in MGFs. For instance, we can write directly, for compositions, C = S EQ(u S EQ≥1 Z)),

C = S EQ(uu 1 Z + u S EQ≥2 Z)),

where u marks all summands and u 1 marks summands equal to 1, giving rise to (22) and (24) above. The symbolic scheme of Theorem III.1 invariably applies to enumeration according to the number of markers. III. 3.3. Number of components in abstract unlabelled schemas. Consider a construction A = K(B), where the metasymbol K designates any standard unlabelled constructor among S EQ, MS ET, PS ET, C YC. What is sought is the BGF A(z, u) of class A, with u marking each component. The specification is then of the form A = K(uB),

K = S EQ, MS ET, PS ET, C YC .

Theorem III.1 applies and yields immediately the BGF A(z, u). In addition, differentiating with respect to u then setting u = 1 provides the GF of cumulated values (hence, in a non-normalized form, the OGF of the sequence of mean values of the number of components): ∂ A(z, u) (z) = . ∂u u=1

III. 3. INHERITED PARAMETERS AND ORDINARY MGFS

171

20

15

10

5

0

2

4

6

8 10

Figure III.8. A random partition of size n = 100 has an aspect rather different from the profile of a random composition of the same size (Figure III.6).

In summary: Proposition III.5 (Components in unlabelled schemas). Given a construction, A = K(B), the BGF A(z, u) and the cumulated GF (z) associated to the number of components are given by the table of Figure III.7. Mean values are then recovered with the usual formula, EAn (# components) =

[z n ](z) . [z n ]A(z)

III.8. r –Components in abstract unlabelled schemas. Consider unlabelled structures. The BGF of the number of r –components in A = K{B} is given by −1 1 − zr Br A(z, u) = 1 − B(z) − (u − 1)Br zr , A(z, u) = A(z) · , 1 − uzr

in the case of sequences (K = S EQ) and multisets (K = MS ET), respectively. Similar formulae hold for the other basic constructions and for cumulative GFs.

III.9. Number of distinct components in a multiset. The specification and the BGF are Y

β∈B

1 + u S EQ≥1 (β)

H⇒

Bn Y uz n , 1+ 1 − zn

n≥1

as follows from first principles.

As an illustration of Proposition III.5, we discuss the profile of random partitions (Figure III.8). Example III.7. The profile of partitions. Let P = MS ET(I) be the class of all integer partitions, where I = S EQ≥1 (Z) represents integers in unary notation. The BGF of P with u marking the number χ of parts (or summands) is obtained from the specification ∞ k k X z u . P = MS ET(uI) H⇒ P(z, u) = exp k 1 − zk k=1

172

III. PARAMETERS AND MULTIVARIATE GFS

100 80 60 40 20 0

100

200

300

400

500

Figure III.9. The number of parts in random partitions of size 1, . . . , 500: exact values of the mean and simulations (circles, one for each value of n).

Equivalently, from first principles, P∼ =

∞ Y

S EQ (uIn )

n=1

H⇒

∞ Y

n=1

1 . 1 − uz n

The OGF of cumulated values then results from the second form of the BGF by logarithmic differentiation: ∞ X zk (25) (z) = P(z) · . 1 − zk k=1

Now, the factor on the right in (25) can be expanded as ∞ X

k=1

∞ X zk = d(n)z n , k 1−z n=1

with d(n) the number of divisors of n. Thus, the mean value of χ is (26)

En (χ ) =

n 1 X d( j)Pn− j . Pn j=1

The same technique applies to the number of parts equal to r . The form of the BGF is r Y e∼ e u) = 1 − z · P(z), P S EQ(In ) H⇒ P(z, = S EQ(uIr ) × 1 − uzr n6=r

which implies that the mean value of the number χ e of r –parts satisfies 1 1 n zr Pn−r + Pn−2r + Pn−3r + · · · . = En (e χ) = [z ] P(z) · Pn 1 − zr Pn From these formulae and a decent symbolic manipulation package, the means are calculated easily up to values of n well into the range of several thousand. . . . . . . . . . . . . . . . . . . . . . . . . .

The comparison between Figures III.6 and III.8 shows that different combinatorial models may well lead to rather different types of probabilistic behaviours. Figure III.9 displays the exact value of the mean number of parts in random partitions of size n = 1, . . . , 500, (as calculated from (26)) accompanied with the observed values of one

III. 3. INHERITED PARAMETERS AND ORDINARY MGFS

173

60

70 60

50

50

40

40

30

30 20 20 10

10 0

10

20

30

40

50

60

0

20

40

60

80

Figure III.10. Two partitions of P1000 drawn at random, compared to the limiting shape 9(x) defined by (27).

random sample for each value of n in the range. The mean number of parts is known to be asymptotic to √ n log n , √ π 2/3 √ and the distribution, though it admits a comparatively large standard deviation O( n), is still concentrated, in the technical sense of the term. We shall prove some of these assertions in Chapter VIII, p. 581. In recent years, Vershik and his collaborators [152, 595] have shown that most√integer partitions tend to conform to a definite profile given (after normalization by n) by the continuous plane curve y = 9(x) defined implicitly by π (27) y = 9(x) iff e−αx + e−αy = 1, α=√ . 6 This is illustrated in Figure III.10 by two randomly drawn elements of P1000 represented together with the “most likely” limit shape. The theoretical result explains the huge differences that are manifest on simulations between integer compositions and integer partitions. The last example of this section demonstrates the application of BGFs to estimates regarding the root degree of a tree drawn uniformly at random among the class Gn of general Catalan trees of size n. Tree parameters such as number of leaves and path length that are more global in nature and need a recursive definition will be discussed in Section III. 5 below. Example III.8. Root degree in general Catalan trees. Consider the parameter χ equal to the degree of the root in a tree, and take the class G of all plane unlabelled trees, i.e., general Catalan trees. The specification is obtained by first defining trees (G), then defining trees with a mark for subtrees (G ◦ ) dangling from the root: z G = Z × S EQ(G) G(z) = 1 − G(z) H⇒ z G ◦ = Z × S EQ(uG) G(z, u) = . 1 − uG(z)

174

III. PARAMETERS AND MULTIVARIATE GFS

This set of equations reveals that the probability that the root degree equals r is 1 n−1 r r 2n − 3 − r Pn {χ = r } = ∼ r +1 , [z ]G(z)r = Gn n−1 n−2 2 this by Lagrange inversion and elementary asymptotics. Furthermore, the cumulative GF is found to be zG(z) (z) = . (1 − G(z))2 The relation satisfied by G entails a further simplification, 1 1 − 1 G(z) − 1, (z) = G(z)3 = z z so that the mean root degree admits a closed form, n−1 1 G n+1 − G n = 3 , En (χ ) = Gn n+1 a quantity clearly asymptotic to 3. A random plane tree is thus usually composed of a small number of root subtrees, at least one of which should accordingly be fairly large. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

III. 4. Inherited parameters and exponential MGFs The theory of inheritance developed in the last section applies almost verbatim to labelled objects. The only difference is that the variable marking size must carry a factorial coefficient dictated by the needs of relabellings. Once more, with a suitable use of multi-index conventions, the translation mechanisms developed in the univariate case (Chapter II) remain in force, this in a way that parallels the unlabelled case. Let us consider a pair hA, χ i, where A is a labelled combinatorial class endowed with its size function | · | and χ = (χ1 , . . . , χd ) is a d-dimensional parameter. As before, the parameter χ is extended into χ by inserting size as zeroth coordinate and a vector z = (z 0 , . . . , z d ) of d + 1 indeterminates is introduced, with z 0 marking size and z j marking χ j . Once the multi-index convention of (20) defining zk has been brought into play, the exponential MGF of hA, χ i (see Definition III.4, p. 164) can be rephrased as X zχ (α) X zk = . Ak (28) A(z) = k0 ! |α|! k

α∈A

This MGF is exponential in z (alias z 0 ) but ordinary in the other variables; only the factorial k0 ! is needed to take into account relabelling induced by labelled products. We a priori restrict attention to parameters that do not depend on the absolute values of labels (but may well depend on the relative order of labels): a parameter is said to be compatible if, for any α, it assumes the same value on any labelled object α and all the order-consistent relabellings of α. A parameter is said to be inherited if it is compatible and it is defined by cases on disjoint unions and determined additively on labelled products—this is Definition III.5 (p. 165) with labelled products replacing cartesian products. In particular, for a compatible parameter, inheritance signifies additivity on components of labelled sequences, sets, and cycles. We can then cutand-paste (with minor adjustments) the statement of Theorem III.1, p. 165:

III. 4. INHERITED PARAMETERS AND EXPONENTIAL MGFS

175

Theorem III.2 (Inherited parameters and exponential MGFs). Let A be a labelled combinatorial class constructed from B, C, and let χ be a parameter inherited from ξ defined on B and (as the case may be) from ζ on C. Then the translation rules of admissible constructions stated in Theorem II.1, p. 103, are applicable, provided the multi-index convention (28) is used. The associated operators on exponential MGFs are then: Union: A=B+C H⇒ A(z) = B(z) + C(z) Product: A=B⋆C H⇒ A(z) = B(z) · C(z) 1 Sequence: A = S EQ(B) H⇒ A(z) = 1 − B(z) 1 Cycle: A = C YC(B) H⇒ A(z) = log . 1 − B(z) Set: A = S ET(B) H⇒ A(z) = exp B(z) .

Proof. Disjoint unions are treated in a similar manner to the unlabelled multivariate case. Labelled products result from X |β| + |γ | zξ (β) zζ (γ ) X zχ (α) = , A(z) = |β|, |γ | (|β| + |γ |)! |α|! α∈A

β∈B,γ ∈C

and the usual translation of binomial convolutions that reflect labellings by means of products of exponential generating functions (like in the univariate case detailed in Chapter II). The translation for composite constructions is then immediate. This theorem can be exploited to determine moments, in a way that entirely parallels its unlabelled counterpart. Example III.9. The profile of permutations. Let P be the class of all permutations and χ the number of components. Using the concept of marking, the specification and the exponential BGF are 1 = (1 − z)−u , P = S ET (u C YC(Z)) H⇒ P(z, u) = exp u log 1−z

as was already obtained by an ad hoc calculation in (5). We also know (p. 160) that the mean number of cycles is the harmonic number Hn and that the distribution is concentrated, since the standard deviation is much smaller than the mean. Regarding the number χ of cycles of length r , the specification and the exponential BGF are now P = S ET C YC6=r (Z) + u C YC=r (Z) r (29) e(u−1)z /r zr 1 = + (u − 1) . H⇒ P(z, u) = exp log 1−z r 1−z The EGF of cumulated values is then

zr 1 . r 1−z The result is a remarkably simple one: In a random permutation of size n, the mean number of r –cycles is equal to 1/r for any r ≤ n. Thus, the profile of a random permutation, where profile is defined as the ordered sequence of cycle lengths, departs significantly from what has been encountered for integer compositions (30)

(z) =

176

III. PARAMETERS AND MULTIVARIATE GFS

Figure III.11. The profile of permutations: a rendering of the cycle structure of six random permutations of size 500, where circle areas are drawn in proportion to cycle lengths. Permutations tend to have a few small cycles (of size O(1)), a few large ones (of size 2(n)), and altogether have Hn ∼ log n cycles on average. and partitions. Formula (30) also sheds a new light on the harmonic number formula for the mean number of cycles—each term 1/r in the harmonic number expresses the mean number of r –cycles. As formulae are so simple, one can extract more information. By (29) one has r

1 e−z /r , [z n−kr ] k 1−z k! r where the last factor counts permutations without cycles of length r . From this (and the asymptotics of generalized derangement numbers in Note IV.9, p. 261), one proves easily that the asymptotic law of the number of r –cycles is Poisson1 of rate 1/r ; in particular it is not concentrated. (This interesting property to be established in later chapters constitutes the starting point of an important study by Shepp and Lloyd [540].) Furthermore, the mean number of cycles whose size is between n/2 and n is Hn − H⌊n/2⌋ , a quantity that equals the probability of existence of such a long cycle and is approximately . log 2 = 0.69314. In other words, we expect a random permutation of size n to have one or a few large cycles. (See the article of Shepp and Lloyd [540] for the original discussion of largest and smallest cycles.) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . P{χ = k} =

III.10. A hundred prisoners II. This is the solution to the prisoners problem of Note II.15, p. 124 The better strategy goes as follows. Each prisoner will first open the drawer which corresponds to his number. If his number is not there, he’ll use the number he just found to access another drawer, then find a number there that points him to a third drawer, and so on, hoping to return to his original drawer in at most 50 trials. (The last opened drawer will then contain his number.) This strategy globally succeeds provided the initial permutation σ defined by σi (the number contained in drawer i) has all its cycles of length at most 50. The probability of the event is ! 100 X z z2 z 50 1 . 100 p = [z ] exp =1− + + ··· + = 0.31182 78206. 1 2 50 j j=51

1 The Poisson distribution of rate λ > 0 has the non-negative integers as support and is determined by

P{k} = e−λ

λk . k!

III. 4. INHERITED PARAMETERS AND EXPONENTIAL MGFS

177

Figure III.12. Two random allocations with m = 12, n = 48, corresponding to λ ≡ n/m = 4 (left). The right-most diagrams display the bins sorted by decreasing order of occupancy.

Do the prisoners stand a chance against a malicious director who would not place the numbers in drawers at random? For instance, the director might organize the numbers in a cyclic permutation. [Hint: randomize the problem by renumbering the drawers according to a randomly chosen permutation.] Example III.10. Allocations, balls-in-bins models, and the Poisson law. Random allocations and the balls-in-bins model were introduced in Chapter II in connection with the birthday paradox and the coupon collector problem. Under this model, there are n balls thrown into m bins in all possible ways, the total number of allocations being thus m n . By the labelled construction of words, the bivariate EGF with z marking the number of balls and u marking the number χ (s) of bins that contain s balls (s a fixed parameter) is given by zs m A = S EQm S ET6=s (Z) + u S ET=s (Z) H⇒ A(s) (z, u) = e z + (u − 1) . s! In particular, the distribution of the number of empty bins (χ (0) ) is expressible in terms of Stirling partition numbers: n! n (m − k)! m Pm,n (χ (0) = k) ≡ n [u k z n ]A(0) (z, u) = . k m−k m mn By differentiating the BGF, we get an exact expression for the mean (any s ≥ 0): 1 1 n−s n(n − 1) · · · (n − s + 1) 1 (31) 1− Em,n (χ (s) ) = . m s! m ms

Let m and n tend to infinity in such a way that n/m = λ is a fixed constant. This regime is extremely important in many applications, some of which are listed below. The average proportion of bins containing s elements is m1 Em,n (χ (s) ), and from (31), one obtains by straightforward calculations the asymptotic limit estimate, (32)

λs 1 Em,n (χ (s) ) = e−λ . s! n/m=λ, n→∞ m lim

(See Figure III.12 for two simulations corresponding to λ = 4.) In other words, a Poisson formula describes the average proportion of bins of a given size in a large random allocation. (Equivalently, the occupancy of a random bin in a random allocation satisfies a Poisson law in the limit.)

178

III. PARAMETERS AND MULTIVARIATE GFS

K

exponential BGF (A(z, u))

cumulative GF ((z))

S EQ :

1 1 − u B(z)

A(z)2 · B(z) =

S ET :

exp (u B(z))

A(z) · B(z) = B(z)e B(z)

C YC :

log

1 1 − u B(z)

B(z) (1 − B(z))2

B(z) . 1 − B(z)

Figure III.13. Exponential GFs relative to the number of components in A = K(B). The variance of each χ (s) (with fixed s) is estimated similarly via a second derivative and one finds: ! λs sλs−1 λs λs+1 (s) −2λ λ Vm,n (χ ) ∼ me . E(λ), E(λ) := e − − (1 − 2s) − s! (s − 1)! s! s! As a consequence, one has the convergence in probability, 1 (s) P −λ λs χ −→e , m s! valid for any fixed s ≥ 0. See Example VIII.14, p. 598 for an analysis of the most filled urn.

III.11. Hashing and random allocations. Random allocations of balls into bins are central in the understanding of a class of important algorithms of computer science known as hashing [378, 537, 538, 598]: given a universe U of data, set up a function (called a hashing function) h : U −→ [1 . . m] and arrange for an array of m bins; an element x ∈ U is placed in bin number h(x). If the hash function scrambles the data in a way that is suitably (pseudo)uniform, then the process of hashing a file of n records (keys, data items) into m bins is adequately modelled by a random allocation scheme. If λ = n/m, representing the “load”, is kept reasonably bounded (say, λ ≤ 10), the previous analysis implies that hashing allows for an almost direct access to data. (See also Example II.19, p. 146 for a strategy that folds colliding items into a table.)

Number of components in abstract labelled schemas. As in the unlabelled universe, a general formula gives the distribution of the number of components for the basic constructions. Proposition III.6. Consider labelled structures and the parameter χ equal to the number of components in a construction A = K{B}, where K is one of S EQ, S ET C YC. The exponential BGF A(z, u) and the exponential GF (z) of cumulated values are given by the table of Figure III.13. Mean values are then easily recovered, and one finds En (χ ) =

[z n ](z) n = n , An [z ]A(z)

by the same formula as in the unlabelled case.

III. 4. INHERITED PARAMETERS AND EXPONENTIAL MGFS

179

III.12. r –Components in abstract labelled schemas. The BGF A(z, u) and the cumulative EGF (z) are given by the following table, S EQ : S ET : C YC : in the labelled case.

1 zr

Br zr 1 · 2 r! (1 − B(z))

1 − B(z) + (u − 1) Brr ! Br zr exp B(z) + (u − 1) r! 1 log r 1 − B(z) + (u − 1) Brr !z

e B(z) ·

Br zr r!

1 Br zr · , (1 − B(z)) r!

Example III.11. Set partitions. Set partitions S are sets of blocks, themselves non-empty sets of elements. The enumeration of set partitions according to the number of blocks is then given by S = S ET(u S ET≥1 (Z))

H⇒

z S(z, u) = eu(e −1) .

Since set partitions are otherwise known to be enumerated by the Stirling partition numbers, one has the BGF and the vertical EGFs as a corollary, X n z n X n z n z 1 uk = eu(e −1) , = (e z − 1)k , k n! n! k! k n n,k

which is consistent with earlier calculations of Chapter II. The EGF of cumulated values, (z) is then almost a derivative of S(z): z d (z) = (e z − 1)ee −1 = S(z) − S(z). dz

Thus, the mean number of blocks in a random partition of size n equals S n = n+1 − 1, Sn Sn a quantity directly expressible in terms of Bell numbers. A delicate computation based on the asymptotic expansion of the Bell numbers reveals that the expected value and the standard deviation are asymptotic to √ n n , , log n log n respectively (Chapter VIII, p. 595). Similarly the exponential BGF of the number of blocks of size k is S = S ET(u S ET=k (Z) + S ET6=0,k (Z))

H⇒

z k S(z, u) = ee −1+(u−1)z /k! ,

out of which mean and variance can also be derived. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Example III.12. Root degree in Cayley trees. Consider the class T of Cayley trees (non-plane labelled trees) and the parameter “root-degree”. The basic specifications are T T (z) = Z ⋆ S ET(T ) = ze T (z) H⇒ T ◦ = Z ⋆ S ET(uT ) T (z, u) = zeuT (z) .

180

III. PARAMETERS AND MULTIVARIATE GFS

The set construction reflects the non-planar character of Cayley trees and the specification T ◦ is enriched by a mark associated to subtrees dangling from the root. Lagrange inversion provides the fraction of trees with root degree k, e−1 1 n! (n − 1)n−2−k ∼ , (k − 1)! (n − 1 − k)! (k − 1)! n n−1

k ≥ 1.

Similarly, the cumulative GF is found to be (z) = T (z)2 , so that the mean root degree satisfies 1 ∼ 2. ETn (root degree) = 2 1 − n Thus the law of root degree is asymptotically a Poisson law of rate 1, shifted by 1. Probabilistic phenomena qualitatively similar to those encountered in plane trees are observed here, since the mean root degree is asymptotic to a constant. However a Poisson law eventually reflecting the non-planarity condition replaces the modified geometric law (known as a negative binomial law) present in plane trees. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

III.13. Numbers of components in alignments. Alignments (O) are sequences of cycles (Chapter II, p. 119). The expected number of components in a random alignment of On is [z n ] log(1 − z)−1 (1 − log(1 − z)−1 )−2 . [z n ](1 − log(1 − z)−1 )−1

Methods of Chapter V imply that the number √ of components in a random alignment has expectation ∼ n/(e − 1) and standard deviation 2( n).

III.14. Image cardinality of a random surjection. The expected cardinality of the image of a random surjection in Rn (Chapter II, p. 106) is

[z n ]e z (2 − e z )−2 . [z n ](2 − e z )−1 The number of values whose preimages have cardinality k is obtained upon replacing the factor e z by z k /k!. By the methods of Chapters IV (p. 259) and V (p. 296), the √ image cardinality of a random surjection has expectation n/(2 log 2) and standard deviation 2( n).

III.15. Distinct component sizes in set partitions. Take the number of distinct block sizes and cycle sizes in set partitions and permutations. The bivariate EGFs are ∞ Y

n=1

n 1 − u + ue z /n! ,

as follows from first principles.

∞ Y

n=1

n 1 − u + ue z /n ,

Postscript: Towards a theory of schemas. Let us look back and recapitulate some of the information gathered in pages 167–180 regarding the number of components in composite structures. The classes considered in Figure III.14 are compositions of two constructions, either in the unlabelled or the labelled universe. Each entry contains the BGF for the number of components (e.g., cycles in permutations, parts in integer partitions, and so on), and the asymptotic orders of the mean and standard deviation of the number of components for objects of size n. Some obvious facts stand out from the data and call for explanation. First the outer construction appears to play the essential rˆole: outer sequence constructs (compare integer compositions, surjections and alignments) tend to dictate a number of

III. 5. RECURSIVE PARAMETERS

181

Unlabelled structures Integer partitions, MS ET ◦ S EQ

z u2 z2 exp u + + ··· 1−z 2 1 − z2 √ √ n log n , 2( n) ∼ √ π 2/3

!

Integer compositions, S EQ ◦ S EQ −1 z 1−u 1−z √ n ∼ , 2( n) 2

Labelled structures Set partitions, S ET ◦ S ET exp u e z − 1 √ n n ∼ ∼ log n log n

Surjections, S EQ ◦ S ET −1 1 − u ez − 1 √ n ∼ , 2( n) 2 log 2

Permutations, S ET ◦ C YC exp u log(1 − z)−1 p ∼ log n, ∼ log n

Alignments, S EQ ◦ C YC −1 1 − u log(1 − z)−1 √ n , 2( n) ∼ e−1

Figure III.14. Major properties of the number of components in six level-two structures. For each class, from top to bottom: (i) specification type; (ii) BGF; (iii) mean and standard deviation of the number of components.

components that is 2(n) on average, while outer set constructs (compare integer partitions, set partitions, and permutations) are associated with a greater variety of asymptotic regimes. Eventually, such facts can be organized into broad analytic schemas, as will be seen in Chapters V–IX.

III.16. Singularity and probability. The differences in behaviour are to be assigned to the rather different types of singularity involved (Chapters IV–VIII): on the one hand sets corresponding algebraically to an exp(·) operator induce an exponential blow-up of singularities; on the other hand sequences expressed algebraically by quasi-inverses (1 − ·)−1 are likely to induce polar singularities. Recursive structures such as trees lead to yet other types of phenomena with a number of components, e.g., the root degree, that is bounded in probability. III. 5. Recursive parameters In this section, we adapt the general methodology of previous sections in order to treat parameters that are defined by recursive rules over structures that are themselves recursively specified. Typical applications concern trees and tree-like structures. Regarding the number of leaves, or more generally, the number of nodes of some fixed degree, in a tree, the method of placing marks applies, as in the non-recursive case. It suffices to distinguish elements of interest and mark them by an auxiliary variable. For instance, in order to mark composite objects made of r components, where r is an integer and K designates any of S EQ, S ET (or MS ET, PS ET), C YC, one

182

III. PARAMETERS AND MULTIVARIATE GFS

should split a construction K(C) as follows: K(C) = uK=r (C) + K6=r (C) = (u − 1)Kr (C) + K(C). This technique gives rise to specifications decorated by marks to which Theorems III.1 and III.2 apply. For a recursively-defined structure, the outcome is a functional equation defining the BGF recursively. The situation is illustrated by Examples III.13 and III.14 below in the case of Catalan trees and the parameter number of leaves. Example III.13. Leaves in general Catalan trees. How many leaves does a random tree of some variety have? Can different varieties of trees be somehow distinguished by the proportion of their leaves? Beyond the botany of combinatorics, such considerations are for instance relevant to the analysis of algorithms since tree leaves, having no descendants, can be stored more economically; see [377, Sec. 2.3] for an algorithmic motivation for such questions. Consider once more the class G of plane unlabelled trees, G = Z × S EQ(G), enumerated ◦ by the Catalan numbers: G n = n1 2n−2 n−1 . The class G where each leaf is marked is G ◦ = Zu + Z × S EQ≥1 (G ◦ )

H⇒

G(z, u) = zu +

zG(z, u) . 1 − G(z, u)

The induced quadratic equation can be solved explicitly q 1 G(z, u) = 1 + (u − 1)z − 1 − 2(u + 1)z + (u − 1)2 z 2 . 2

It is however simpler to expand using the Lagrange inversion theorem which yields n 1 n−1 y k n k G n,k = [u ] [z ]G(z, u) = [u ] [y ] u+ n 1 − y n−k 1 n n−2 y 1 n n−1 = [y ] . = n k n k k−1 (1 − y)n−k

These numbers are known as Narayana numbers, see EIS A001263, and they surface repeatedly in connection with ballot problems. The mean number of leaves is derived from the cumulative GF, which is 1 z 1 , (z) = ∂u G(z, u)|u=1 = z + √ 2 2 1 − 4z so that the mean is n/2 exactly for n √ ≥ 2. The distribution is concentrated since the standard deviation is easily calculated to be O( n). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Example III.14. Leaves and node types in binary trees. The class B of binary plane trees, also 1 2n ) can be specified as enumerated by Catalan numbers (Bn = n+1 n

(33)

B = Z + (B × Z) + (Z × B) + (B × Z × B),

which stresses the distinction between four types of nodes: leaves, left branching, right branching, and binary. Let u 0 , u 1 , u 2 be variables that mark nodes of degree 0,1,2, respectively. Then the root decomposition (33) yields, for the MGF B = B(z, u 0 , u 1 , u 2 ), the functional equation B = zu 0 + 2zu 1 B + zu 2 B 2 ,

which, by Lagrange inversion, gives 2k1 n , Bn,k0 ,k1 ,k2 = n k0 , k1 , k2

III. 5. RECURSIVE PARAMETERS

183

subject to the natural conditions: k0 + k1 + k2 = n and k0 = k2 + 1. Moments can be easily calculated using this approach [499]. In particular, the mean number of nodes of each type is asymptotically: n n n leaves: ∼ , 1–nodes : ∼ , 2–nodes : ∼ . 4 2 4 There is an equal asymptotic proportion of leaves, double nodes, left branching, and right √ branching nodes. Furthermore, the standard deviation is in each case O( n), so that all the corresponding distributions are concentrated. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

III.17. Leaves and node-degree profile in Cayley trees. For Cayley trees, the bivariate EGF with u marking the number of leaves is the solution to T (z, u) = uz + z(e T (z,u) − 1). (By Lagrange inversion, the distribution is expressible in terms of Stirling partition numbers.) The mean number of leaves in a random Cayley tree is asymptotic to ne−1 . More generally, the mean number of nodes of outdegree k in a random Cayley tree of size n is asymptotic to 1 . k! Degrees are thus approximately described by a Poisson law of rate 1. n · e−1

III.18. Node-degree profile in simple varieties of trees. For a family of trees generated by T (z) = zφ(T (z)) with φ a power series, the BGF of the number of nodes of degree k satisfies T (z, u) = z φ(T (z, u)) + φk (u − 1)T (z, u)k ,

where φk = [u k ]φ(u). The cumulative GF is (z) = z

φk T (z)k = φk z 2 T (z)k−1 T ′ (z), 1 − zφ ′ (T (z))

from which expectations can be determined.

III.19. Marking in functional graphs. Consider the class F of finite mappings discussed in Chapter II: F = S ET(K), K = C YC(T ), T = Z ⋆ S ET(T ). The translation into EGFs is 1 , T (z) = ze T (z) . F(z) = e K (z) , K (z) = log 1 − T (z) Here are the bivariate EGFs for (i) the number of components, (ii) the number of maximal trees, (iii) the number of leaves: (i) eu K (z) , (iii)

(ii)

1 1 − T (z, u)

1 , 1 − uT (z)

with

T (z, u) = (u − 1)z + ze T (z,u) .

The trivariate EGF F(u 1 , u 2 , z) of functional graphs with u 1 marking components and u 2 marking trees is F(z, u 1 , u 2 ) = exp(u 1 log(1 − u 2 T (z))−1 ) =

1 . (1 − u 2 T (z))u 1

An explicit expression for the coefficients involves the Stirling cycle numbers.

184

III. PARAMETERS AND MULTIVARIATE GFS

We shall now stop supplying examples that could be multiplied ad libitum, since such calculations greatly simplify when interpreted in the light of asymptotic analysis, as developed in Part B. The phenomena observed asymptotically are, for good reasons, especially close to what the classical theory of branching processes provides (see the books by Athreya–Ney [21] and Harris [324], as well as our discussion in the context of “complete” GFs on p. 196). Linear transformations on parameters and path length in trees. We have so far been dealing with a parameter defined directly by recursion. Next, we turn to other parameters such as path length. As a preamble, one needs a simple linear transformation on combinatorial parameters. Let A be a class equipped with two scalar parameters, χ and ξ , related by χ (α) = |α| + ξ(α). Then, the combinatorial form of BGFs yields X X X z |α| u χ (α) = z |α| u |α|+ξ(α) = (zu)|α| u ξ(α) ; α∈A

α∈A

α∈A

that is, (34)

Aχ (z, u) = Aξ (zu, u).

This is clearly a general mechanism: Linear transformations and MGFs: A linear transformation on parameters induces a monomial substitution on the corresponding marking variables in MGFs. We now put this mechanism to use in the recursive analysis of path length in trees. Example III.15. Path length in trees. The path length of a tree is defined as the sum of distances of all nodes to the root of the tree, where distances are measured by the number of edges on the minimal connecting path of a node to the root. Path length is an important characteristic of trees. For instance, when a tree is used as a data structure with nodes containing additional information, path length represents the total cost of accessing all data items when a search is started from the root. For this reason, path length surfaces, under various models, in the analysis of algorithms, in particular, in the area of algorithms and data structures for searching and sorting (e.g., tree-sort, quicksort, radix-sort [377, 538]). The formal definition of path length of a tree is X dist(ν, root(τ )), (35) λ(τ ) := ν∈τ

where the sum is over all nodes of the tree and the distance between two nodes is measured by the number of connecting edges. The definition implies an inductive rule X (36) λ(τ ) = (λ(υ) + |υ|) , υ≺τ

in which υ ≺ τ indicates a summation over all the root subtrees υ of τ . (To verify the equivalence of (35) and (36), observe that path length also equals the sum of all subtree sizes.) From this point on, we focus the discussion on general Catalan trees (see Note III.20 for other cases): G = Z × S EQ(G). Introduce momentarily the parameter µ(τ ) = |τ |+λ(τ ). Then,

III. 5. RECURSIVE PARAMETERS

185

one has from the inductive definition (36) and the general transformation rule (34): z (37) G λ (z, u) = and G µ (z, u) = G λ (zu, u). 1 − G µ (z, u)

In other words, G(z, u) ≡ G λ (z, u) satisfies a nonlinear functional equation of the difference type: z G(z, u) = . 1 − G(uz, u) (This functional equation will be revisited in connection with area under Dyck paths in Chapter V, p. 330.) The generating function (z) of cumulated values of λ is then obtained by differentiation with respect to u, then setting u = 1. We find in this way that the cumulative GF (z) := ∂u G(z, u)|u=1 satisfies z zG ′ (z) + (z) , (z) = 2 (1 − G(z))

which is a linear equation that solves to (z) = z 2

z G ′ (z) z . − √ = 2(1 − 4z) 2 1 − 4z (1 − G(z))2 − z

Consequently, one has (n ≥ 1)

n = 22n−3 −

1 2n − 2 , 2 n−1

where the sequence starting 1, 5, 22, 93, 386 for n ≥ 2 constitutes EIS A000346. By elementary asymptotic analysis, we get: √ The mean path length of a random Catalan tree of size n is asymptotic to 21 π n 3 ; in short: a branch from the root to a√random node in a random Catalan tree of size n has expected length of the order of n. Random Catalan trees thus tend to be somewhat imbalanced—by comparison, a fully balanced binary tree has all paths of length at most log2 n + O(1). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

The imbalance in random Catalan trees is a general phenomenon—it holds for binary Catalan and more generally for all simple varieties of trees. Note III.20 √ below and Example VII.9 (p. 461) imply that path √ length is invariably of order n n on average in such cases. Height is of typical order n as shown by R´enyi and Szekeres [507], de Bruijn, Knuth, and Rice [145], Kolchin [386], as well as Flajolet and Odlyzko [246]: see Subsection VII. 10.2, p. 535 for the outline of a proof. Figure III.15 borrowed from [538] illustrates this on a simulation. (The contour of the histogram of nodes by levels, once normalized, has been proved to converge to the process known as Brownian excursion.)

III.20. Path length in simple varieties of trees. The BGF of path length in a variety of trees generated by T (z) = zφ(T (z)) satisfies In particular, the cumulative GF is

T (z, u) = zφ(T (zu, u)).

(z) ≡ ∂u (T (z, u))u=1 = from which coefficients can be extracted.

φ ′ (T (z)) (zT ′ (z))2 , φ(T (z))

186

III. PARAMETERS AND MULTIVARIATE GFS

Figure III.15. A random pruned binary tree of size 256 and its associated level profile: the histogram on the left displays the number of nodes at each level in the tree.

III. 6. Complete generating functions and discrete models By a complete generating function, we mean, loosely speaking, a generating function in a (possibly large, and even infinite in the limit) number of variables that mark a homogeneous collection of characteristics of a combinatorial class2 . For instance one might be interested in the joint distribution of all the different letters composing words, the number of cycles of all lengths in permutations, and so on. A complete MGF naturally entails detailed knowledge on the enumerative properties of structures to which it is relative. Complete generating functions, given their expressive power, also make weighted models amenable to calculation, a situation that covers in particular Bernoulli trials (p. 190) and branching processes from classical probability theory (p. 196). Complete GFs for words. As a basic example, consider the class of all words W = S EQ{A} over some finite alphabet A = {a1 , . . . , ar }. Let χ = (χ1 , . . . , χr ), where χ j (w) is the number of occurrences of the letter a j in word w. The MGF of A with respect to χ is A = u 1 a1 + u 2 a2 + · · · u r ar

H⇒

A(z, u) = zu 1 + zu 2 + · · · + zu r ,

and χ on W is clearly inherited from χ on A. Thus, by the sequence rule, one has (38)

W = S EQ(A)

H⇒

W (z, u) =

1 , 1 − z(u 1 + u 2 + · · · + u r )

which describes all words according to their compositions into letters.PIn particular, the number of words with n j occurrences of letter a j and with n = n j is in this

2Complete GFs are not new objects. They are simply an avatar of multivariate GFs. Thus the term is only meant to be suggestive of a particular usage of MGFs, and essentially no new theory is needed in order to cope with them.

III. 6. COMPLETE GENERATING FUNCTIONS AND DISCRETE MODELS

framework obtained as [u n1 1 u n2 2

· · · u rnr ] (u 1

n + u 2 + · · · + ur ) = n 1 , n 2 , . . . , nr n

We are back to the usual multinomial coefficients.

=

187

n! . n 1 !n 2 ! · · · nr

III.21. After Bhaskara Acharya (circa 1150AD). Consider all the numbers formed in decimal with digit 1 used once, with digit 2 used twice,. . . , with digit 9 used nine times. Such numbers all have 45 digits. Compute their sum S and discover, much to your amazement that S equals 45875559600006153219084769286399999999999999954124440399993846780915230713600000.

This number has a long run of nines (and further nines are hidden!). Is there a simple explanation? This exercise is inspired by the Indian mathematician Bhaskara Acharya who discovered multinomial coefficients near 1150AD; see [377, pp. 23–24] for a brief historical note.

Complete GFs for permutations and set partitions. Consider permutations and the various lengths of their cycles. The MGF where u k marks cycles of length k for k = 1, 2, . . . can be written as an MGF in infinitely many variables: ! z2 z3 z (39) P(z, u) = exp u 1 + u 2 + u 3 + · · · . 1 2 3 This MGF expression has the neat feature that, upon restricting all but a finite number of u j to 1, we derive all the particular cases of interest with respect to any finite collection of cycles lengths. Observe also that one can calculate in the usual way any coefficient [z n ]P as it only involves the variables u 1 , . . . , u n .

III.22. The theory of formal power series in infinitely many variables. (This note is for formalists.) Mathematically, an object like P in (39) is perfectly well defined. Let U = {u 1 , u 2 , . . .} be an infinite collection of indeterminates. First, the ring of polynomials R = C[U ] is well defined and a given element of R involves only finitely many indeterminates. Then, from R, one can define the ring of formal power series in z, namely R[[z]]. (Note that, if f ∈ R[[z]], then each [z n ] f involves only finitely many of the variables u j .) The basic operations and the notion of convergence, as described in Appendix A.5: Formal power series, p. 730, apply in a standard way. For instance, in the case of (39), the complete GF P(z, u) is obtainable as the formal limit ! z z k+1 zk P(z, u) = lim exp u 1 + · · · + u k + + ··· 1 k k+1 k→∞ in R[[z]] equipped with the formal topology. (In contrast, the quantity evocative of a generating function of words over an infinite alphabet −1 ∞ X ! W = 1−z u j j=1

cannot be soundly defined as an element of the formal domain R[[z]].)

Henceforth, we shall keep in mind that verifications of formal correctness regarding power series in infinitely many indeterminates are always possible by returning to basic definitions. Complete generating functions are often surprisingly simple to expand. For instance, the equivalent form of (39) P(z, u) = eu 1 z/1 · eu 2 z

2 /2

· eu 3 z

3 /3

···

188

III. PARAMETERS AND MULTIVARIATE GFS

implies immediately that the number of permutations with k1 cycles of size 1, k2 of size 2, and so on, is n! , k1 ! k2 ! · · · kn ! 1k1 2k2 · · · n kn

(40)

P provided jk j = n. This is a result originally due to Cauchy. Similarly, the EGF of set partitions with u j marking the number of blocks of size j is ! z2 z3 z S(z, u) = exp u 1 + u 2 + u 3 + · · · . 1! 2! 3! A formula analogous to (40) follows: the number of partitions with k1 blocks of size 1, k2 of size 2, and so on, is n! . k1 ! k2 ! · · · kn ! 1!k1 2!k2 · · · n!kn

Several examples of such complete generating functions are presented in Comtet’s book; see [129], pages 225 and 233.

III.23. Complete GFs for compositions and surjections.

The complete GFs of integer compositions and surjections with u j marking the number of components of size j are 1−

1 P∞

j j=1 u j z

,

1−

1 P∞

zj j=1 u j j!

.

P The associated counts with n = j jk j are given by n! k1 + k2 + · · · k1 + k2 + · · · , . k1 , k2 , . . . k1 , k2 , . . . 1!k1 2!k2 · · ·

These factored forms follow directly from the multinomial expansion. The symbolic form of the multinomial expansion of powers of a generating function is sometimes expressed in terms of Bell polynomials, themselves nothing but a rephrasing of the multinomial expansion; see Comtet’s book [129, Sec. 3.3] for a fair treatment of such polynomials.

III.24. Fa`a di Bruno’s formula. The formulae for the successive derivatives of a functional composition h(z) = f (g(z)) ∂z h(z) = f ′ (g(z))g ′ (z),

∂z2 h(z) = f ′′ (g(z))g ′ (z)2 + f ′ (z)g ′′ (z), . . . ,

are clearly equivalent to the expansion of a formal power series composition. Indeed, assume without loss of generality that z = 0 and g(0) = 0; set f n := ∂zn f (0), and similarly for g, h. Then k X fk X zn g g1 z + 2 z 2 + · · · . = hn h(z) ≡ n! k! 2! n k

Thus in one direct application of the multinomial expansion, one finds g ℓ k X fk X hn g1 ℓ1 g2 ℓ2 k k ··· , = n! k! 1! 2! k! ℓ1 , ℓ2 , . . . , ℓk k

C

where the summation condition C is: 1ℓ1 + 2ℓ2 + · · · + kℓk = n, ℓ1 + ℓ2 + · · · + ℓk = k. This shallow identity is known as Fa`a di Bruno’s formula [129, p. 137]. (Fa`a di Bruno (1825– 1888) was canonized by the Catholic Church in 1988, presumably for reasons unrelated to his formula.)

III. 6. COMPLETE GENERATING FUNCTIONS AND DISCRETE MODELS

189

III.25. Relations between symmetric functions. Symmetric functions may be manipulated by mechanisms that are often reminiscent of the set and multiset construction. They appear in many areas of combinatorial enumeration. Let X = {xi }ri=1 be a collection of formal variables. Define the symmetric functions Y X Y X xi z X X 1 an z n , (1 + xi z) = bn z n , cn z n . = = 1 − x z 1 − x z i i n n n i

i

i

The an , bn , cn , called, respectively, elementary, monomial, and power symmetric functions, are expressible as an =

X

i 1 σi+1

unary left-branching (u ′1 )

valley:

σi−1 > σi < σi+1

binary node (u 2 )

Figure III.17. Local order patterns in a permutation and the four types of nodes in the corresponding increasing binary tree.

inherited and the corresponding exponential MGFs are related by Z z A(z, u) = (∂t B(t, u)) · C(t, u) dt. 0

To illustrate this multivariate extension, we shall consider a quadrivariate statistic on permutations. Example III.23. Local order patterns in permutations. An element σi of a permutation written σ = σ1 , . . . , σn when compared to its immediate neighbours can be categorized into one of four types4 summarized in the first two columns of Figure III.17. The correspondence with binary increasing trees described in Example II.17 and Figure II.16 (p. 143) then shows the following: peaks and valleys correspond to leaves and binary nodes, respectively, while double rises and double falls are associated with right-branching and left-branching unary nodes. Consider the class b I of non-empty increasing binary trees (so that b I = I \ {ǫ} in the notations of p. 143) and let u 0 , u 1 , u ′1 , u 2 be markers for the number of nodes of each type, as summarized in Figure III.17. Then the exponential MGF of non-empty increasing trees under this statistic is given by b I = u 0 Z + u 1 (Z 2 ⋆ b I) + u ′1 (b I ⋆ Z 2 ) + u 2 (b I ⋆ Z2 ⋆ b I) Z z H⇒ b I (z) = u 0 z + I (w)2 dw, (u 1 + u 1 )b I (w) + u 2 b 0

which gives rise to the differential equation:

∂ b I (z, u) = u 0 + (u 1 + u ′1 )b I (z, u) + u 2 b I (z, u)2 . ∂z This is solved by separation of variables as v δ v 1 + δ tan(zδ) b − 1, I (z, u) = u 2 δ − v 1 tan(zδ) u 2 where the following abbreviations are used: q 1 v 1 = (u 1 + u ′1 ), δ = u 0 u 2 − v 12 . 2 One finds

(66)

z3 z2 b I = u 0 z + u 0 (u 1 + u ′1 ) + u 0 ((u 1 + u ′1 )2 + 2u 0 u 2 ) + · · · , 2! 3!

4Here, for |σ | = n, we regard σ as bordered by (−∞, −∞), i.e., we set σ = σ 0 n+1 = −∞ and let

the index i in Figure III.17 vary in [1 . . n]. Alternative bordering conventions prove occasionally useful.

III. 7. ADDITIONAL CONSTRUCTIONS

203

Figure III.18. The level profile of a random increasing binary tree of size 256. (Compare with Figure III.15, p. 186, for binary trees drawn under the uniform Catalan statistics.)

which agrees with the small cases. This calculation is consistent with what has been found in Chapter II regarding the EGF of all non-empty permutations and of alternating permutations, z , tan(z), 1−z

that follow from the substitutions {u 0 = u 1 = u ′1 = u 2 = 1} and {u 0 = u 2 = 1, u 1 = u ′1 = 0}, respectively. The substitution {u 0 = u 1 = u, u ′1 = u 2 = 1} gives a simple variant (without the empty permutation) of the BGF of Eulerian numbers (75) on p. 209. From the quadrivariate GF, there results that, in a tree of size n the mean number of nodes of nullary, unary, or binary type is asymptotic to n/3, with a variance that is O(n), thereby ensuring concentration of distribution. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

A similar analysis yields path length. It is found that a random increasing binary tree of size n has mean path length 2n log n + O(n). Contrary to what the uniform combinatorial model gives, such trees tend to be rather well balanced, and a typical branch is only about 38.6% longer than in a perfect binary . tree (since 2/ log 2 = 1.386): see Figure III.18 for an illustration. This fact applies to binary search trees (Note III.33) and it justifies the fact that the performance of such trees is quite good, when they are applied to random data [378, 429, 538] or subjected to randomization [451, 520]. See Subsection VI. 10.3 (p. 427) dedicated to tree recurrences for a general analysis of additive functionals on such trees and Example IX.28, p. 684, for a distributional analysis of depth.

III.33. Binary search trees (BSTs). BST(τ )

Given a permutation τ , one defines inductively a tree

by

BST(ǫ) = ∅; BST(τ ) = hτ1 , BST(τ |τ1 )i. (Here, τ | P represents the subword of τ consisting of those elements that satisfy predicate P.) Let IBT(σ ) be the increasing binary tree canonically associated to σ . Then one has the fundamental Equivalence Principle, IBT(σ )

shape

shape

≡

BST(σ

−1 ),

where A ≡ B means that A and B have identical tree shapes. (Hint: relate the trees to the cartesian representation of permutations [538, 600], as in Example II.17, p. 143.)

III. 7.3. Implicit structures. For implicit structures defined by a relation of the form A = K[X ], we note that equations involving sums and products, either labelled

204

III. PARAMETERS AND MULTIVARIATE GFS

or not, are easily solved just as in the univariate case. The same remark applies for sequence and set constructions: refer to the corresponding sections of Chapters I (p. 88) and II (p. 137). Again, the process is best understood by examples. Suppose for instance one wants to enumerate connected labelled graphs by the number of nodes (marked by z) and the number of edges (marked by u). The class K of connected graphs and the class G of all graphs are related by the set construction, G = S ET(K), meaning that every graph decomposes uniquely into connected components. The corresponding exponential BGFs then satisfy G(z, u) = e K (z,u)

implying

K (z, u) = log G(z, u),

since the number of edges in a graph is inherited (additively) from the corresponding numbers in connected components. Now, the number of graphs of size n having k , so that edges is n(n−1)/2 k ! ∞ n X n(n−1)/2 z (1 + u) (67) K (z, u) = log 1 + . n! n=1

This formula, which appears as a refinement of the univariate formula of Chapter II (p. 138), then simply reads: connected graphs are obtained as components (the log operator) of general graphs, where a general graph is determined by the presence or absence of an edge (corresponding to (1+u)) between any pair of nodes (the exponent n(n − 1)/2). To pull information out of the formula (67) is, however, not obvious due to the alternation of signs in the expansion of log(1 + w) and due to the strongly divergent character of the involved series. As an aside, we note here that the quantity b(z, u) = K z , u K u enumerates connected graphs according to size (marked by z) and excess (marked by u) of the number of edges over the number of nodes. This means that the results of Note II.23 (p. 135), obtained by Wright’s decomposition, can be rephrased as the expansion (within C(u)[[z]]): ! ∞ n −n X 1 n(n−1)/2 z u log 1 + (1 + u) W−1 (z) + W0 (z) + · · · = n! u n=1 (68) 1 1 1 1 1 1 T − T2 + log − T − T2 + ··· , = u 2 2 1−T 2 4 with T ≡ T (z). See Temperley’s early works [573, 574] as well as the “giant paper on the giant component” [354] and the paper [254] for direct derivations that eventually constitute analytic alternatives to Wright’s combinatorial approach.

Example III.24. Smirnov words. Following the treatment of Goulden and Jackson [303], we define a Smirnov word to be any word that has no consecutive equal letters. Let W = S EQ(A) be the set of words over the alphabet A = {a1 , . . . , ar } of cardinality r , and S be the set of

III. 7. ADDITIONAL CONSTRUCTIONS

205

Smirnov words. Let also v j mark the number of occurrences of the jth letter in a word. One has5 1 W (v 1 , . . . , vr ) = 1 − (v 1 + · · · + vr ) Start from a Smirnov word and substitute for any letter a j that appears in it an arbitrary nonempty sequence of letters a j . When this operation is done at all places of a Smirnov word, it gives rise to an unconstrained word. Conversely, any word can be associated to a unique Smirnov word by collapsing into single letters maximal groups of contiguous equal letters. In other terms, arbitrary words are derived from Smirnov words by a simultaneous substitution: W = S a1 7→ S EQ≥1 {a1 }, . . . , ar 7→ S EQ≥1 {ar } . This leads to the relation (69)

W (v 1 , . . . , vr ) = S

vr v1 , ... , 1 − v1 1 − vr

.

This relation determines the MGF S(v 1 , . . . , vr ) implicitly. Now, since the inverse function of v/(1 − v) is v/(1 + v), one finds the solution: −1 r X vj v1 vr . (70) S(v 1 , . . . , vr ) = W , ... , = 1 − 1 + v1 1 + vr 1+vj j=1

For instance, if we set v j = z, that is, we “forget” the composition of the words into letters, we obtain the OGF of Smirnov words counted according to length as X 1 1+z =1+ r (r − 1)n−1 z n . = z 1 − r 1+z 1 − (r − 1)z n≥1

This is consistent with elementary combinatorics since a Smirnov word of length n is determined by the choice of its first letter (r possibilities) followed by a sequence of n − 1 choices constrained to avoid one letter among r (and corresponding to r − 1 possibilities for each position). The interest of (70) is to apply equally well to the Bernoulli model where letters may receive unequal probabilities and where a direct combinatorial argument does not appear to be easy: it suffices to perform the substitution v j 7→ p j z in this case: see Example IV.10, p. 262 and Note V.11, p. 311, for applications to asymptotics. From these developments, one can next build the GF of words that never contain more than m consecutive equal letters. It suffices to effect in (70) the substitution v j 7→ v j + · · · + vm j . In particular for the univariate problem (or, equivalently, the case where letters are equiprobable), one finds the OGF 1 1−z m

1−r

z 1−z m 1 + z 1−z 1−z

=

1 − z m+1 . 1 − r z + (r − 1)z m+1

This extends to an arbitrary alphabet the analysis of single runs and double runs in binary words that was performed in Subsection I. 4.1, p. 51. Naturally, the present approach applies equally well to non-uniform letter probabilities and to a collection of run-length upper-bounds and lower-bounds dependent on each particular letter. This topic is in particular pursued by different methods in several works of Karlin and coauthors (see, e.g., [446]), themselves motivated by applications to life sciences. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5The variable z marking length, being redundant, is best omitted in this calculation.

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III. PARAMETERS AND MULTIVARIATE GFS

III.34. Enumeration in free groups. Consider the composite alphabet B = A ∪ A, where

A = {a1 , . . . , ar } and A = {a1 , . . . , ar }. A word over alphabet B is said to be reduced if it arises from a word over B by a maximal application of the reductions a j a j 7→ ǫ and a j a j 7→ ǫ (with ǫ the empty word). A reduced word thus has no factor of the form a j a j or a j a j . Such a reduced word serves as a canonical representation of an element in the free group Fr generated by A, upon identifying a j = a −1 j . The GF of the class R of reduced words, with u j and u j marking the number of occurrences of letter a j and a j , respectively, is ur u1 u1 ur + , ..., + , R(u 1 , . . . , u r , u 1 , . . . , u r ) = S 1 − u1 1 − u1 1 − ur 1 − ur where S is the GF of Smirnov words, as in (70). In particular this gives the OGF of reduced words with z marking length as R(z) = (1 + z)/(1 − (2r − 1)z); this implies Rn = 2r (2r − 1)n , which matches the result given by elementary combinatorics. The Abelian image λ(w) of an element w of the free group Fk is obtained by letting all letters commute and applying the reductions a j · a −1 j = 1. It can then be put under the form m

a1 1 · · · arm r , with each m j in Z, so that it can be identified with an element of Zr . Let x = m (x1 , . . . , xr ) be a vector of indeterminates and define xλ(w) to be the monomial x1 1 · · · xrm r . Of interest in certain group-theoretic investigations is the MGF of reduced words ! X zx1−1 zx1 zxr zxr−1 |w| λ(w) Q(z; x) := z x =S , , ..., + + 1 − zx1 1 − zxr 1 − zx −1 1 − zxr−1 w∈R

1

which is found to simplify to Q(z; x) =

1−z

Pr

1 − z2

−1 2 j=1 (x j + x j ) + (2r − 1)z

.

This last form appears in a paper of Rivin [514], where it is obtained by matrix techniques. Methods developed in Chapter IX can then be used to establish central and local limit laws for the asymptotic distribution of λ(w) over Rn , providing an alternative to the methods of Rivin [514] and Sharp [539]. (This note is based on an unpublished memo of Flajolet, Noy, and Ventura, 2006.)

III.35. Carlitz compositions II. Here is an alternative derivation of the OGF of Carlitz compositions (Note III.32, p. 201). Carlitz compositions with largest summand ≤ r are obtained from the OGF of Smirnov words by the substitution v j 7→ z j : −1 r j X z , (71) K [r ] (z) = 1 − 1+zj j=1

The OGF of all Carlitz compositions then results from letting r → ∞: −1 ∞ j X z . (72) K (z) = 1 − 1+zj j=1

The asymptotic form of the coefficients is derived in Chapter IV, p. 263.

III. 7.4. Inclusion–exclusion. Inclusion–exclusion is a familiar type of reasoning rooted in elementary mathematics. Its principle, in order to count exactly, consists in grossly overcounting, then performing a simple correction of the overcounting, then correcting the correction, and so on. Characteristically, enumerative results provided by inclusion exclusion involve an alternating sum. We revisit this process here in the

III. 7. ADDITIONAL CONSTRUCTIONS

207

perspective of multivariate generating functions, where it essentially reduces to a combined use of substitution and implicit definitions. Our approach follows Goulden and Jackson’s encyclopaedic treatise [303]. Let E be a set endowed with a real- or complex-valued measure | · | in such a way that, for A, B ⊂ E, there holds |A ∪ B| = |A| + |B|

whenever

A ∩ B = ∅.

Thus, | · | is an additive measure, typically taken as set cardinality (i.e., |e| = 1 for e ∈ E) or a discrete probability measure on E (i.e., |e| = pe for e ∈ E). The general formula |A ∪ B| = |A| + |B| − |AB| where AB := A ∩ B, follows immediately from basic set-theoretic principles: X X X X |c| = |a| + |b| − |i|. c∈A∪B

a∈A

b∈B

i∈A∩B

What is called the inclusion–exclusion principle or sieve formula is the following multivariate generalization, for an arbitrary family A1 , . . . , Ar ⊂ E: |A1 ∪ · · · ∪ Ar | ≡ E \ (A1 A2 · · · Ar ) X X (73) = |Ai | − |Ai1 Ai2 | + · · · + (−1)r −1 |A1 A2 · · · Ar |, 1≤i 1 0 and s = 0, respectively, W hsi (z) =

z k N (z)s−1 , D(z)s+1

W h0i (z) =

c(z) , D(z)

with N (z) and D(z) given by N (z) = (1 − r z)(c(z) − 1) + z k ,

D(z) = (1 − r z)c(z) + z k .

The expression of W h0i is in agreement with Chapter I, Equation (62), p. 61.

III.39. Patterns in Bernoulli sequences. Let A be an alphabet where letter α has probability πα and consider the Bernoulli model where letters in words are chosen independently. Fix a pattern p = p1 · · · pk and define the finite language of protrusions as [ Ŵ= {pi+1 pi+2 · · · pk }, i : ci 6=0

where the union is over all correlation positions of the pattern. Define now the correlation polynomial γ (z) (relative to p and the πα ) as the generating polynomial of the finite language of protrusions weighted by (πα ). For instance, p = ababa gives rise to Ŵ = {ǫ, ba, baba} and γ (z) = 1 + πa πb z 2 + πa2 πb2 z 4 .

The BGF of words with z marking length and u marking the number of occurrences of p is (u − 1)γ (z) − u , W (z, u) = (1 − z)((u − 1)γ (z) − u) + (u − 1)π [p]z k where π [p] is the product of the probabilities of letters of p.

III.40. Patterns in trees I. Consider the class B of pruned binary trees. An occurrence of pattern t in a tree τ is defined by a node of τ whose dangling subtree is isomorphic to t. We seek the BGF B(z, u) of class B where √ u marks the number of occurrences of t. The OGF of B is B(z) = (1− 1 − 4z)/(2z). The quantity v B(zv) is the BGF of B with v marking external nodes. By virtue of the pointing operation, the quantity 1 k ∂v (v B(zv)) , Uk := k! v=1

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III. PARAMETERS AND MULTIVARIATE GFS

describes trees with k distinct external nodes distinguished (pointed). Let m = |t|. The quantity X V := Uk u k (z m )k satisfies V = (v B(zv))v=1+uz m ,

by virtue of Taylor’s formula. It is also the BGF of trees with distinguished occurrences of t marked by v. Setting v 7→ u − 1 in V then gives B(z, u) as q 1 1 − 1 − 4z − 4(u − 1)z m+1 . (78) B(z, u) = 2z p 1 1 − 1 − 4z + 4z m+1 represents the OGF of trees not containing In particular B(z, 0) = 2z pattern t. The method generalizes to any simple variety of trees. It can be used to prove that the factored p representation (as a directed acyclic graph) of a random tree of size n has expected size O(n/ log n). (These results appear in [257]; see also Example IX.26, p. 680, for a related Gausian law.)

III.41. Patterns in trees II. Here follows an alternative derivation of (78) that is based on the

root decomposition of trees. A pattern t occurs either in the left root subtree τ0 , or in the right root subtree τ1 , or at the root iself in the case in which t coincides with τ . Thus the number ω[τ ] of occurrences of t in τ satisfies the recursive definition ω[τ ] = ω[τ0 ] + ω[τ1 ] + [[τ = t]], ω[τ ] The function u is almost multiplicative, and

ω[∅] = 0.

u ω[τ ] = u [[τ =t]] u ω[τ0 ] u ω[τ1 ] = u ω[τ0 ] u ω[τ1 ] + [[τ = t]] · (u − 1). P Thus, the bivariate generating function B(z, u) := t z |t| u ω[t] satisfies the quadratic equation, B(z, u) = 1 + (u − 1)z m + z B(z, u)2 ,

which, when solved, yields (78).

III. 8. Extremal parameters Apart from additively inherited parameters already examined at length in this chapter, another important category is that of parameters defined by a maximum rule. Two major cases are the largest component in a combinatorial structure (for instance, the largest cycle of a permutation) and the maximum degree of nesting of constructions in a recursive structure (typically, the height of a tree). In this case, bivariate generating functions are of little help, because of the nonlinear character of the maxfunction. The standard technique consists in introducing a collection of univariate generating functions defined by imposing a bound on the parameter of interest. Such GFs can then be constructed by the symbolic method in its univariate version. III. 8.1. Largest components. Consider a construction B = 8[A], where 8 may involve an arbitrary combination of basic constructions, and assume here for simplicity that the construction for B is a non-recursive one. This corresponds to a relation between generating functions B(z) = 9[A(z)], where 9 is the functional that is the “image” of the combinatorial construction 8. Elements of A thus appear as components in an object β ∈ B. Let B hbi denote the subclass of B formed with objects whose A–components all have a size at most b. The

III. 8. EXTREMAL PARAMETERS

215

GF of B hbi is obtained by the same process as that of B itself, save that A(z) should be replaced by the GF of elements of size at most b. Thus, B hbi (z) = 9[Tb A(z)], where the truncation operator is defined on series by Tb f (z) =

b X

fn z

n=0

n

( f (z) =

∞ X

f n z n ).

n=0

Example III.27. A pot-pourri of largest components. Several instances of largest components have already been analysed in Chapters I and II. For instance, the cycle decomposition of permutations translated by 1 P = S ET(C YC(Z)) H⇒ P(z) = exp log 1−z gives more generally the EGF of permutations with longest cycle ≤ b, ! z2 zb z , + + ··· + P hbi (z) = exp 1 2 b which involves the truncated logarithm. The labelled specification of words over an m–ary alphabet W = S ETm (S ET(Z))

H⇒

W (z) = e z

leads to the EGF of words such that each letter occurs at most b times: !m z2 zb z hbi + + ··· + , W (z) = 1 + 1! 2! b!

m

which now involves the truncated exponential. Similarly, the EGF of set partitions with largest block of size at most b is ! z2 zb z hbi . + + ··· + S (z) = exp 1! 2! b! A slightly less direct example is that of the longest run in a binary string (p. 51), which we now revisit. The collection W of binary words over the alphabet {a, b} admits the unlabelled specification W = S EQ(a) · S EQ(b S EQ(a)), corresponding to a “scansion” dictated by the occurrences of the letter b. The corresponding OGF then appears under the form 1 1 W (z) = Y (z) · , where Y (z) = 1 − zY (z) 1−z corresponds to Y = S EQ(a). Thus, the OGF of strings with at most k − 1 consecutive occurrences of the letter a obtains upon replacing Y (z) by its truncation: 1 W hki (z) = Y hki (z) , where Y hki (z) = 1 + z + z 2 + · · · + z k−1 , 1 − zY hki (z) so that 1 − zk W hki (z) = . 1 − 2z + z k+1 An asymptotic analysis is given in Example V.4, p. 308. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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III. PARAMETERS AND MULTIVARIATE GFS

Generating functions for largest components are thus easy to derive. The asymptotic analysis of their coefficients is however often hard when compared to additive parameters, owing to the need to rely on complex analytic properties of the truncation operator. The bases of a general asymptotic theory have been laid by Gourdon [305].

III.42. Smallest components. The EGF of permutations with smallest cycle of size > b is z z2 zb 1 exp − − − ··· − 1−z 1 2 b

!

.

A symbolic theory of smallest components in combinatorial structures is easily developed as regards formal GFs. Elements of the corresponding asymptotic theory are provided by Panario and Richmond in [470].

III. 8.2. Height. The degree of nesting of a recursive construction is a generalization of the notion of height in the simpler case of trees. Consider for instance a recursively defined class B = 8[B], where 8 is a construction. Let B [h] denote the subclass of B composed solely of elements whose construction involves at most h applications of 8. We have by definition B [h+1] = 8{B [h] }. Thus, with 9 the image functional of construction 8, the corresponding GFs are defined by a recurrence, B [h+1] = 9[B [h] ].

(This discussion is related to the semantics of recursion, p. 33.)

Example III.28. Generating functions for tree height. Consider first general plane trees: z . G = Z × S EQ(G) H⇒ G(z) = 1 − G(z) Define the height of a tree as the number of edges on its longest branch. Then the set of trees of height ≤ h satisfies the recurrence G [0] = Z,

G [h+1] = Z × S EQ(G [h] ).

Accordingly, the OGF of trees of bounded height satisfies G [0] (z) = z,

G [h+1] (z) =

The recurrence unwinds and one finds (79)

G [h] (z) =

z . 1 − G [h] (z)

z 1−

,

z 1−

z ..

.

1−z where the number of stages in the fraction equals b. This is the finite form (technically known as a “convergent”) of a continued fraction expansion. From implied linear recurrences and an analysis based on Mellin transforms, de Bruijn, Knuth, and Rice [145] have determined the √ average height of a general plane tree to be ∼ π n. We provide a proof of this fact in Chapter V (p. 329) dedicated to applications of rational and meromorphic asymptotics.

III. 8. EXTREMAL PARAMETERS

217

For plane binary trees defined by B =Z +B×B

B(z) = z + (B(z))2 ,

so that

(size here is the number of external nodes), the recurrence is B [0] (z) = z, B [h+1] (z) = z + (B [h] (z))2 .

In this case, the B [h] are the approximants to a “continuous quadratic form”, namely B [h] (z) = z + (z + (z + (· · · )2 )2 )2 .

These are polynomials of degree 2h for which no closed form expression is known, nor even likely to exist6. However, using complex asymptotic methods and singularity analysis, √ Flajolet and Odlyzko [246] have shown that the average height of a binary plane tree is ∼ 2 π n. See Subsection VII. 10.2, p. 535 for the sketch of a proof. For Cayley trees, finally, the defining equation is T = Z ⋆ S ET(T )

T (z) = ze T (z) .

H⇒

The EGF of trees of bounded height satisfy the recurrence T [0] (z) = z,

[h] T [h+1] (z) = ze T (z) .

We are now confronted with a “continuous exponential”, T [h] (z) = ze ze

ze

..

. ze z .

The average height was found√by R´enyi and Szekeres who appealed again to complex analytic methods and found it to be ∼ 2π n. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

These examples show that height statistics are closely related to iteration theory. Except in a few cases like general plane trees, normally no algebra is available and one has to resort to complex analytic methods as expounded in forthcoming chapters. III. 8.3. Averages and moments. For extremal parameters, the GFs of mean values obey a general pattern. Let F be some combinatorial class with GF f (z). Consider for instance an extremal parameter χ such that f [h] (z) is the GF of objects with χ parameter at most h. The GF of objects for which χ = h exactly is equal to f [h] (z) − f [h−1] (z).

Thus differencing gives access to the probability distribution of height over F. The generating function of cumulated values (providing mean values after normalization) is then ∞ h i X 4(z) = h f [h] (z) − f [h−1] (z) h=0

=

∞ h X h=0

i f (z) − f [h] (z) ,

as is readily checked by rearranging the second sum, or equivalently using summation by parts. 6 These polynomials are exactly the much-studied Mandelbrot polynomials whose behaviour in the

complex plane gives rise to extraordinary graphics (Figure VII.23, p. 536).

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III. PARAMETERS AND MULTIVARIATE GFS

For the largest components, the formulae involve truncated Taylor series. For height, analysis involves in all generality the differences between the fixed point of a functional 8 (the GF f (z)) and the approximations to the fixed point ( f [h] (z)) provided by iteration. This is a common scheme in extremal statistics.

III.43. The height of increasing binary trees. Given the specification of increasing binary trees in Equation (61), p. 143, the EGF of trees of height at most h is given by the recurrence Z z I [0] (z) = 1, I [h+1] (z) = 1 + I [h] (w)2 dw. 0

Devroye [157, 158] showed in 1986 that the expected height of a tree of size n is asymptotic . to c log n where c = 4.31107 is a solution of c log((2e)/c) = 1.

III.44. Hierarchical partitions. Let ε(z) = ez − 1. The generating function

ε(ε(· · · (ε(z)))) (h times). can be interpreted as the EGF of certain hierarchical partitions. (Such structures show up in statistical classification theory [585, 586].)

III.45. Balanced trees. Balanced structures lead to counting GFs close to the ones obtained for height statistics. The OGF of balanced 2–3 trees of height h counted by the number of leaves satisfies the recurrence Z [h+1] (z) = Z [h] (z 2 + z 3 ) = (Z [h] (z))2 + (Z [h] (z))3 ,

which can be expressed in terms of the iterates of σ (z) = z 2 + z 3 (see Note I.67, p. 91, as well as Chapter IV, p. 281, for asymptotics). It is possible to express the OGF of cumulated values of the number of internal nodes in such trees in terms of the iterates of σ .

III.46. Extremal statistics in random mappings. One can express the EGFs relative to the largest cycle, longest branch, and diameter of functional graphs. Similarly for the largest tree, largest component. [Hint: see [247] for details.] III.47. Deep nodes in trees. The BGF giving the number of nodes at maximal depth in a general plane tree or a Cayley tree can be expressed in terms of a continued fraction or a continuous exponential.

III. 9. Perspective The message of this chapter is that we can use the symbolic method not just to count combinatorial objects but also to quantify their properties. The relative ease with which we are able to do so is testimony to the power of the method as a major organizing principle of analytic combinatorics. The global framework of the symbolic method leads us to a natural structural categorization of parameters of combinatorial objects. First, the concept of inherited parameters permits a direct extension of the already seen formal translation mechanisms from combinatorial structures to GFs, for both labelled and unlabelled objects—this leads to MGFs useful for solving a broad variety of classical combinatorial problems. Second, the adaptation of the theory to recursive parameters provides information about trees and similar structures, this even in the absence of explicit representations of the associated MGFs. Third, extremal parameters, which are defined by a maximum rule (rather than an additive rule), can be studied by analysing families of univariate GFs. Yet another illustration of the power of the symbolic method is found in the notion of complete GF, which in particular enables us to study Bernoulli trials and branching processes.

III. 9. PERSPECTIVE

219

As we shall see starting with Chapter IV, these approaches become especially powerful since they serve as the basis for the asymptotic analysis of properties of structures. Not only does the symbolic method provide precise information about particular parameters, but it also paves the way for the discovery of general schemas and theorems that tell us what to expect about a broad variety of combinatorial types. Bibliographic notes. Multivariate generating functions are a common tool from classical combinatorial analysis. Comtet’s book [129] is once more an excellent source of examples. A systematization of multivariate generating functions for inherited parameters is given in the book by Goulden and Jackson [303]. In contrast generating functions for cumulated values of parameters (related to averages) seemed to have received relatively little attention until the advent of digital computers and the analysis of algorithms. Many important techniques are implicit in Knuth’s treatises, especially [377, 378]. Wilf discusses related issues in his book [608] and the paper [606]. Early systems specialized to tree algorithms were proposed by Flajolet and Steyaert in the 1980s [215, 261, 262, 560]; see also Berstel and Reutenauer’s work [56]. Some of the ideas developed there initially drew their inspiration from the well-established treatment of formal power series in non-commutative indeterminates; see the books by Eilenberg [189] and Salomaa and Soittola [527] as well as the proceedings edited by Berstel [54]. Several computations in this area can nowadays even be automated with the help of computer algebra systems [255, 528, 628].

Je n’ai jamais e´ t´e assez loin pour bien sentir l’application de l’alg`ebre a` la g´eom´etrie. Je n’aimais point cette mani`ere d’op´erer sans voir ce qu’on fait, et il me sembloit que r´esoudre un probl`eme de g´eom´etrie par les e´ quations, c’´etoit jouer un air en tournant une manivelle. (“I never went far enough to get a good feel for the application of algebra to geometry. I was not pleased with this method of operating according to the rules without seeing what one does; solving geometrical problems by means of equations seemed like playing a tune by turning a crank.”)

— J EAN -JACQUES ROUSSEAU, Les Confessions, Livre VI

Part B

COMPLEX ASYMPTOTICS

IV

Complex Analysis, Rational and Meromorphic Asymptotics Entre deux v´erit´es du domaine r´eel, le chemin le plus facile et le plus court passe bien souvent par le domaine complexe. PAUL PAINLEV E´ [467, p. 2] It has been written that the shortest and best way between two truths of the real domain often passes through the imaginary one1. — JACQUES H ADAMARD [316, p. 123]

IV. 1. IV. 2. IV. 3. IV. 4. IV. 5. IV. 6. IV. 7. IV. 8.

Generating functions as analytic objects Analytic functions and meromorphic functions Singularities and exponential growth of coefficients Closure properties and computable bounds Rational and meromorphic functions Localization of singularities Singularities and functional equations Perspective

225 229 238 249 255 263 275 286

Generating functions are a central concept of combinatorial theory. In Part A, we have treated them as formal objects; that is, as formal power series. Indeed, the major theme of Chapters I–III has been to demonstrate how the algebraic structure of generating functions directly reflects the structure of combinatorial classes. From now on, we examine generating functions in the light of analysis. This point of view involves assigning values to the variables that appear in generating functions. Comparatively little benefit results from assigning only real values to the variable z that figures in a univariate generating function. In contrast, assigning complex values turns out to have serendipitous consequences. When we do so, a generating function becomes a geometric transformation of the complex plane. This transformation is very regular near the origin—one says that it is analytic (or holomorphic). In other words, near 0, it only effects a smooth distortion of the complex plane. Farther away from the origin, some cracks start appearing in the picture. These cracks—the dignified name is singularities—correspond to the disappearance of smoothness. It turns out that a function’s singularities provide a wealth of information regarding the function’s coefficients, and especially their asymptotic rate of growth. Adopting a geometric point of view for generating functions has a large pay-off. 1Hadamard’s quotation (1945) is a free rendering of the original one due to Painlev´e (1900); namely, “The shortest and easiest path betwen two truths of the real domain most often passes through the complex domain.”

223

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IV. COMPLEX ANALYSIS, RATIONAL AND MEROMORPHIC ASYMPTOTICS

By focusing on singularities, analytic combinatorics treads in the steps of many respectable older areas of mathematics. For instance, Euler recognized that for the Riemann zeta function ζ (s) to become infinite (hence have a singularity) at 1 implies the existence of infinitely many prime numbers; Riemann, Hadamard, and de la Vall´ee-Poussin later uncovered deep connections between quantitative properties of prime numbers and singularities of 1/ζ (s). The purpose of this chapter is largely to serve as an accessible introduction or a refresher of basic notions regarding analytic functions. We start by recalling the elementary theory of functions and their singularities in a style tuned to the needs of analytic combinatorics. Cauchy’s integral formula expresses coefficients of analytic functions as contour integrals. Suitable uses of Cauchy’s integral formula then make it possible to estimate such coefficients by suitably selecting an appropriate contour of integration. For the common case of functions that have singularities at a finite distance, the exponential growth formula relates the location of the singularities closest to the origin—these are also known as dominant singularities—to the exponential order of growth of coefficients. The nature of these singularities then dictates the fine structure of the asymptotics of the function’s coefficients, especially the subexponential factors involved. As regards generating functions, combinatorial enumeration problems can be broadly categorized according to a hierarchy of increasing structural complexity. At the most basic level, we encounter scattered classes, which are simple enough, so that the associated generating function and coefficients can be made explicit. (Examples of Part A include binary and general plane trees, Cayley trees, derangements, mappings, and set partitions). In that case, elementary real-analysis techniques usually suffice to estimate asymptotically counting sequences. At the next, intermediate, level, the generating function is still explicit, but its form is such that no simple expression is available for coefficients. This is where the theory developed in this and the next chapters comes into play. It usually suffices to have an expression for a generating function, but not necessarily its coefficients, so as to be able to deduce precise asymptotic estimates of its coefficients. (Surjections, generalized derangements, unary–binary trees are easily subjected to this method. A striking example, that of trains, is detailed in Section IV. 4.) Properties of analytic functions then make this analysis depend only on local properties of the generating function at a few points, its dominant singularities. The third, highest, level, within the perspective of analytic combinatorics, comprises generating functions that can no longer be made explicit, but are only determined by a functional equation. This covers structures defined recursively or implicitly by means of the basic constructors of Part A. The analytic approach even applies to a large number of such cases. (Examples include simple families of trees, balanced trees, and the enumeration of certain molecules treated at the end of this chapter. Another characteristic example is that of non-plane unlabelled trees treated in Chapter VII.) As we shall see throughout this book, the analytic methodology applies to almost all the combinatorial classes studied in Part A, which are provided by the symbolic method. In the present chapter we carry out this programme for rational functions and meromorphic functions (i.e., functions whose singularities are poles).

IV. 1. GENERATING FUNCTIONS AS ANALYTIC OBJECTS

225

IV. 1. Generating functions as analytic objects Generating functions, considered in Part A as purely formal objects subject to algebraic operations, are now going to be interpreted as analytic objects. In so doing one gains easy access to the asymptotic form of their coefficients. This informal section offers a glimpse of themes that form the basis of Chapters IV–VII. In order to introduce the subject, let us start with two simple generating functions, one, f (z), being the OGF of the Catalan numbers (cf G(z), p. 35), the other, g(z), being the EGF of derangements (cf D (1) (z), p. 123): √ exp(−z) 1 g(z) = (1) f (z) = 1 − 1 − 4z , . 2 1−z

At this stage, the forms above are merely compact descriptions of formal power series built from the elementary series

1 1 = 1 + y + y2 + · · · , (1 − y)1/2 = 1 − y − y 2 − · · · , 2 8 1 1 exp(y) = 1 + y + y2 + · · · , 1! 2! by standard composition rules. Accordingly, the coefficients of both GFs are known in explicit form: 1 1 (−1)n 1 2n − 2 n n , gn := [z ]g(z) = . − + ··· + f n := [z ] f (z) = n n−1 0! 1! n! (1 − y)−1

Stirling’s formula and the comparison with the alternating series giving exp(−1) provide, respectively, (2)

4n−1 fn ∼ √ , n→∞ π n 3

. gn = ∼ e−1 = 0.36787. n→∞

Our purpose now is to provide intuition on how such approximations could be derived without appealing to explicit forms. We thus examine, heuristically for the moment, the direct relationship between the asymptotic forms (2) and the structure of the corresponding generating functions in (1). Granted the growth estimates available for f n and gn , it is legitimate to substitute in the power series expansions of the GFs f (z) and g(z) any real or complex value of a small enough modulus, the upper bounds on modulus being ρ f = 1/4 (for f ) and ρg = 1 (for g). Figure IV.1 represents the graph of the resulting functions when such real values are assigned to z. The graphs are smooth, representing functions that are differentiable any number of times for z interior to the interval (−ρ, +ρ). However, at the right boundary point, smoothness stops: g(z) become infinite at z = 1, and so it even ceases to be finitely defined; f (z) does tend to the limit 21 as z → ( 41 )− , but its derivative becomes infinite there. Such special points at which smoothness stops are called singularities, a term that will acquire a precise meaning in the next sections. Observe also that, in spite of the series expressions being divergent outside the specified intervals, the functions f (z) and g(z) can be continued in certain regions: it

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0.5 3

0.4 0.3 2

0.2 0.1 –0.2

–0.1 0 –0.1 –0.2

1

0.1 z

0.2

–1

0

z

1

Figure IV.1. Left: the graph of the Catalan OGF, f (z), for z ∈ (− 41 , + 14 ); right: the graph of the derangement EGF, g(z), for z ∈ (−1, +1).

√ suffices to make use of the global expressions of Equation (1), with exp and being assigned their usual real-analytic interpretation. For instance: √ 1 e2 1− 5 , f (−1) = g(−2) = . 2 3 Such continuation properties, most notably to the complex realm, will prove essential in developing efficient methods for coefficient asymptotics. One may proceed similarly with complex numbers, starting with numbers whose modulus is less than the radius of convergence of the series defining the GF. Figure IV.2 displays the images of regular grids by f and g, as given by (1). This illustrates the fact that a regular grid is transformed into an orthogonal network of curves and more precisely that f and g preserve angles—this property corresponds to complex differentiability and is equivalent to analyticity to be introduced shortly. The singularity of f is clearly perceptible on the right of its diagram, since, at z = 1/4 (corresponding to f (z) = 1/2), the function f folds lines and divides angles by a factor of 2. The singularity of g at z = 1 is indirectly perceptible from the fact that g(z) → ∞ as z → 1 (the square grid had to be truncated at z = 0.75, since this book can only accommodate finite graphs). Let us now turn to coefficient asymptotics. As is expressed by (2), the coefficients f n and gn each belong to a general asymptotic type for coefficients of a function F, namely, (3)

[z n ]F(z) = An θ (n),

corresponding to an exponential growth factor An modulated by a tame factor θ (n), which is subexponential. Here, one has A = 4 for f n and A = 1 for gn ; also, √ 1 −1 3 θ (n) ∼ 4 ( π n ) for f n and θ (n) ∼ e−1 for gn . Clearly, A should be related to the radius of convergence of the series. We shall see that, invariably, for combinatorial generating functions, the exponential rate of growth is given by A = 1/ρ, where ρ is the first singularity encountered along the positive real axis (Theorem IV.6,

IV. 1. GENERATING FUNCTIONS AS ANALYTIC OBJECTS 0.3

0.3

0.2

0.2

0.1 0

227

0.1 0.1

0

0.2

–0.1

–0.1

–0.2

–0.2

–0.3

0.1 0.2 0.3 0.4 0.5 0.6 0.7

–0.3

0.4

0.6 0.4

0.2 0.2

0

0

0.1 0.2 0.3 0.4 0.5

1

1.2

1.4

1.6

1.8

–0.2 –0.4

–0.2

–0.6

–0.4

Figure IV.2. The images of regular grids by f (z) (left) and g(z) (right).

p. 240). In addition, under general complex analytic conditions, it will be established that θ (n) = O(1) is systematically associated to a simple pole of the generating function (Theorem IV.10, p. 258), while θ (n) = O(n −3/2 ) systematically arises from a singularity that is of the square-root type (Chapters VI and VII). We enunciate: First Principle of Coefficient Asymptotics. The location of a function’s singularities dictates the exponential growth (An ) of its coefficients. Second Principle of Coefficient Asymptotics. The nature of a function’s singularities determines the associate subexponential factor (θ (n)). Observe that the rescaling rule, [z n ]F(z) = ρ −n [z n ]F(ρz), enables one to normalize functions so that they are singular at 1. Then, various theorems, starting with Theorems IV.9 and IV.10, provide sufficient conditions under which the following fundamental implication is valid, (4)

h(z) ∼ σ (z)

H⇒

[z n ]h(z) ∼ [z n ]σ (z).

There h(z), whose coefficients are to be estimated, is a function singular at 1 and σ (z) is a local approximation near the singularity; usually σ is a much simpler function, typically like (1 − z)α logβ (1 − z) whose coefficients are comparatively easy to estimate (Chapter VI). The relation (4) expresses a mapping between asymptotic scales of functions near singularities and asymptotics scales of coefficients. Under suitable conditions, it then suffices to estimate a function locally at a few special points (singularities), in order to estimate its coefficients asymptotically.

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A succinct roadmap. Here is what now awaits the reader. Section IV. 2 serves to introduce basic notions of complex function theory. Singularities and exponential growth of coefficients are examined in Section IV. 3, which justifies the First Principle. Next, in Section IV. 4, we establish the computability of exponential growth rates for all the non-recursive structures that are specifiable. Section IV. 5 presents two important theorems that deal with rational and meromorphic functions and illustrate the Second Principle, in its simplest version (the subexponential factors are merely polynomials). Then, Section IV. 6 examines constructively ways to locate singularities and treats in detail the case of patterns in words. Finally, Section IV. 7 shows how functions only known through a functional equation may be accessible to complex asymptotic methods.

IV.1. Euler, the discrete, and the continuous. Eulers’s proof of the existence of infinitely many prime numbers illustrates in a striking manner the way analysis of generating functions can inform us on the discrete realm. Define, for real s > 1 the function ζ (s) :=

∞ X 1 , ns

n=1

known as the Riemann zeta function. The decomposition ( p ranges over the prime numbers 2, 3, 5, . . .) 1 1 1 1 1 1 ζ (s) = 1 + s + 2s + · · · 1 + s + 2s + · · · 1 + s + 2s + · · · · · · 2 3 5 2 3 5 (5) Y 1 −1 1− s = p p expresses precisely the fact that each integer has a unique decomposition as a product of primes. Analytically, the identity (5) is easily checked to be valid for all s > 1. Now suppose that there were only finitely many primes. Let s tend to 1+ in (5). Then, the left-hand side becomes Q infinite, while the right-hand side tends to the finite limit p (1 − 1/ p)−1 : a contradiction has been reached.

IV.2. Elementary transfers. Elementary series manipulation yield the following general result: Let h(z) be a power series with radius of convergence > 1 and assume that h(1) 6= 0; then one has √ h(z) h(1) h(1) 1 [z n ] ∼ h(1), [z n ]h(z) 1 − z ∼ − √ , [z n ]h(z) log ∼ . 3 1−z 1 − z n 2 πn

See our discussion on p. 434 and Bender’s survey [36] for many similar statements, of which this chapter and Chapter VI provide many far-reaching extensions.

IV.3. Asymptotics of generalized derangements. The EGF of permutations without cycles of length 1 and 2 satisfies (p. 123) 2

j (z) =

e−z−z /2 1−z

with

e−3/2 . z→1 1 − z

j (z) ∼

Analogy with derangements suggests that [z n ] j (z) ∼ e−3/2 . [For a proof, use Note IV.2 or n→∞

refer to Example IV.9 below, p. 261.] Here is a table of exact values of [z n ] j (z) (with relative error of the approximation by e−3/2 in parentheses): jn : error :

n=5 0.2 (10−1 )

n = 10 0.22317 (2 · 10−4 )

n = 20 0.2231301600 (3 · 10−10 )

n = 50 0.2231301601484298289332804707640122 (10−33 )

IV. 2. ANALYTIC AND MEROMORPHIC FUNCTIONS

229

The quality of the asymptotic approximation is extremely good, such a property being, as we shall see, invariably attached to polar singularities.

IV. 2. Analytic functions and meromorphic functions Analytic functions are a primary mathematical concept of asymptotic theory. They can be characterized in two essentially equivalent ways (see Subsection IV. 2.1): by means of convergent series expansions (`a la Cauchy and Weierstrass) and by differentiability properties (`a la Riemann). The first aspect is directly related to the use of generating functions for enumeration; the second one allows for a powerful abstract discussion of closure properties that usually requires little computation. Integral calculus with analytic functions (see Subsection IV. 2.2) assumes a shape radically different from that which prevails in the real domain: integrals become quintessentially independent of details of the integration contour—certainly the prime example of this fact is Cauchy’s famous residue theorem. Conceptually, this independence makes it possible to relate properties of a function at a point (e.g., the coefficients of its expansion at 0) to its properties at another far-away point (e.g., its residue at a pole). The presentation in this section and the next one constitutes an informal review of basic properties of analytic functions tuned to the needs of asymptotic analysis of counting sequences. The entry in Appendix B.2: Equivalent definitions of analyticity, p. 741, provides further information, in particular a proof of the Basic Equivalence Theorem, Theorem IV.1 below. For a detailed treatment, we refer the reader to one of the many excellent treatises on the subject, such as the books by Dieudonn´e [165], Henrici [329], Hille [334], Knopp [373], Titchmarsh [577], or Whittaker and Watson [604]. The reader previously unfamiliar with the theory of analytic functions should essentially be able to adopt Theorems IV.1 and IV.2 as “axioms” and start from here using basic definitions and a fair knowledge of elementary calculus. Figure IV.19 at the end of this chapter (p. 287) recapitulates the main results of relevance to Analytic Combinatorics. IV. 2.1. Basics. We shall consider functions defined in certain regions of the complex domain C. By a region is meant an open subset of the complex plane that is connected. Here are some examples:

simply connected domain

slit complex plane

indented disc

annulus.

Classical treatises teach us how to extend to the complex domain the standard functions of real analysis: polynomials are immediately extended as soon as complex addition and multiplication have been defined, while the exponential is definable by means of Euler’s formula. One has for instance z 2 = (x 2 − y 2 ) + 2i x y,

e z = e x cos y + ie x sin y,

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if z = x + i y, that is, x = ℜ(z) and y = ℑ(z) are the real and imaginary parts of z. Both functions are consequently defined over the whole complex plane C. The square-root and logarithm functions are conveniently described in polar coordinates: √ √ (6) z = ρeiθ/2 , log z = log ρ + iθ,

if z = ρeiθ . One can take the domain of validity of (6) to be the complex plane slit along the axis from 0 to −∞, that is, restrict θ to the open interval (−π, +π ), in which case the definitions above specify what is known as the principal determination. There √ is no way for instance to extend by continuity the definition of z in any domain √ √ containing 0 in its interior since, for a > √ 0 and z → −a, one has z → i a as √ z → −a from above, whereas z → −i a as z → −a from below. This situation is depicted here:

√ +i a √ −i a

0

√ The values of z as z varies along |z| = a.

√ a

The point z = 0, where several determinations “meet”, is accordingly known as a branch point. Analytic functions. First comes the main notion of an analytic function that arises from convergent series expansions and is of obvious relevance to generatingfunctionology. Definition IV.1. A function f (z) defined over a region is analytic at a point z 0 ∈ if, for z in some open disc centred at z 0 and contained in , it is representable by a convergent power series expansion X (7) f (z) = cn (z − z 0 )n . n≥0

A function is analytic in a region iff it is analytic at every point of . As derived from an elementary property of power series (Note IV.4), given a function f that is analytic at a point z 0 , there exists a disc (of possibly infinite radius) with the property that the series representing f (z) is convergent for z inside the disc and divergent for z outside the disc. The disc is called the disc of convergence and its radius is the radius of convergence of f (z) at z = z 0 , which will be denoted by Rconv ( f ; z 0 ). The radius of convergence of a power series conveys basic information regarding the rate at which its coefficients grow; see Subsection IV. 3.2 below for developments. It is also easy to prove by simple series rearrangement that if a function is analytic at z 0 , it is then analytic at all points interior to its disc of convergence (see Appendix B.2: Equivalent definitions of analyticity, p. 741). P

f n z n be a power series. n Define R as the supremum of all values of x ≥ 0 such that { f n x } is bounded. Then, for

IV.4. The disc of convergence of a power series. Let f (z) =

IV. 2. ANALYTIC AND MEROMORPHIC FUNCTIONS

231

|z| < R, the sequence f n z n tends geometrically to 0; hence f (z) is convergent. For |z| > R, the sequence f n z n is unbounded; hence f (z) is divergent. In short: a power series converges in the interior of a disc; it diverges in its exterior.

Consider for instance the function f (z) = 1/(1 − z) defined over C \ {1} in the usual way via complex division. It is analytic at 0 by virtue of the geometric series sum, X 1 = 1 · zn , 1−z n≥0

which converges in the disc |z| < 1. At a point z 0 6= 1, we may write 1 1 1 1 = = z−z 0 1−z 1 − z 0 − (z − z 0 ) 1 − z 0 1 − 1−z 0 (8) n+1 X 1 n (z − z 0 ) . = 1 − z0 n≥0

The last equation shows that f (z) is analytic in the disc centred at z 0 with radius |1 − z 0 |, that is, the interior of the circle centred at z 0 and passing through the point 1. In particular Rconv ( f, z 0 ) = |1 − z 0 | and f (z) is globally analytic in the punctured plane C \ {1}. The example of (1 − z)−1 illustrates the definition of analyticity. However, the series rearrangement approach that it uses might be difficult to carry out for more complicated functions. In other words, a more manageable approach to analyticity is called for. The differentiability properties developed now provide such an approach. Differentiable (holomorphic) functions. The next important notion is a geometric one based on differentiability. Definition IV.2. A function f (z) defined over a region is called complex-differentiable (also holomorphic) at z 0 if the limit, for complex δ, f (z 0 + δ) − f (z 0 ) lim δ→0 δ exists. (In particular, the limit is independent of the way δ tends to 0 in C.) This d limit is denoted as usual by f ′ (z 0 ), or dz f (z) , or ∂z f (z 0 ). A function is complexz0

differentiable in iff it is complex-differentiable at every z 0 ∈ .

From the definition, if f (z) is complex-differentiable at z 0 and f ′ (z 0 ) 6= 0, it acts locally as a linear transformation: f (z) − f (z 0 ) = f ′ (z 0 )(z − z 0 ) + o(z − z 0 )

(z → z 0 ).

Then, f (z) behaves in small regions almost like a similarity transformation (composed of a translation, a rotation, and a scaling). In particular, it preserves angles2 and infinitesimal squares get transformed into infinitesimal squares; see Figure IV.3 for a rendering. Further aspects of the local shape of an analytic function will be examined in Section VIII. 1, p. 543, in relation with the saddle-point method. 2A mapping of the plane that locally preserves angles is also called a conformal map. Section VIII. 1

(p. 543) presents further properties of the local “shape” of an analytic function.

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8 6 4 2 0

2

4

6

8

10

–2 –4 –6 –8

1.5 10

1

8

0.5

6

0

4

–0.5 –1

2 0

2 2

1 1

0 y –1

0x –1 –2–2

2

–1.5 2

1 1

0

0x y –1

–1 –2–2

Figure IV.3. Multiple views of an analytic function. The image of the domain = {z |ℜ(z)| < 2, |ℑ(z)| < 2} by f (z) = exp(z) + z + 2: [top] transformation of a square grid in by f ; [bottom] the modulus and argument of f (z).

√ For instance the function z, defined by (6) in the complex plane slit along the ray (−∞, 0), is complex-differentiable at any z 0 of the slit plane since √ √ √ z0 + δ − z0 √ 1 1 + δ/z 0 − 1 = lim z 0 = √ , (9) lim δ→0 δ→0 δ δ 2 z0 √ which extends the customary proof of real analysis. Similarly, 1 − z is complexdifferentiable in the complex plane slit along the ray (1, +∞). More generally, the usual proofs from real analysis carry over almost verbatim to the complex realm, to the effect that ′ f′ 1 ′ ′ ′ ′ ′ ′ = − 2 , ( f ◦ g)′ = ( f ′ ◦ g)g ′ . ( f + g) = f + g , ( f g) = f g + f g , f f

The notion of complex differentiability is thus much more manageable than the notion of analyticity. It follows from a well known theorem of Riemann (see for instance [329, vol. 1, p 143] and Appendix B.2: Equivalent definitions of analyticity, p. 741) that analyticity and complex differentiability are equivalent notions. Theorem IV.1 (Basic Equivalence Theorem). A function is analytic in a region if and only if it is complex-differentiable in .

The following are known facts (see p. 236 and Appendix B): (i) if a function is analytic (equivalently complex-differentiable) in , it admits (complex) derivatives of any order there—this property markedly differs from real analysis: complexdifferentiable, equivalently analytic, functions are all smooth; (ii) derivatives of a

IV. 2. ANALYTIC AND MEROMORPHIC FUNCTIONS

233

function may be obtained through term-by-term differentiation of the series representation of the function. Meromorphic functions. We finally introduce meromorphic3 functions that are mild extensions of the concept of analyticity (or holomorphy) and are essential to the theory. The quotient of two analytic functions f (z)/g(z) ceases to be analytic at a point a where g(a) = 0; however, a simple structure for quotients of analytic functions prevails. Definition IV.3. A function h(z) is meromorphic at z 0 iff, for z in a neighbourhood of z 0 with z 6= z 0 , it can be represented as f (z)/g(z), with f (z) and g(z) being analytic at z 0 . In that case, it admits near z 0 an expansion of the form X (10) h(z) = h n (z − z 0 )n . n≥−M

If h −M 6= 0 and M ≥ 1, then h(z) is said to have a pole of order M at z = z 0 . The coefficient h −1 is called the residue of h(z) at z = z 0 and is written as Res[h(z); z = z 0 ]. A function is meromorphic in a region iff it is meromorphic at every point of the region. IV. 2.2. Integrals and residues. A path in a region is described by its parameterization, which is a continuous function γ mapping [0, 1] into . Two paths γ , γ ′ in that have the same end points are said to be homotopic (in ) if one can be continuously deformed into the other while staying within as in the following examples:

homotopic paths:

A closed path is defined by the fact that its end points coincide: γ (0) = γ (1), and a path is simple if the mapping γ is one-to-one. A closed path is said to be a loop of if it can be continuously deformed within to a single point; in this case one also says that the path is homotopic to 0. In what follows paths are taken to be piecewise continuously differentiable and, by default, loops are oriented positively. Integrals along curves in the complex plane are defined in the usual way as curvilinear integrals of complex-valued functions. Explicitly: let f (x + i y) be a function 3“Holomorphic” and “meromorphic” are words coming from Greek, meaning, respectively, “of com-

plete form” and “of partial form”.

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IV. COMPLEX ANALYSIS, RATIONAL AND MEROMORPHIC ASYMPTOTICS

and γ be a path; then, Z f (z) dz

:=

γ

=

Z

1

Z0 1 0

f (γ (t))γ ′ (t) dt [AC − B D] dt + i

Z

1 0

[AD + BC] dt,

where f ◦ γ = A + i B and γ ′ = C + i D. However, integral calculus in the complex plane greatly differs from its form on the real line—in many ways, it is much simpler and much more powerful. One has: Theorem IV.2 (Null Integral R Property). Let f be analytic in and let λ be a simple loop of . Then, one has λ f = 0. Equivalently, integrals are largely independent of details of contours: for f analytic in , one has Z Z (11) f = f, γ

γ′

provided γ and γ ′ are homotopic (not necessarily closed) paths in . A proof of Theorem IV.2 is sketched in Appendix B.2: Equivalent definitions of analyticity, p. 741. Residues. The important Residue Theorem due to Cauchy relates global properties of a meromorphic function (its integral along closed curves) to purely local characteristics at designated points (its residues at poles). Theorem IV.3 (Cauchy’s residue theorem). Let h(z) be meromorphic in the region and let λ be a positively oriented simple loop in along which the function is analytic. Then Z X 1 h(z) dz = Res[h(z); z = s], 2iπ λ s where the sum is extended to all poles s of h(z) enclosed by λ.

Proof. (Sketch) To see it in the representative case where h(z) has only a pole at z = 0, observe by appealing to primitive functions that n+1 Z Z X dz z + h −1 , h(z) dz = hn n+1 λ λ z λ n≥−M n6=−1

where the bracket notation u(z) λ designates the variation of the function u(z) along the contour λ. This expression reduces to its last term, itself equal to 2iπ h −1 , as is checked by using integration along a circle (set z = r eiθ ). The computation extends by translation to the case of a unique pole at z = a. Next, in the case of multiple poles, we observe that the simple loop can only enclose finitely many poles (by compactness). The proof then follows from a simple decomposition of the interior domain of λ into cells, each containing only one pole. Here is an illustration in the case of three poles.

IV. 2. ANALYTIC AND MEROMORPHIC FUNCTIONS

235

(Contributions from internal edges cancel.)

Global (integral) to local (residues) connections. Here is a textbook example of a reduction from global to local properties of analytic functions. Define the integrals Z ∞ dx Im := , 1 + x 2m −∞ and consider specifically I1 . Elementary calculus teaches us that I1 = π since the antiderivative of the integrand is an arc tangent: Z ∞ dx = [arctan x]+∞ I1 = −∞ = π. 2 −∞ 1 + x

Here is an alternative, and in many ways more fruitful, derivation. In the light of the residue theorem, we consider the integral over the whole line as the limit of integrals over large intervals of the form [−R, +R], then complete the contour of integration by means of a large semi-circle in the upper half-plane, as shown below:

11 00 00 11 00 i 11

−R

0

+R

Let γ be the contour comprised of the interval and the semi-circle. Inside γ , the integrand has a pole at x = i, where 1 i 1 1 =− + ··· , ≡ (x + i)(x − i) 2x −i 1 + x2 so that its residue there is −i/2. By the residue theorem, the integral taken over γ is equal to 2iπ times the residue of the integrand at i. As R → ∞, the integral along the semi-circle vanishes (it is less than π R/(R 2 − 1) in modulus), while the integral along the real segment gives I1 in the limit. There results the relation giving I1 : i 1 ; x = i = (2iπ ) − I1 = 2iπ Res = π. 2 1 + x2

The evaluation of the integral in the framework of complex analysis rests solely upon the local expansion of the integrand at special points (here, the point i). This is a remarkable feature of the theory, one that confers it much simplicity, when compared with real analysis.

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IV. COMPLEX ANALYSIS, RATIONAL AND MEROMORPHIC ASYMPTOTICS

iπ ) so that α 2m = −1. Contour integration of IV.5. The general integral Im . Let α = exp( 2m the type used for I1 yields m X 1 2 j−1 , Im = 2iπ Res ; x = α 1 + x 2m j=1

while, for any β = α 2 j−1 with 1 ≤ j ≤ m, one has

As a consequence,

1 1 1 β 1 ∼ ≡− . 2m 2m−1 x −β 2m x − β x→β 2mβ 1+x

π iπ α + α 3 + · · · + α 2m−1 = π . m m sin 2m √ p √ √ In particular, I2 = π/ 2, I3 = 2π/3, I4 = π4 2 2 + 2, and π1 I5 , π1 I6 are expressible by radicals, but π1 I7 , π1 I9 are not. The special cases π1 I17 , π1 I257 are expressible by radicals. I2m = −

IV.6. Integrals of rational fractions. Generally, all integrals of rational functions taken over the whole real line are computable by residues. In particular, Z +∞ Z +∞ dx dx , K = Jm = m 2 m 2 2 2 (1 + x ) (1 + x )(2 + x 2 ) · · · (m 2 + x 2 ) −∞ −∞

can be explicitly evaluated.

Cauchy’s coefficient formula. Many function-theoretic consequences are derived from the residue theorem. For instance, if f is analytic in , z 0 ∈ , and λ is a simple loop of encircling z 0 , one has Z 1 dζ (12) f (z 0 ) = . f (ζ ) 2iπ λ ζ − z0 This follows directly since

Res [ f (ζ )/(ζ − z 0 ); ζ = z 0 ] = f (z 0 ). Then, by differentiation with respect to z 0 under the integral sign, one has similarly Z 1 dζ 1 (k) f (z 0 ) = f (ζ ) . (13) k! 2iπ λ (ζ − z 0 )k+1

The values of a function and its derivatives at a point can thus be obtained as values of integrals of the function away from that point. The world of analytic functions is a very friendly one in which to live: contrary to real analysis, a function is differentiable any number of times as soon as it is differentiable once. Also, Taylor’s formula invariably holds: as soon as f (z) is analytic at z 0 , one has 1 ′′ f (z 0 )(z − z 0 )2 + · · · , 2! with the representation being convergent in a disc centred at z 0 . [Proof: a verification from (12) and (13), or a series rearrangement as in Appendix B, p. 742.]

(14)

f (z) = f (z 0 ) + f ′ (z 0 )(z − z 0 ) +

A very important application of the residue theorem concerns coefficients of analytic functions.

IV. 2. ANALYTIC AND MEROMORPHIC FUNCTIONS

237

Theorem IV.4 (Cauchy’s Coefficient Formula). Let f (z) be analytic in a region containing 0 and let λ be a simple loop around 0 in that is positively oriented. Then, the coefficient [z n ] f (z) admits the integral representation Z 1 dz f n ≡ [z n ] f (z) = f (z) n+1 . 2iπ λ z Proof. This formula follows directly from the equalities Z h i 1 dz f (z) n+1 = Res f (z)z −n−1 ; z = 0 = [z n ] f (z), 2iπ λ z of which the first one follows from the residue theorem, and the second one from the identification of the residue at 0 as a coefficient. Analytically, the coefficient formula allows us to deduce information about the coefficients from the values of the function itself, using adequately chosen contours of integration. It thus opens the possibility of estimating the coefficients [z n ] f (z) in the expansion of f (z) near 0 by using information on f (z) away from 0. The rest of this chapter will precisely illustrate this process in the case of rational and meromorphic functions. Observe also that the residue theorem provides the simplest proof of the Lagrange inversion theorem (see Appendix A.6: Lagrange Inversion, p. 732) whose rˆole is central to tree enumerations, as we saw in Chapters I and II. The notes below explore some independent consequences of the residue theorem and the coefficient formula.

IV.7. Liouville’s Theorem. If a function f (z) is analytic in the whole of C and is of modulus bounded by an absolute constant, | f (z)| ≤ B, then it must be a constant. [By trivial bounds, upon integrating on a large circle, it is found that the Taylor coefficients at the origin of index ≥ 1 are all equal to 0.] Similarly, if f (z) is of at most polynomial growth, | f (z)| ≤ B (|z|+1)r , over the whole of C, then it must be a polynomial.

IV.8. Lindel¨of integrals. Let a(s) be analytic in ℜ(s) > 14 where it is assumed to satisfy a(s) = O(exp((π − δ)|s|)) for some δ with 0 < δ < π . Then, one has for | arg(z)| < δ, Z 1/2+i∞ ∞ X 1 π a(k)(−z)k = − a(s)z s ds, 2iπ 1/2−i∞ sin π s k=1

in the sense that the integral exists and provides the analytic continuation of the sum in | arg(z)| < δ. [Close the integration contour by a large semi-circle on the right and evaluate by residues.] Such integrals, sometimes called Lindel¨of integrals, provide representations for many functions whose Taylor coefficients are given by an explicit rule [268, 408].

IV.9. Continuation of polylogarithms. As a consequence of Lindel¨of’s representation, the generalized polylogarithm functions, X Liα,k (z) = n −α (log n)k z n n≥1

(α ∈ R,

k ∈ Z≥0 ),

are analytic in the complex plane C slit along (1+, ∞). (More properties are presented in Section VI. 8, p. 408; see also [223, 268].) For instance, one obtains in this way r Z ∞ 1 X π 1 +∞ log( 4 + t 2 ) dt = 0.22579 · · · = log , “ (−1)n log n ” = − 4 −∞ cosh(π t) 2 n=1

when the divergent series on the left is interpreted as Li0,1 (−1) = limz→−1+ Li0,1 (z).

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IV. COMPLEX ANALYSIS, RATIONAL AND MEROMORPHIC ASYMPTOTICS

IV.10. Magic duality. Let φ be a function initially defined over the non-negative integers but admitting a meromorphic extension over the whole of C. Under growth conditions in the style of Note IV.8, the function X F(z) := φ(n)(−z)n , n≥1

which is analytic at the origin, is such that, near positive infinity, X F(z) ∼ E(z) − φ(−n)(−z)−n , z→+∞

n≥1

for some elementary function E(z), which is a linear combination of terms of the form z α (log z)k . [Starting from the representation of Note IV.8, close the contour of integration by a large semicircle to the left.] In such cases, the function is said to satisfy the principle of magic duality—its expansion at 0 and ∞ are given by one and the same rule. Functions 1 , log(1 + z), exp(−z), Li2 (−z), Li3 (−z), 1+z satisfy a form of magic duality. Ramanujan [52] made a great use of this principle, which applies to a wide class of functions including hypergeometric ones; see Hardy’s insightful discussion [321, Ch XI].

IV.11. Euler–Maclaurin and Abel–Plana summations. Under simple conditions on the analytic function f , one has Plana’s (also known as Abel’s) complex variables version of the Euler– Maclaurin summation formula: Z ∞ Z ∞ ∞ X 1 f (i y) − f (−i y) dy. f (n) = f (0) + f (x) d x + 2 e2iπ y − 1 0 0 n=0

(See [330, p. 274] for a proof and validity conditions.)

IV.12. N¨orlund–Rice integrals. Let a(z) be analytic for ℜ(z) > k0 − 21 and of at most polynomial growth in this right half-plane. Then, with γ a simple loop around the interval [k0 , n], one has Z n X n! ds 1 n a(s) . (−1)n−k a(k) = 2iπ γ s(s − 1)(s − 2) · · · (s − n) k k=k0

If a(z) is meromorphic and suitably small in a larger region, then the integral can be estimated by residues. For instance, with n n X X n (−1)k n (−1)k , Tn = Sn = , k k k2 + 1 k k=1

k=1

it is found that Sn = − Hn (a harmonic number), while Tn oscillates boundedly as n → +∞. [This technique is a classical one in the calculus of finite differences, going back to N¨orlund [458]. In computer science it is known as the method of Rice’s integrals [256] and is used in the analysis of many algorithms and data structures including digital trees and radix sort [378, 564].]

IV. 3. Singularities and exponential growth of coefficients For a given function, a singularity can be informally defined as a point where the function ceases to be analytic. (Poles are the simplest type of singularity.) Singularities are, as we have stressed repeatedly, essential to coefficient asymptotics. This section presents the bases of a discussion within the framework of analytic function theory.

IV. 3. SINGULARITIES AND EXPONENTIAL GROWTH OF COEFFICIENTS

239

IV. 3.1. Singularities. Let f (z) be an analytic function defined over the interior region determined by a simple closed curve γ , and let z 0 be a point of the bounding curve γ . If there exists an analytic function f ⋆ (z) defined over some open set ⋆ containing z 0 and such that f ⋆ (z) = f (z) in ⋆ ∩ , one says that f is analytically continuable at z 0 and that f ⋆ is an immediate analytic continuation of f . Pictorially: γ

Analytic continuation:

Ω* Ω

z0 (f)

f ⋆ (z) = f (z) on ⋆ ∩ .

( f* )

Consider for instance the P quasi-inverse function, f (z) = 1/(1 − z). Its power series representation f (z) = n≥0 z n initially converges in |z| < 1. However, the calculation of (8), p. 231, shows that it is representable locally by a convergent series near any point z 0 6= 1. In particular, it is continuable at any point of the unit disc except 1. (Alternatively, one may appeal to complex-differentiability to verify directly that f (z), which is given by a “global” expression, is holomorphic, hence analytic, in the punctured plane C \ {1}.) In sharp contrast with real analysis, where a smooth function admits of uncountably many extensions, analytic continuation is essentially unique: if f ⋆ (in ⋆ ) and f ⋆⋆ (in ⋆⋆ ) continue f at z 0 , then one must have f ⋆ (z) = f ⋆⋆ (z) in the intersection ⋆ ∩ ⋆⋆ , which in particular includes a small disc around z 0 . Thus, the notion of immediate analytic continuation at a boundary point is intrinsic. The process can be iterated and we say that g is an analytic continuation4 of f along a path, even if the domains of definition of f and g do not overlap, provided a finite chain of intermediate function elements connects f and g. This notion is once more intrinsic—this is known as the principle of unicity of analytic continuation (Rudin [523, Ch. 16] provides a thorough discussion). An analytic function is then much like a hologram: as soon as it is specified in any tiny region, it is rigidly determined in any wider region to which it can be continued. Definition IV.4. Given a function f defined in the region interior to the simple closed curve γ , a point z 0 on the boundary (γ ) of the region is a singular point or a singularity5 if f is not analytically continuable at z 0 . Granted the intrinsic character of analytic continuation, we can usually dispense with a detailed description of the original domain and the curve γ . In simple terms, a function is singular at z 0 if it cannot be continued as an analytic function beyond z 0 . A point at which a function is analytic is also called by √ contrast a regular point. The two functions f (z) = 1/(1 − z) and g(z) = 1 − z may be taken as initially defined over the open unit disc by their power series representation. Then, as we already know, they can be analytically continued to larger regions, the punctured plane 4The collection of all function elements continuing a given function gives rise to the notion of Riemann surface, for which many good books exist, e.g., [201, 549]. We shall not need to appeal to this theory. 5For a detailed discussion, see [165, p. 229], [373, vol. 1, p. 82], or [577].

240

IV. COMPLEX ANALYSIS, RATIONAL AND MEROMORPHIC ASYMPTOTICS

= C \ {1} for f [e.g., by the calculation of (8), p. 231] and the complex plane slit along (1, +∞) for g [e.g., by virtue of continuity and differentiability as in (9), p. 232]. But both are singular at 1: for f , this results (say) from the fact that f (z) → ∞ as z → 1; for g this is due to the branching character of the square-root. Figure IV.4 displays a few types of singularities that are traceable by the way they deform a regular grid near a boundary point. A converging power series is analytic inside its disc of convergence; in other words, it can have no singularity inside this disc. However, it must have at least one singularity on the boundary of the disc, as asserted by the theorem below. In addition, a classical theorem, called Pringsheim’s theorem, provides a refinement of this property in the case of functions with non-negative coefficients, which happens to include all counting generating functions. Theorem IV.5 (Boundary singularities). A function f (z) analytic at the origin, whose expansion at the origin has a finite radius of convergence R, necessarily has a singularity on the boundary of its disc of convergence, |z| = R. Proof. Consider the expansion (15)

f (z) =

X

fn zn ,

n≥0

assumed to have radius of convergence exactly R. We already know that there can be no singularity of f within the disc |z| < R. To prove that there is a singularity on |z| = R, suppose a contrario that f (z) is analytic in the disc |z| < ρ for some ρ satisfying ρ > R. By Cauchy’s coefficient formula (Theorem IV.4, p. 237), upon integrating along the circle of radius r = (R + ρ)/2, and by trivial bounds, it is seen that the coefficient [z n ] f (z) is O(r −n ). But then, the series expansion of f would have to converge in the disc of radius r > R, a contradiction. Pringsheim’s Theorem stated and proved now is a refinement of Theorem IV.5 that applies to all series having non-negative coefficients, in particular, generating functions. It is central to asymptotic enumeration, as the remainder of this section will amply demonstrate. Theorem IV.6 (Pringsheim’s Theorem). If f (z) is representable at the origin by a series expansion that has non-negative coefficients and radius of convergence R, then the point z = R is a singularity of f (z).

IV.13. Proof of Pringsheim’s Theorem. (See also [577, Sec. 7.21].) In a nutshell, the idea

of the proof is that if f has positive coefficients and is analytic at R, then its expansion slightly to the left of R has positive coefficients. Then, the power series of f would converge in a disc larger than the postulated disc of convergence—a clear contradiction. Suppose then a contrario that f (z) is analytic at R, implying that it is analytic in a disc of radius r centred at R. We choose a number h such that 0 < h < 31 r and consider the expansion of f (z) around z 0 = R − h: X (16) f (z) = gm (z − z 0 )m . m≥0

IV. 3. SINGULARITIES AND EXPONENTIAL GROWTH OF COEFFICIENTS

f 0 (z) = 1.5

1 1−z

241

f 1 (z) = e z/(1−z) 4

1

2

0.5

0

0.5

1

1.5

2

2.5

0

3

-0.5

0

4

2

6

8

10

-2

-1 -4 -1.5

√ f 2 (z) = − 1 − z

f 3 (z) = −(1 − z)3/2 2

0.6

0.4 1 0.2

-1.4

-1.2

-1

-0.8

-0.6

-0.4

-0.2

0

0

-2.5

-2

-1.5

-1

-0.5

0

0

0.5

-0.2 -1 -0.4

-0.6 -2

f 4 (z) = log 1.5

1 1−z

1

0.5

0

0

1

2

3

4

-0.5

-1

-1.5

Figure IV.4. The images of a grid on the unit square (with corners ±1±i) by various functions singular at z = 1 reflect the nature of the singularities involved. Singularities are apparent near the right of each diagram where small grid squares get folded or unfolded in various ways. (In the case of functions f 0 , f 1 , f 4 that become infinite at z = 1, the grid has been slightly truncated to the right.)

242

IV. COMPLEX ANALYSIS, RATIONAL AND MEROMORPHIC ASYMPTOTICS

By Taylor’s formula and the representability of f (z) together with its derivatives at z 0 by means of (15), we have X n gm = f n z 0n−m , m n≥0

and in particular, gm ≥ 0. Given the way h was chosen, the series (16) converges at z = R + h (so that z − z 0 = 2h) as illustrated by the following diagram:

R

r

2h

R+h R z0 = R − h

Consequently, one has f (R + h) =

X

m≥0

X n

n≥0

m

f n z 0m−n (2h)m .

This is a converging double sum of positive terms, so that the sum can be reorganized in any way we like. In particular, one has convergence of all the series involved in X n f (R + h) = f n (R − h)m−n (2h)m m m,n≥0 X = f n [(R − h) + (2h)]n =

n≥0 X

n≥0

f n (R + h)n .

This establishes the fact that f n = o((R + h)−n ), thereby reaching a contradiction with the assumption that the series representation of f has radius of convergence exactly R. Pringsheim’s theorem is proved.

Singularities of a function analytic at 0, which lie on the boundary of the disc of convergence, are called dominant singularities. Pringsheim’s theorem appreciably simplifies the search for dominant singularities of combinatorial generating functions since these have non-negative coefficients—it is sufficient to investigate analyticity along the positive real line and detect the first place at which it ceases to hold. Example IV.1. Some combinatorial singularities. The derangement and the surjection EGFs, D(z) =

e−z , 1−z

R(z) = (2 − e z )−1

are analytic, except for a simple pole at z = 1 in the case of D(z), and for points χk = log 2 + 2ikπ that are simple poles in the case of R(z). Thus the dominant singularities for derangements and surjections are at 1 and log 2, respectively.

IV. 3. SINGULARITIES AND EXPONENTIAL GROWTH OF COEFFICIENTS

243

√ It is known that Z cannot be unambiguously defined as an analytic function in a neighbourhood of Z = 0. As a consequence, the function √ 1 − 1 − 4z G(z) = , 2 which is the generating function of general Catalan trees, is an analytic function in regions that must exclude 1/4; for instance, one may take the complex plane slit along the ray (1/4, +∞). The OGF of Catalan numbers C(z) = G(z)/z is, as G(z), a priori analytic in the slit plane, except perhaps at z = 0, where it has the indeterminate form 0/0. However, after C(z) is extended by continuity to C(0) = 1, it becomes an analytic function at 0, where its Taylor series converges in |z| < 14 . In this case, we say that that C(z) has an apparent or removable singularity at 0. (See also Morera’s Theorem, Note B.6, p. 743.) Similarly, the EGF of cyclic permutations 1 L(z) = log 1−z is analytic in the complex plane slit along (1, +∞). A function having no singularity at a finite distance is called entire; its Taylor series then converges everywhere in the complex plane. The EGFs, 2 e z+z /2

and

z ee −1 ,

associated, respectively, with involutions and set partitions, are entire. . . . . . . . . . . . . . . . . . . .

IV. 3.2. The Exponential Growth Formula. We say that a number sequence {an } is of exponential order K n , which we abbreviate as (the symbol ⊲⊳ is a “bowtie”) an ⊲⊳ K n

iff

lim sup |an |1/n = K .

The relation “an ⊲⊳ K n ” reads as “an is of exponential order K n ”. It expresses both an upper bound and a lower bound, and one has, for any ǫ > 0: (i) |an | >i.o (K − ǫ)n ; that is to say, |an | exceeds (K − ǫ)n infinitely often (for infinitely many values of n); (ii) |an | 0, f n (R − ǫ)n → 0. In particular, | f n |(R − ǫ)n < 1 for all sufficiently large n, so that | f n |1/n < (R − ǫ)−1 “almost everywhere”. (ii) In theP other direction, for any ǫ > 0, | f n |(R + ǫ)n cannot be a bounded sequence, since otherwise, n | f n |(R + ǫ/2)n would be a convergent series. Thus, | f n |1/n > (R + ǫ)−1 “infinitely often”. A global approach to the determination of growth rates is desirable. This is made possible by Theorem IV.5, p. 240, as shown by the following statement. Theorem IV.7 (Exponential Growth Formula). If f (z) is analytic at 0 and R is the modulus of a singularity nearest to the origin in the sense that6 R := sup r ≥ 0 f is analytic in |z| < r ,

then the coefficient f n = [z n ] f (z) satisfies n 1 . f n ⊲⊳ R

For functions with non-negative coefficients, including all combinatorial generating functions, one can also adopt R := sup r ≥ 0 f is analytic at all points of 0 ≤ z < r .

Proof. Let R be as stated. We cannot have R < Rconv ( f ; 0) since a function is analytic everywhere in the interior of the disc of convergence of its series representation. We cannot have R > Rconv ( f ; 0) by the Boundary Singularity Theorem. Thus R = Rconv ( f ; 0). The statement then follows from (17). The adaptation to non-negative coefficients results from Pringsheim’s theorem. The exponential growth formula thus directly relates the exponential growth of coefficients of a function to the location of its singularities nearest to the origin. This is precisely expressed by the First Principle of Coefficient Asymptotics (p. 227), which, given its importance, we repeat here: First Principle of Coefficient Asymptotics. The location of a function’s singularities dictates the exponential growth (An ) of its coefficient.

Example IV.2. Exponential growth and combinatorial enumeration. Here are a few immediate applications of exponential bounds. Surjections. The function R(z) = (2 − e z )−1 6 One should think of the process defining R as follows: take discs of increasing radii r and stop as soon as a singularity is encountered on the boundary. (The √ dual process that would start from a large disc and restrict its radius is in general ill-defined—think of 1 − z.)

IV. 3. SINGULARITIES AND EXPONENTIAL GROWTH OF COEFFICIENTS

n

1 n log rn

1 ∗ n log rn

10 20 50 100

0.33385 0.35018 0.35998 0.36325

∞

0.36651 (log 1/ρ)

−0.22508 −0.18144 −0.154449 −0.145447

245

−0.13644 (log(1/ρ ∗ )

Figure IV.5. The growth rate of simple and double surjections.

is the EGF of surjections. The denominator is an entire function, so that singularities may only arise from its zeros, to be found at the points χk = log 2 + 2ikπ , k ∈ Z. The dominant singularity of R is then at ρ = χ0 = log 2. Thus, with rn = [z n ]R(z), n 1 rn ⊲⊳ . log 2 Similarly, if “double” surjections are considered (each value in the range of the surjection is taken at least twice), the corresponding EGF is 1 , 2 + z − ez with the counts starting as 1,0,1,1,7,21,141 (EIS A032032). The dominant singularity is at ∗ ρ ∗ defined as the positive root of equation eρ − ρ ∗ = 2, and the coefficient rn∗ satisfies: ∗ ∗ n rn ⊲⊳ (1/ρ ) Numerically, this gives R ∗ (z) =

rn ⊲⊳ 1.44269n

and

rn∗ ⊲⊳ 0.87245n ,

with the actual figures for the corresponding logarithms being given in Figure IV.5. These estimates constitute a weak form of a more precise result to be established later in this chapter (p. 260): If random surjections of size n are considered equally likely, the probability of a surjection being a double surjection is exponentially small. 2 Derangements. For the cases d1,n = [z n ]e−z (1−z)−1 and d2,n = [z n ]e−z−z /2 (1−z)−1 , we have, from the poles at z = 1,

d1,n ⊲⊳ 1n

and

d2,n ⊲⊳ 1n .

The implied upper bound is combinatorially trivial. The lower bound expresses that the probability for a random permutation to be a derangement is not exponentially small. For d1,n , we have already proved (p. 225) by an elementary argument the stronger result d1,n → e−1 ; in the case of d2,n , we shall establish later (p. 261) the precise asymptotic estimate d2,n → e−3/2 .

Unary–binary trees. The expression p 1 − z − 1 − 2z − 3z 2 U (z) = = z + z2 + 2 z3 + 4 z4 + 9 z5 + · · · , 2z represents the OGF of (plane unlabelled) unary–binary trees. From the equivalent form, √ 1 − z − (1 − 3z)(1 + z) U (z) = , 2z

246

IV. COMPLEX ANALYSIS, RATIONAL AND MEROMORPHIC ASYMPTOTICS

it follows that U (z) is analytic in the complex plane slit along ( 31 , +∞) and (−∞, −1) and is singular at z = −1 and z = 1/3 where it has branch points. The closest singularity to the origin being at 31 , one has Un ⊲⊳ 3n .

In this case, the stronger upper bound Un ≤ 3n results directly from the possibility of encoding such trees by words over a ternary alphabet using Łukasiewicz codes (Chapter I, p. 74). A complete asymptotic expansion will be obtained, as one of the first applications of singularity analysis, in Chapter VI (p. 396). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

IV.15. Coding theory bounds and singularities. Let C be a combinatorial class. We say that it can be encoded with f (n) bits if, for all sufficiently large values of n, elements of Cn can be encoded as words of f (n) bits. (An interesting example occurs in Note I.23, p. 53.) Assume that C has OGF C(z) with radius of convergence R satisfying 0 < R < 1. Then, for any ǫ, C can be encoded with (1 + ǫ)κn bits where κ = − log2 R, but C cannot be encoded with (1 − ǫ)κn bits. b with radius of convergence R satisfying 0 < R < ∞, then C Similarly, if C has EGF C(z) can be encoded with n log(n/e) + (1 + ǫ)κn bits where κ = − log2 R, but C cannot be encoded with n log(n/e) + (1 − ǫ)κn bits. Since the radius of convergence is determined by the distance to singularities nearest to the origin, we have the following interesting fact: singularities convey information on optimal codes. Saddle-point bounds. The exponential growth formula (Theorem IV.7, p. 244) can be supplemented by effective upper bounds which are very easy to derive and often turn out to be surprisingly accurate. We state: Proposition IV.1 (Saddle-point bounds). Let f (z) be analytic in the disc |z| < R with 0 < R ≤ ∞. Define M( f ; r ) for r ∈ (0, R) by M( f ; r ) := sup|z|=r | f (z)|. Then, one has, for any r in (0, R), the family of saddle-point upper bounds (18)

[z n ] f (z)

≤

M( f ; r ) rn

implying

[z n ] f (z) ≤

inf

r ∈(0,R)

M( f ; r ) . rn

If in addition f (z) has non-negative coefficients at 0, then (19)

[z n ] f (z)

≤

f (r ) rn

implying

[z n ] f (z) ≤

inf

r ∈(0,R)

f (r ) . rn

Proof. In the general case of (18), the first inequality results from trivial bounds applied to the Cauchy coefficient formula, when integration is performed along a circle: Z 1 dz [z n ] f (z) = f (z) n+1 . 2iπ |z|=r z It is consequently valid for any r smaller than the radius of convergence of f at 0. The second inequality in (18) plainly represents the best possible bound of this type. In the positive case of (19), the bounds can be viewed as a direct specialization of (18). (Alternatively, they can be obtained in a straightforward manner, since fn ≤

f n−1 f n+1 f0 + ··· + + f n + n+1 + · · · , n r r r

whenever the f k are non-negative.)

IV. 3. SINGULARITIES AND EXPONENTIAL GROWTH OF COEFFICIENTS

247

Note that the value s that provides the best bound in (19) can be determined by setting a derivative to zero, (20)

s

f ′ (s) = n. f (s)

Thanks to the universal character of the first bound, any approximate solution of this last equation will in fact provide a valid upper bound. We shall see in Chapter VIII another way to conceive of these bounds as a first step in an important method of asymptotic analysis; namely, the saddle-point method, which explains where the term “saddle-point bound” originates from (Theorem VIII.2, p. 547). For reasons that are well developed there, the bounds usually capture the actual asymptotic behaviour up to a polynomial factor. A typical instance is the weak form of Stirling’s formula, 1 en ≡ [z n ]e z ≤ n , n! n √ which only overestimates the true asymptotic value by a factor of 2π n.

IV.16. A suboptimal but easy saddle-point bound. Let f (z) be analytic in |z| < 1 with non-negative coefficients. Assume that f (x) ≤ (1 − x)−β for some β ≥ 0 and all x ∈ (0, 1). Then [z n ] f (z) = O(n β ).

(Better bounds of the form O(n β−1 ) are usually obtained by the method of singularity analysis expounded in Chapter VI.) Example IV.3. Combinatorial examples of saddle-point bounds. Here are applications to fragmented permutations, set partitions (Bell numbers), involutions, and integer partitions. Fragmented permutations. First, fragmented permutations (Chapter II, p. 125) are labelled structures defined by F = S ET(S EQ≥1 (Z)). The EGF is e z/(1−z) ; we claim that

√ 1 −1/2 ) 1 Fn ≡ [z n ]e z/(1−z) ≤ e2 n− 2 +O(n . n! Indeed, the minimizing radius of the saddle-point bound (19) is s such that n s 1 d − . − n log s = 0= 2 ds 1 − s s (1 − s) √ The equation is solved by s = (2n +1− 4n + 1)/(2n). One can either use this exact value and compute an asymptotic approximation of f (s)/s n , or adopt right away the approximate value √ s1 = 1 − 1/ n, which leads to simpler calculations. The estimate (21) results. It is off from the actual asymptotic value only by a factor of order n −3/4 (cf Example VIII.7, p. 562).

(21)

Bell numbers and set partitions. Another immediate application is an upper bound on z Bell numbers enumerating set partitions, S = S ET(S ET≥1 (Z)), with EGF ee −1 . According to (20), the best saddle-point bound is obtained for s such that ses = n. Thus, s 1 Sn ≤ ee −1−n log s where s : ses = n; n! additionally, one has s = log n − log log n + o(log log n). See Chapter VIII, p. 561 for the complete saddle-point analysis.

(22)

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n 100 200 300 400 500

e In 0.106579 · 1085 0.231809 · 10195 0.383502 · 10316 0.869362 · 10444 0.425391 · 10578

In 0.240533 · 1083 0.367247 · 10193 0.494575 · 10314 0.968454 · 10442 0.423108 · 10576

−1

−2 0

1

2

3

Figure IV.6. A √ comparison of the exact number of involutions In to its approxiIn ) against mation e In = n!e n+n/2 n −n/2 : [left] a table; [right] a plot of log10 (In /e log10 n suggesting that the ratio satisfies In /e In ∼ K · n −1/2 , the slope of the curve being ≈ − 12 . Involutions. Involutions are specified by I = S ET(C YC1,2 (Z)) and have EGF I (z) = √ exp(z + 12 z 2 ). One determines, by choosing s = n as an approximate solution to (20): √

1 e n+n/2 . (23) In ≤ n! n n/2 (See Figure IV.6 for numerical data and Example VIII.5, p. 558 for a full analysis.) Similar bounds hold for permutations with all cycle lengths ≤ k and permutations σ such that σ k = I d. Integer partitions. The function

(24)

P(z) =

∞ Y

k=1

∞ ℓ X 1 1 z = exp ℓ 1 − zℓ 1 − zk ℓ=1

is the OGF of integer partitions, an unlabelled analogue of set partitions. Its radius of convergence is a priori bounded from above by 1, since the set P is infinite and the second form of P(z) shows that it is exactly equal to 1. Therefore Pn ⊲⊳ 1n . A finer upper bound results from the estimate (see also p. 576) r π2 t 1 (25) L(t) := log P(e−t ) ∼ + log − t + O(t 2 ), 6t 2π 24 which is obtained from Euler–Maclaurin summation or, better, from a Mellin analysis following Appendix B.7: Mellin transform, p. 762. Indeed, the Mellin transform of L is, by the harmonic sum rule, L ⋆ (s) = ζ (s)ζ (s + 1)Ŵ(s),

s ∈ h1, +∞i,

and the successive left-most poles at s = 1 (simple pole), s = 0 (double pole), and s = −1 (simple pole) translate into the asymptotic expansion (25). When z → 1− , we have ! 2 e−π /12 √ π2 , 1 − z exp (26) P(z) ∼ √ 6(1 − z) 2π √ from which we derive (choose s = D n as an approximate solution to (20)) √

Pn ≤ Cn −1/4 eπ 2n/3 ,

for some C > 0. This last bound is once more only off by a polynomial factor, as we shall prove when studying the saddle-point method (Proposition VIII.6, p. 578). . . . . . . . . . . . . . . .

IV. 4. CLOSURE PROPERTIES AND COMPUTABLE BOUNDS

249

IV.17. A natural boundary. One has P(r eiθ ) → ∞ as r → 1− , for any angle θ that is a rational multiple of 2π . The points e2iπ p/q being dense on the unit circle, the function P(z) admits the unit circle as a natural boundary; that is, it cannot be analytically continued beyond this circle. IV. 4. Closure properties and computable bounds Analytic functions are robust: they satisfy a rich set of closure properties. This fact makes possible the determination of exponential growth constants for coefficients of a wide range of classes of functions. Theorem IV.8 below expresses computability of growth rate for all specifications associated with iterative specifications. It is the first result of several that relate symbolic methods of Part A with analytic methods developed here. Closure properties of analytic functions. The functions analytic at a point z = a are closed under sum and product, and hence form a ring. If f (z) and g(z) are analytic at z = a, then so is their quotient f (z)/g(z) provided g(a) 6= 0. Meromorphic functions are furthermore closed under quotient and hence form a field. Such properties are proved most easily using complex-differentiability and extending the usual relations from real analysis, for instance, ( f + g)′ = f ′ + g ′ , ( f g)′ = f g ′ + f ′ g. Analytic functions are also closed under composition: if f (z) is analytic at z = a and g(w) is analytic at b = f (a), then g ◦ f (z) is analytic at z = a. Graphically: a

f

g b=f(a)

c=g(b)

The proof based on complex-differentiability closely mimicks the real case. Inverse functions exist conditionally: if f ′ (a) 6= 0, then f (z) is locally linear near a, hence invertible, so that there exists a g satisfying f ◦ g = g ◦ f = I d, where I d is the identity function, I d(z) ≡ z. The inverse function is itself locally linear, hence complex-differentiable, hence analytic. In short: the inverse of an analytic function f at a place where the derivative does not vanish is an analytic function. We shall return to this important property later in this chapter (Subsection IV. 7.1, p. 275), then put it to full use in Chapter VI (p. 402) and VII (p. 452) in order to derive strong asymptotic properties of simple varieties of trees.

IV.18. A Mean Value Theorem for analytic functions. Let f be analytic in and assume the existence of M := supz∈ | f ′ (z)|. Then, for all a, b in , one has | f (b) − f (a)| ≤ 2M|b − a|. (Hint: a simple consequence of the Mean Value Theorem applied to ℜ( f ), ℑ( f ).)

Subsection IV. 6.2, p. 269, for a proof based on integration.)

IV.19. The analytic inversion lemma. Let f be analytic on ∋ z 0 and satisfy f ′ (z 0 ) = 6 0. Then, there exists a small region 1 ⊆ containing z 0 and a C > 0 such that | f (z) − f (z ′ )| > C|z − z ′ |, for all z, z ′ ∈ 1 , z 6= z ′ . Consequently, f maps bijectively 1 on f (1 ). (See also One way to establish closure properties, as suggested above, is to deduce analyticity criteria from complex differentiability by way of the Basic Equivalence Theorem (Theorem IV.1, p. 232). An alternative approach, closer to the original notion of analyticity, can be based on a two-step process: (i) closure properties are shown to hold

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IV. COMPLEX ANALYSIS, RATIONAL AND MEROMORPHIC ASYMPTOTICS

true for formal power series; (ii) the resulting formal power series are proved to be locally convergent by means of suitable majorizations on their coefficients. This is the basis of the classical method of majorant series originating with Cauchy.

series technique. Given two power series, define f (z) g(z) if nIV.20. The majorant n

[z ] f (z) ≤ [z ]g(z) for all n ≥ 0. The following two conditions are equivalent: (i) f (z) is analytic in the disc |z| < ρ; (ii) for any r > ρ −1 there exists a c such that c f (z) . 1 − rz

If f, g are majorized by c/(1 −r z), d/(1 −r z), respectively, then f + g and f · g are majorized, f (z) + g(z)

c+d , 1 − rz

f (z) · g(z)

e , 1 − sz

for any s > r and for some e dependent on s. Similarly, the composition f ◦ g is majorized: c f ◦ g(z) . 1 − r (1 + d)z

Constructions for 1/ f and for the functional inverse of f can be similarly developed. See Cartan’s book [104] and van der Hoeven’s study [587] for a systematic treatment.

As a consequence of closure properties, for functions defined by analytic expressions, singularities can be determined inductively in an intuitively transparent manner. If Sing( f ) and Zero( f ) are, respectively, the set of singularities and zeros of the function f , then, due to closure properties of analytic functions, the following informally stated guidelines apply. Sing( f ± g) Sing( f × g) Sing( f /g) Sing(√ f ◦ g) Sing( f ) Sing(log( f )) Sing( f (−1) )

⊆ ⊆ ⊆ ⊆ ⊆ ⊆ ⊆

Sing( f ) ∪ Sing(g) Sing( f ) ∪ Sing(g) Sing( f ) ∪ Sing(g) ∪ Zero(g) Sing(g) ∪ g (−1) (Sing( f )) Sing( f ) ∪ Zero( f ) Sing( f ) ∪ Zero( f ) f (Sing( f )) ∪ f (Zero( f ′ )).

A mathematically rigorous treatment would require considering multivalued functions and Riemann surfaces, so that we do not state detailed validity conditions and keep for these formulae the status of useful heuristics. In fact, because of Pringsheim’s theorem, the search of dominant singularities of combinatorial generating function can normally avoid considering the complete multivalued structure of functions, since only some initial segment of the positive real half-line needs to be considered. This in turn implies a powerful and easy way of determining the exponential order of coefficients of a wide variety of generating functions, as we explain next. Computability of exponential growth constants. As defined in Chapters I and II, a combinatorial class is constructible or specifiable if it can be specified by a finite set of equations involving only the basic constructors. A specification is iterative or nonrecursive if in addition the dependency graph (p. 33) of the specification is acyclic. In that case, no recursion is involved and a single functional term (written with sums, products, sequences, sets, and cycles) describes the specification.

IV. 4. CLOSURE PROPERTIES AND COMPUTABLE BOUNDS

251

Our interest here is in effective computability issues. We recall that a real number α is computable iff there exists a program 5α , which, on input m, outputs a rational number αm guaranteed to be within ±10−m of α. We state:

Theorem IV.8 (Computability of growth). Let C be a constructible unlabelled class that admits an iterative specification in terms of (S EQ, PS ET, MS ET, C YC; +, ×) starting with (1, Z). Then, the radius of convergence ρC of the OGF C(z) of C is either +∞ or a (strictly) positive computable real number. Let D be a constructible labelled class that admits an iterative specification in terms of (S EQ, S ET, C YC; +, ⋆) starting with (1, Z). Then, the radius of convergence ρ D of the EGF D(z) of D is either +∞ or a (strictly) positive computable real number. Accordingly, if finite, the constants ρC , ρ D in the exponential growth estimates, n n 1 1 1 n n [z ]C(z) ≡ Cn ⊲⊳ , [z ]D(z) ≡ Dn ⊲⊳ , ρC n! ρD

are computable numbers. Proof. In both cases, the proof proceeds by induction on the structural specification of the class. For each class F, with generating function F(z), we associate a signature, which is an ordered pair hρ F , τ F i, where ρ F is the radius of convergence of F and τ F is the value of F at ρ F , precisely, τ F := lim F(x). x→ρ F−

(The value τ F is well defined as an element of R ∪ {+∞} since F, being a counting generating function, is necessarily increasing on (0, ρ F ).) Unlabelled case. An unlabelled class G is either finite, in which case its OGF G(z) is a polynomial, or infinite, in which case it diverges at z = 1, so that ρG ≤ 1. It is clearly decidable, given the specification, whether a class is finite or not: a necessary and sufficient condition for a class to be infinite is that one of the unary constructors (S EQ, MS ET, C YC) intervenes in the specification. We prove (by induction) the assertion of the theorem together with the stronger property that τ F = ∞ as soon as the class is infinite. First, the signatures of the neutral class 1 and the atomic class Z, with OGF 1 and z, are h+∞, 1i and h+∞, +∞i. Any non-constant polynomial which is the OGF of a finite set has the signature h+∞, +∞i. The assertion is thus easily verified in these cases. Next, let F = S EQ(G). The OGF G(z) must be non-constant and satisfy G(0) = 0, in order for the sequence construction to be properly defined. Thus, by the induction hypothesis, one has 0 < ρG ≤ +∞ and τG = +∞. Now, the function G being increasing and continuous along the positive axis, there must exist a value β such that 0 < β < ρG with G(β) = 1. For z ∈ (0, β), the quasi-inverse F(z) = (1 − G(z))−1 is well defined and analytic; as z approaches β from the left, F(z) increases unboundedly. Thus, the smallest singularity of F along the positive axis is at β, and by Pringsheim’s theorem, one has ρ F = β. The argument shows at the same time that τ F = +∞. There only remains to check that β is computable. The coefficients of

252

IV. COMPLEX ANALYSIS, RATIONAL AND MEROMORPHIC ASYMPTOTICS

G form a computable sequence of integers, so that G(x), which can be well approximated via a truncated Taylor series, is an effectively computable number7 if x is itself a positive computable number less than ρG . Then, binary search provides an effective procedure for determining β. Next, we consider the multiset construction, F = MS ET(G), whose translation into OGFs necessitates the P´olya exponential of Chapter I (p. 34): 1 1 2 3 F(z) = Exp(G(z)) where Exp(h(z)) := exp h(z) + h(z ) + h(z ) + · · · . 2 3 Once more, the induction hypothesis is assumed for G. If G is a polynomial, then F is a rational function with poles at roots of unity only. Thus, ρ F = 1 and τ F = ∞ in that particular case. In the general case of F = MS ET(G) with G infinite, we start by fixing arbitrarily a number r such that 0 < r < ρG ≤ 1 and examine F(z) for z ∈ (0, r ). The expression for F rewrites as 1 1 G(z 2 ) + G(z 3 ) + · · · . Exp(G(z)) = e G(z) · exp 2 3 The first factor is analytic for z on (0, ρG ) since, the exponential function being entire, e G has the singularities of G. As to the second factor, one has G(0) = 0 (in order for the set construction to be well-defined), while G(x) is convex for x ∈ [0, r ] (since its second derivative is positive). Thus, there exists a positive constant K such that G(x) ≤ K x when x ∈ [0, r ]. Then, the series 12 G(z 2 ) + 13 G(z 3 ) + · · · has its terms dominated by those of the convergent series K 2 K 3 r + r + · · · = K log(1 − r )−1 − K r. 2 3 By a well-known theorem of analytic function theory, a uniformly convergent sum of analytic functions is itself analytic; consequently, 21 G(z 2 ) + 13 G(z 3 ) + · · · is analytic at all z of (0, r ). Analyticity is then preserved by the exponential, so that F(z), being analytic at z ∈ (0, r ) for any r < ρG has a radius of convergence that satisfies ρ F ≥ ρG . On the other hand, since F(z) dominates termwise G(z), one has ρ F ≤ ρG . Thus finally one has ρ F = ρG . Also, τG = +∞ implies τ F = +∞. A parallel discussion covers the case of the powerset construction (PS ET) whose associated functional Exp is a minor modification of the P´olya exponential Exp. The cycle construction can be treated by similar arguments based on consideration of “P´olya’s logarithm” as F = C YC(G) corresponds to F(z) = Log

1 , 1 − G(z)

where

Log h(z) = log h(z) +

1 log h(z 2 ) + · · · . 2

In order to conclude with the unlabelled case, it only remains to discuss the binary constructors +, ×, which give rise to F = G + H , F = G · H . It is easily verified that 7 The present argument only establishes non-constructively the existence of a program, based on the

fact that truncated Taylor series converge geometrically fast at an interior point of their disc of convergence. Making explict this program and the involved parameters from the specification itself however represents a much harder problem (that of “uniformity” with respect to specifications) that is not addressed here.

IV. 4. CLOSURE PROPERTIES AND COMPUTABLE BOUNDS

253

ρ F = min(ρG , ρ H ). Computability is granted since the minimum of two computable numbers is computable. That τ F = +∞ in each case is immediate.

Labelled case. The labelled case is covered by the same type of argument as above, the discussion being even simpler, since the ordinary exponential and logarithm replace the P´olya operators Exp and Log. It is still a fact that all the EGFs of infinite non-recursive classes are infinite at their dominant positive singularity, though the radii of convergence can now be of any magnitude (compared to 1).

IV.21. Restricted constructions. This is an exercise in induction. Theorem IV.8 is stated for specifications involving the basic constructors. Show that the conclusion still holds if the corresponding restricted constructions (K=r , Kr , with K being any of the basic constructors) are also allowed. IV.22. Syntactically decidable properties. For unlabelled classes F , the property ρ F = 1 is decidable. For labelled and unlabelled classes, the property ρ F = +∞ is decidable.

IV.23. P´olya–Carlson and a curious property of OGFs. Here is a statement first conjectured by P´olya, then proved by Carlson in 1921 (see [164, p. 323]): If a function is represented by a power series with integer coefficients that converges inside the unit disc, then either it is a rational function or it admits the unit circle as a natural boundary. This theorem applies in particular to the OGF of any combinatorial class.

IV.24. Trees are recursive structures only! General and binary trees cannot receive an iterative specification since their OGFs assume a finite value at their Pringsheim singularity. [The same is true of most simple families of trees; cf Proposition VI.6, p. 404]. IV.25. Non-constructibility of permutations and graphs. The class P of all P permutations

n cannot be specified as a constructible unlabelled class since the OGF P(z) = n n!z has radius of convergence 0. (It is of course constructible as a labelled class.) Graphs, whether labelled or unlabelled, are too numerous to form a constructible class.

Theorem IV.8 establishes a link between analytic combinatorics, computability theory, and symbolic manipulation systems. It is based on an article of Flajolet, Salvy, and Zimmermann [255] devoted to such computability issues in exact and asymptotic enumeration. Recursive specifications are not discussed now since they tend to give rise to branch points, themselves amenable to singularity analysis techniques to be fully developed in Chapters VI and VII. The inductive process, implied by the proof of Theorem IV.8, that decorates a specification with the radius of convergence of each of its subexpressions, provides a practical basis for determining the exponential growth rate of counts associated to a non-recursive specification. Example IV.4. Combinatorial trains. This purposely artificial example from [219] (see Figure IV.7) serves to illustrate the scope of Theorem IV.8 and demonstrate its inner mechanisms at work. Define the class of all labelled trains by the following specification, Tr = Wa ⋆ S EQ(Wa ⋆ S ET(Pa)), Wa = S EQ≥1 (Pℓ), (27) Pℓ = Z ⋆ Z ⋆ (1 + C YC(Z)), Pa = C YC(Z) ⋆ C YC(Z).

In figurative terms, a train (T r ) is composed of a first wagon (Wa) to which is appended a sequence of passenger wagons, each of the latter capable of containing a set of passengers (Pa). A wagon is itself composed of “planks” (Pℓ) conventionally identified by their two end points (Z ⋆ Z) and to which a circular wheel (C YC(Z)) may optionally be attached. A passenger is

254

IV. COMPLEX ANALYSIS, RATIONAL AND MEROMORPHIC ASYMPTOTICS

Tr

0.48512

⋆ 0.48512 Wa

0.68245

Seq

0.68245 1

Seq≥1

⋆ 0.68245

⋆

Z

(Wa)

Z

Set

∞

1 ∞

⋆

+ 1

∞

Cyc

Cyc

Cyc

Z

Z

Z

1 1

1 ∞

1

1

∞

∞

Figure IV.7. The inductive determination of the radius of convergence of the EGF of trains: (left) a hierarchical view of the specification of T r ; (right) the corresponding radii of convergence for each subspecification.

composed of a head and a belly that are each circular arrangements of atoms. Here is a depiction of a random train:

The translation into a set of EGF equations is immediate and a symbolic manipulation system readily provides the form of the EGF of trains as

log((1−z)−1 ) z 2 1 + log((1 − z)−1 ) e 1 − T r (z) = 1 − z 2 1 + log((1 − z)−1 ) 1 − z 2 1 + log((1 − z)−1 )

z 2 1 + log((1 − z)−1 )

2 −1

together with the expansion T r (z) = 2

,

z2 z3 z4 z5 z6 z7 +6 + 60 + 520 + 6660 + 93408 + ··· . 2! 3! 4! 5! 6! 7!

The specification (27) has a hierarchical structure, as suggested by the top representation of Figure IV.7, and this structure is itself directly reflected by the form of the expression tree of the GF T r (z). Then, each node in the expression tree of T r (z) can be tagged with the corresponding value of the radius of convergence. This is done according to the principles of Theorem IV.8;

IV. 5. RATIONAL AND MEROMORPHIC FUNCTIONS

255

see the right diagram of Figure IV.7. For instance, the quantity 0.68245 associated to W a(z) is given by the sequence rule and is determined as the smallest positive solution of the equation z 2 1 − log(1 − z)−1 = 1.

The tagging process works upwards till the root of the tree is reached; here the radius of con. vergence of T r is determined to be ρ = 0.48512 · · · , a quantity that happens to coincide with 49 50 the ratio [z ]T r (z)/[z ]T r (z) to more than 15 decimal places. . . . . . . . . . . . . . . . . . . . . . . . .

IV. 5. Rational and meromorphic functions The last section has fully justified the First Principle of Coefficient Asymptotics leading to the exponential growth formula f n ⊲⊳ An for the coefficients of an analytic function f (z). Indeed, as we saw, one has A = 1/ρ, where ρ equals both the radius of convergence of the series representing f and the distance of the origin to the dominant, i.e., closest, singularities. We are going to start examining here the Second Principle, already given on p. 227 and relative to the form f n = An θ (n), with θ (n) the subexponential factor: Second Principle of Coefficient Asymptotics. The nature of a function’s singularities determines the associate subexponential factor (θ (n)). In this section, we develop a complete theory in the case of rational functions (that is, quotients of polynomials) and, more generally, meromorphic functions. The net result is that, for such functions, the subexponential factors are essentially polynomials: Polar singularities

;

subexponential factors θ (n) of polynomial growth.

A distinguishing feature is the extremely good quality of the asymptotic approximations obtained; for naturally occurring combinatorial problems, 15 digits of accuracy is not uncommon in coefficients of index as low as 50 (see Figure IV.8, p. 260 below for a striking example). IV. 5.1. Rational functions. A function f (z) is a rational function iff it is of the form f (z) = N (z)/D(z), with N (z) and D(z) being polynomials, which we may, without loss of generality, assume to be relatively prime. For rational functions that are analytic at the origin (e.g., generating functions), we have D(0) 6= 0. Sequences { f n }n≥0 that are coefficients of rational functions satisfy linear recurrence relations with constant coefficients. This fact is easy to establish: compute [z n ] f (z) · D(z); then, with D(z) = d0 + d1 z + · · · + dm z m , one has, for all n > deg(N (z)), m X d j f n− j = 0. j=0

The main theorem we prove now provides an exact finite expression for coefficients of f (z) in terms of the poles of f (z). Individual terms in these expressions are sometimes called exponential–polynomials.

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IV. COMPLEX ANALYSIS, RATIONAL AND MEROMORPHIC ASYMPTOTICS

Theorem IV.9 (Expansion of rational functions). If f (z) is a rational function that is analytic at zero and has poles at points α1 , α2 , . . . , αm , then its coefficients are a sum of exponential–polynomials: there exist m polynomials {5 j (x)}mj=1 such that, for n larger than some fixed n 0 , (28)

f n ≡ [z n ] f (z) =

m X

5 j (n)α −n j .

j=1

Furthermore the degree of 5 j is equal to the order of the pole of f at α j minus one. Proof. Since f (z) is rational it admits a partial fraction expansion. To wit: X cα,r f (z) = Q(z) + , (z − α)r (α,r )

where Q(z) is a polynomial of degree n 0 := deg(N ) − deg(D) if f = N /D. Here α ranges over the poles of f (z) and r is bounded from above by the multiplicity of α as a pole of f . Coefficient extraction in this expression results from Newton’s expansion, 1 (−1)r n 1 (−1)r n + r − 1 −n α . [z n ] = [z ] = r r −1 (z − α)r αr αr 1 − αz

The binomial coefficient is a polynomial of degree r − 1 in n, and collecting terms associated with a given α yields the statement of the theorem. Notice that the expansion (28) is also an asymptotic expansion in disguise: when grouping terms according to the α’s of increasing modulus, each group appears to be exponentially smaller than the previous one. In particular, if there is a unique dominant pole, |α1 | < |α2 | ≤ |α3 | ≤ · · · , then f n ∼ α1−n 51 (n),

and the error term is exponentially small as it is O(α2−n nr ) for some r . A classical instance is the OGF of Fibonacci numbers, z , F(z) = 1 − z − z2 √ √ −1 + 5 . −1 − 5 . with poles at = 0.61803 and = −1.61803, so that 2 2 1 1 ϕn 1 [z n ]F(z) ≡ Fn = √ ϕ n − √ ϕ¯ n = √ + O( n ), ϕ 5 5 5 √ with ϕ = (1 + 5)/2 the golden ratio, and ϕ¯ its conjugate.

IV.26. A simple exercise. Let f (z) be as in Theorem IV.9, assuming additionally a single dominant pole α1 , with multiplicity r . Then, by inspection of the proof of Theorem IV.9: 1 C with C = lim (z − α1 )r f (z). α1−n+r nr −1 1 + O fn = z→α1 (r − 1)! n

This is certainly the most direct illustration of the Second Principle: under the assumptions, a one-term asymptotic expansion of the function at its dominant singularity suffices to determine the asymptotic form of the coefficients.

IV. 5. RATIONAL AND MEROMORPHIC FUNCTIONS

257

Example IV.5. Qualitative analysis of a rational function. This is an artificial example designed to demonstrate that all the details of the full decomposition are usually not required. The rational function 1 f (z) = 2 3 2 (1 − z ) (1 − z 2 )3 (1 − z2 )

2 2iπ/3 a cubic root of unity), has a pole of order 5 at z = 1, poles of order 2 at z = ω, √ω (ω = e a pole of order 3 at z = −1, and simple poles at z = ± 2. Therefore,

f n = P1 (n) + P2 (n)ω−n + P3 (n)ω−2n + P4 (n)(−1)n +

+P5 (n)2−n/2 + P6 (n)(−1)n 2−n/2 where the degrees of P1 , . . . , P6 are 4, 1, 1, 2, 0, 0. For an asymptotic equivalent of f n , only the poles at roots of unity need to be considered since they correspond to the fastest exponential growth; in addition, only z = 1 needs to be considered for first-order asymptotics; finally, at z = 1, only the term of fastest growth needs to be taken into account. In this way, we find the correspondence 1 1 n4 1 n+4 H⇒ f ∼ f (z) ∼ ∼ . n 864 4 32 · 23 · ( 21 ) (1 − z)5 32 · 23 · ( 12 ) The way the analysis can be developed without computing details of partial fraction expansion is typical. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Theorem IV.9 applies to any specification leading to a GF that is a rational function8. Combined with the qualitative approach to rational coefficient asymptotics, it gives access to a large number of effective asymptotic estimates for combinatorial counting sequences. Example IV.6. Asymptotics of denumerants. Denumerants are integer partitions with summands restricted to be from a fixed finite set (Chapter I, p. 43). We let P T be the class relative to set T ⊂ Z>0 , with the known OGF, Y 1 P T (z) = . 1 − zω ω∈T

Without loss of generality, we assume that gcd(T ) = 1; that is, the coin denomination are not all multiples of a number d > 1. A particular case is the one of integer partitions whose summands are in {1, 2, . . . , r }, P {1,...,r } (z) =

r Y

m=1

1 . 1 − zm

The GF has all its poles being roots of unity. At z = 1, the order of the pole is r , and one has 1 1 P {1,...,r } (z) ∼ , r ! (1 − z)r as z → 1. Other poles have strictly smaller multiplicity. For instance the multiplicity of z = −1 is equal to the number of factors (1 − z 2 j )−1 in P {1,...,r } , which is the same as the number of coin denominations that are even; this last number is at most r − 1 since, by the gcd assumption gcd(T ) = 1, at least one is odd. Similarly, a primitive qth root of unity is found to have 8 In Part A, we have been occasionally led to discuss coefficients of some simple enough rational functions, thereby anticipating the statement of the theorem: see for instance the discussion of parts in compositions (p. 168) and of records in sequences (p. 190).

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multiplicity at most r − 1. It follows that the pole z = 1 contributes a term of the form nr −1 to the coefficient of index n, while each of the other poles contributes a term of order at most nr −2 . We thus find 1 {1,...,r } . ∼ cr nr −1 with cr = Pn r !(r − 1)!

The same argument provides the asymptotic form of PnT , since, to first order asymptotics, only the pole at z = 1 counts. Proposition IV.2. Let T be a finite set of integers without a common divisor (gcd(T ) = 1). The number of partitions with summands restricted to T satisfies PnT ∼

1 nr −1 , τ (r − 1)!

with τ :=

Y

ω,

r := card(T ).

ω∈T

For instance, in a strange country that would have pennies (1 cent), nickels (5 cents), dimes (10 cents), and quarters (25 cents), the number of ways to make change for a total of n cents is [z n ]

1 (1 − z)(1 − z 5 )(1 − z 10 )(1 − z 25 )

∼

1 n3 n3 ≡ , 1 · 5 · 10 · 25 3! 7500

asymptotically. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

IV. 5.2. Meromorphic functions. An expansion similar to that of Theorem IV.9 (p. 256) holds true for coefficients of a much larger class; namely, meromorphic functions. Theorem IV.10 (Expansion of meromorphic functions). Let f (z) be a function meromorphic at all points of the closed disc |z| ≤ R, with poles at points α1 , α2 , . . . , αm . Assume that f (z) is analytic at all points of |z| = R and at z = 0. Then there exist m polynomials {5 j (x)}mj=1 such that: (29)

f n ≡ [z n ] f (z) =

m X j=1

5 j (n)α −n + O(R −n ). j

Furthermore the degree of 5 j is equal to the order of the pole of f at α j minus one. Proof. We offer two different proofs, one based on subtracted singularities, the other one based on contour integration. (i) Subtracted singularities. Around any pole α, f (z) can be expanded locally: X f (z) = (30) cα,k (z − α)k k≥−M

(31)

=

Sα (z) + Hα (z)

where the “singular part” Sα (z) is obtained by collecting all the terms with index in [−M . . − 1] (that is, forming Sα (z) = Nα (z)/(z − α) M with Nα (z) a polynomial P of degree less than M) and Hα (z) is analytic at α. Thus setting S(z) := j Sα j (z), we observe that f (z) − S(z) is analytic for |z| ≤ R. In other words, by collecting the singular parts of the expansions and subtracting them, we have “removed” the singularities of f (z), whence the name of method of subtracted singularities sometimes given to the method [329, vol. 2, p. 448].

IV. 5. RATIONAL AND MEROMORPHIC FUNCTIONS

259

Taking coefficients, we get: [z n ] f (z) = [z n ]S(z) + [z n ]( f (z) − S(z)). The coefficient of [z n ] in the rational function S(z) is obtained from Theorem IV.9. It suffices to prove that the coefficient of z n in f (z) − S(z), a function analytic for |z| ≤ R, is O(R −n ). This fact follows from trivial bounds applied to Cauchy’s integral formula with the contour of integration being λ = {z : |z| = R}, as in the proof of Proposition IV.1, p 246 (saddle-point bounds): Z n dz 1 O(1) [z ]( f (z) − S(z)) = 1 2π R. ( f (z) − S(z)) n+1 ≤ 2π 2π z R n+1 |z|=R

(ii) Contour integration. There is another line of proof for Theorem IV.10 which we briefly sketch as it provides an insight which is useful for applications to other types of singularities treated in Chapter VI. It consists in using Cauchy’s coefficient formula and “pushing” the contour of integration past singularities. In other words, one computes directly the integral Z dz 1 f (z) n+1 In = 2iπ |z|=R z

by residues. There is a pole at z = 0 with residue f n and poles at the α j with residues corresponding to the terms in the expansion stated in Theorem IV.10; for instance, if f (z) ∼ c/(z − a) as z → a, then c c −n−1 −n−1 Res( f (z)z ; z = a) = Res z ; z = a = n+1 . (z − a) a Finally, by the same trivial bounds as before, In is O(R −n ).

IV.27. Effective error bounds. The error term O(R −n ) in (29), call it εn , satisfies |εn | ≤ R −n · sup | f (z)|. |z|=R

This results immediately from the second proof. This bound may be useful, even in the case of rational functions to which it is clearly applicable.

As a consequence of Theorem IV.10, all GFs whose dominant singularities are poles can be easily analysed. Prime candidates from Part A are specifications that are “driven” by a sequence construction, since the translation of sequences involves a quasi-inverse, itself conducive to polar singularities. This covers in particular surjections, alignments, derangements, and constrained compositions, which we treat now. Example IV.7. Surjections. These are defined as sequences of sets (R = S EQ(S ET≥1 (Z))) with EGF R(z) = (2 − e z )−1 (see p. 106). We have already determined the poles in Exam. ple IV.2 (p. 244), the one of smallest modulus being at log 2 = 0.69314. At this dominant 1 −1 pole, one finds R(z) ∼ − 2 (z − log 2) . This implies an approximation for the number of surjections: n+1 1 n! n · . Rn ≡ n![z ]R(z) ∼ ξ(n), with ξ(n) := 2 log 2

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IV. COMPLEX ANALYSIS, RATIONAL AND MEROMORPHIC ASYMPTOTICS

3 75 4683 545835 102247563 28091567595 10641342970443 5315654681981355 3385534663256845323 2677687796244384203115 2574844419803190384544203 2958279121074145472650648875 4002225759844168492486127539083 6297562064950066033518373935334635 11403568794011880483742464196184901963 23545154085734896649184490637144855476395

3 75 4683 545835 102247563 28091567595 10641342970443 5315654681981355 338553466325684532 6 2677687796244384203 088 2574844419803190384544 450 295827912107414547265064 6597 40022257598441684924861275 55859 6297562064950066033518373935 416161 1140356879401188048374246419617 4527074 2354515408573489664918449063714 5314147690

Figure IV.8. The surjection numbers pyramid: for n = 2, 4, . . . , 32, the exact values of the numbers Rn (left) compared to the approximation ⌈ξ(n)⌋ with discrepant digits in boldface (right).

Figure IV.8 gives, for n = 2, 4, . . . , 32, a table of the values of the surjection numbers (left) compared with the asymptotic approximation rounded9 to the nearest integer, ⌈ξ(n)⌋: It is piquant to see that ⌈ξ(n)⌋ provides the exact value of Rn for all values of n = 1, . . . , 15, and it starts losing one digit for n = 17, after which point a few “wrong” digits gradually appear, but in very limited number; see Figure IV.8. (A similar situation prevails for tangent numbers discussed in our Invitation, p. 5.) The explanation of such a faithful asymptotic representation owes to the fact that the error terms provided by meromorphic asymptotics are exponentially small. In effect, there is no other pole in |z| ≤ 6, the next ones being at log 2 ± 2iπ with modulus of about 6.32. Thus, for rn = [z n ]R(z), there holds n+1 Rn 1 1 (32) ∼ · + O(6−n ). n! 2 log 2 For the double surjection problem, R ∗ (z) = (2 + z − e z ), we get similarly 1 [z n ]R ∗ (z) ∼ ρ ∗ (ρ ∗ )−n−1 , e −1 ∗

with ρ ∗ = 1.14619 the smallest positive root of eρ − ρ ∗ = 2. . . . . . . . . . . . . . . . . . . . . . . . . .

It is worth reflecting on this example as it is representative of a “production chain” based on the two successive implications which are characteristic of Part A and Part B of the book: 1 R = S EQ(S ET≥1 (Z)) H⇒ R(z) = 2 − ez 1 1 1 1 R(z) ∼ − −→ Rn ∼ (log 2)−n−1 . z→log 2 2 (z − log 2) n! 2 9The notation ⌈x⌋ represents x rounded to the nearest integer: ⌈x⌋ := ⌊x + 1 ⌋. 2

IV. 5. RATIONAL AND MEROMORPHIC FUNCTIONS

261

The first implication (written “H⇒”, as usual) is provided automatically by the symbolic method. The second one (written here “−→”) is a direct translation from the expansion of the GF at its dominant singularity to the asymptotic form of coefficients; it is valid conditionally upon complex analytic conditions, here those of Theorem IV.10. Example IV.8. Alignments. These are sequences of cycles (O = S EQ(C YC(Z)), p. 119) with EGF 1 O(z) = . 1 1 − log 1−z

There is a singularity when log(1 − z)−1 = 1, which is at ρ = 1 − e−1 and which arises before z = 1, where the logarithm becomes singular. Then, the computation of the asymptotic form of [z n ]O(z) only requires a local expansion near ρ, O(z) ∼

−e−1 z − 1 + e−1

[z n ]O(z) ∼

−→

e−1 , (1 − e−1 )n+1

and the coefficient estimates result from Theorem IV.10. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

IV.28. Some “supernecklaces”. One estimates [z n ] log

1 1 1 − log 1−z

!

∼

1 (1 − e−1 )−n , n

where the EGF enumerates labelled cycles of cycles (supernecklaces, p. 125). [Hint: Take derivatives.] Example IV.9. Generalized derangements. The probability that the shortest cycle in a random permutation of size n has length larger than k is [z n ]D (k) (z),

where

D (k) (z) =

1 − z − z 2 −···− z k k , e 1 2 1−z

as results from the specification D(k) = S ET(C YC>k (Z)). For any fixed k, one has (easily) D (k) (z) ∼ e− Hk /(1 − z) as z → 1, with 1 being a simple pole. Accordingly the coefficients [z n ]D (k) (z) tend to e− Hk as n → ∞. In summary, due to meromorphy, we have the characteristic implication e − Hk −→ [z n ]D (k) (z) ∼ e− Hk . 1−z Since there is no other singularity at a finite distance, the error in the approximation is (at least) exponentially small, D (k) (z) ∼

1 − z − z 2 −···− z k k = e− Hk + O(R −n ), e 1 2 1−z for any R > 1. The cases k = 1, 2 in particular justify the estimates mentioned at the beginning of this chapter, on p. 228. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (33)

[z n ]

This example is also worth reflecting upon. In prohibiting cycles of length < k, k we modify the EGF of all permutations, (1 − z)−1 by a factor e−z/1−···−z /k . The resulting EGF is meromorphic at 1; thus only the value of the modifying factor at z = 1 matters, so that this value, namely e− Hk , provides the asymptotic proportion of k–derangements. We shall encounter more and more shortcuts of this sort as we progress into the book.

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IV. COMPLEX ANALYSIS, RATIONAL AND MEROMORPHIC ASYMPTOTICS

IV.29. Shortest cycles of permutations are not too long. Let Sn be the random variable denoting the length of the shortest cycle in a random permutation of size n. Using the circle |z| = 2 to estimate the error in the approximation e− Hk above, one finds that, for k ≤ log n, 1 k+1 P(Sn > k) − e− Hk ≤ n e2 , 2

which is exponentially small in this range of k-values. Thus, the approximation e− Hk remains usable when k is allowed to tend sufficiently slowly to ∞ with n. One can also explore the possibility of better bounds and larger regions of validity of the main approximation. (See Panario and Richmond’s study [470] for a general theory of smallest components in sets.)

IV.30. Expected length of the shortest cycle. The classical approximation of the harmonic

numbers, Hk ≈ log k + γ , suggests e−γ /k as a possible approximation to (33) for both large n and large k in suitable regions. In agreement with this heuristic argument, the expected length of the shortest cycle in a random permutation of size n is effectively asymptotic to n X e−γ ∼ e−γ log n, k

k=1

a property first discovered by Shepp and Lloyd [540].

The next example illustrates the analysis of a collection of rational generating functions (Smirnov words) paralleling nicely the enumeration of a special type of integer composition (Carlitz compositions), which belongs to meromorphic asymptotics. Example IV.10. Smirnov words and Carlitz compositions. Bernoulli trials have been discussed in Chapter III (p. 204), in relation to weighted word models. Take the class W of all words over an r –ary alphabet, where letter j is assigned probability p j and letters of words are drawn P independently. With this weighting, the GF of all words is W (z) = 1/(1 − p j z) = (1 − z)−1 . Consider the problem of determining the probability that a random word of length n is of Smirnov type, that is, all blocks of length 2 are formed with unequal letters. In order to avoid degeneracies, we impose r ≥ 3 (since for r = 2, the only Smirnov words are ababa. . . and babab. . . ). By our discussion in Example III.24 (p. 204), the GF of Smirnov words (again with the probabilistic weighting) is S(z) =

1−

1 P pjz . 1+ p j z

By monotonicity of the denominator, this rational function has a dominant singularity at the unique positive solution of the equation (34)

r X

j=1

pjρ = 1, 1 + pjρ

and the point ρ is a simple pole. Consequently, ρ is a well-characterized algebraic number defined implicitly by a polynomial equation of degree ≤ r . One can furthermore check, by studying the variations of the denominator, that the other roots are all real and negative; thus, ρ is the unique dominant singularity. (Alternatively, appeal to the Perron–Frobenius argument of Example V.11, p. 349) It follows that the probability for a word to be Smirnov is, not too

IV. 6. LOCALIZATION OF SINGULARITIES

263

surprisingly, exponentially small, the precise formula being −1 r X p ρ j . [z n ]S(z) ∼ C · ρ −n , C = (1 + p j ρ)2 j=1

A similar analysis, using bivariate generating functions, shows that in a random word of length n conditioned to be Smirnov, the letter j appears with asymptotic frequency (35)

qj =

pj 1 , Q (1 + p j ρ)2

Q :=

r X

j=1

pj (1 + p j ρ)2

,

in the sense that the mean number of occurrences of letter j is asymptotic to q j n. All these results are seen to be consistent with the equiprobable letter case p j = 1/r , for which ρ = r/(r − 1). Carlitz compositions illustrate a limit situation, in which the alphabet is infinite, while letters have different sizes. Recall that a Carlitz composition of the integer n is a composition of n such that no two adjacent summands have equal value. By Note III.32, p. 201, such compositions can be obtained by substitution from Smirnov words, to the effect that −1 ∞ j X z . (36) K (z) = 1 − 1+zj j=1

The asymptotic form of the coefficients then results from an analysis of dominant poles. The OGF has a simple pole at ρ, which is the smallest positive root of the equation

(37)

∞ X

j=1

ρj = 1. 1+ρj

(Note the analogy with (34) due to commonality of the combinatorial argument.) Thus: . . Kn ∼ C · βn , C = 0.45636 34740, β = 1.75024 12917. There, β = 1/ρ with ρ as in (37). In a way analogous to Smirnov words, the asymptotic frequency of summand k appears to be proportional to kρ k /(1 + ρ k )2 ; see [369, 421] for further properties. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

IV. 6. Localization of singularities There are situations where a function possesses several dominant singularities, that is, several singularities are present on the boundary of the disc of convergence. We examine here the induced effect on coefficients and discuss ways to locate such dominant singularities. IV. 6.1. Multiple singularities. In the case when there exists more than one dominant singularity, several geometric terms of the form β n sharing the same modulus (and each carrying its own subexponential factor) must be combined. In simpler situations, such terms globally induce a pure periodic behaviour for coefficients that is easy to describe. In the general case, irregular fluctuations of a somewhat arithmetic nature may prevail.

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IV. COMPLEX ANALYSIS, RATIONAL AND MEROMORPHIC ASYMPTOTICS

300000 200000 100000 00

100

200

300

400

-100000 -200000 -300000

Figure IV.9. The coefficients [z n ] f (z) of the rational function f (z) = −3 −1 1 + 1.02z 4 1 − 1.05z 5 illustrate a periodic superposition of regimes, depending on the residue class of n modulo 40.

Pure periodicities. When several dominant singularities of f (z) have the same modulus and are regularly spaced on the boundary of the disc of convergence, they may induce complete cancellations of the main exponential terms in the asymptotic expansion of the coefficient f n . In that case, different regimes will be present in the coefficients f n based on congruence properties of n. For instance, the functions 1 = 1 − z2 + z4 − z6 + z8 − · · · , 1 + z2

1 = 1 + z3 + z6 + z9 + · · · , 1 − z3

exhibit patterns of periods 4 and 3, respectively, this corresponding to poles that are roots of unity or order 4 (±i), and 3 (ω : ω3 = 1). Then, the function φ(z) =

1 2 − z 2 + z 3 + z 4 + z 8 + z 9 − z 10 1 + = 1 + z2 1 − z3 1 − z 12

has coefficients that obey a pattern of period 12 (for example, the coefficients φn such that n ≡ 1, 5, 6, 7, 11 modulo 12 are zero). Accordingly, the coefficients of [z n ]ψ(z)

where

ψ(z) = φ(z) +

1 , 1 − z/2

manifest a different exponential growth when n is congruent to 1, 5, 6, 7, 11 mod 12. See Figure IV.9 for such a superposition of pure periodicities. In many combinatorial applications, generating functions involving periodicities can be decomposed at sight, and the corresponding asymptotic subproblems generated are then solved separately.

IV.31. Decidability of polynomial properties. Given a polynomial p(z) ∈ Q[z], the following

properties are decidable: (i) whether one of the zeros of p is a root of unity; (ii) whether one of the zeros of p has an argument that is commensurate with π . [One can use resultants. An algorithmic discussion of this and related issues is given in [306].]

Nonperiodic fluctuations. As a representative example, consider the polynomial D(z) = 1 − 65 z + z 2 , whose roots are α=

3 4 +i , 5 5

α¯ =

3 4 −i , 5 5

IV. 6. LOCALIZATION OF SINGULARITIES

1

1

0.5

0.5

00

50

100

150

200

00

-0.5

-0.5

-1

-1

5

265

10

15

20

Figure IV.10. The coefficients of f (z) = 1/(1 − 56 z + z 2 ) exhibit an apparently chaotic behaviour (left) which in fact corresponds to a discrete sampling of a sine function (right), reflecting the presence of two conjugate complex poles.

both of modulus 1 (the numbers 3, 4, 5 form a Pythagorean triple), with argument . ±θ0 where θ0 = arctan( 34 ) = 0.92729. The expansion of the function f (z) = 1/D(z) starts as 1 11 84 3 779 4 2574 5 6 z − z − z + ··· , = 1 + z + z2 − 6 2 5 25 125 625 3125 1 − 5z + z the sign sequence being

+ + + − − − + + + + − − − + + + − − − − + + + − − − − + + + − − −,

which indicates a somewhat irregular oscillating behaviour, where blocks of three or four pluses follow blocks of three or four minuses. The exact form of the coefficients of f results from a partial fraction expansion: b 1 3 1 3 a + with a = + i, b = − i, f (z) = 1 − z/α 1 − z/α¯ 2 8 2 8

where α = eiθ0 , α = e−iθ0 Accordingly,

sin((n + 1)θ0 ) . sin(θ0 ) This explains the sign changes observed. Since the angle θ0 is not commensurate with π , the coefficients fluctuate but, unlike in our earlier examples, no exact periodicity is present in the sign patterns. See Figure IV.10 for a rendering and Figure V.3 (p. 299) for a meromorphic case linked to compositions into prime summands. Complicated problems of an arithmetical nature may occur if several such singularities with non-commensurate arguments combine, and some open problem remain even in the analysis of linear recurring sequences. (For instance no decision procedure is known to determine whether such a sequence ever vanishes [200].) Fortunately, such problems occur infrequently in combinatorial applications, where dominant poles of rational functions (as well as many other functions) tend to have a simple geometry as we explain next. (38)

f n = ae−inθ0 + beinθ0 =

IV.32. Irregular fluctuations and Pythagorean triples. The quantity θ0 /π is an irrational number, so that the sign fluctuations of (38) are “irregular” (i.e., non-purely periodic). [Proof: a contrario. Indeed, otherwise, α = (3 + 4i)/5 would be a root of unity. But then the minimal

266

IV. COMPLEX ANALYSIS, RATIONAL AND MEROMORPHIC ASYMPTOTICS

polynomial of α would be a cyclotomic polynomial with non-integral coefficients, a contradiction; see [401, VIII.3] for the latter property.]

IV.33. Skolem-Mahler-Lech Theorem. Let f n be the sequence of coefficients of a rational

function, f (z) = A(z)/B(z), where A, B ∈ Q[z]. The set of all n such that f n = 0 is the union of a finite (possibly empty) set and a finite number (possibly zero) of infinite arithmetic progressions. (The proof is based on p-adic analysis, but the argument is intrinsically nonconstructive; see [452] for an attractive introduction to the subject and references.)

Periodicity conditions for positive generating functions. By the previous discussion, it is of interest to locate dominant singularities of combinatorial generating functions, and, in particular, determine whether their arguments (the “dominant directions”) are commensurate to 2π . In the latter case, different asymptotic regimes of the coefficients manifest themselves, depending on the congruence properties of n. Definition IV.5. For a sequence ( f n ) with GF f (z), the support of f , denoted Supp( f ), is the set of all n such that f n 6= 0. The sequence ( f n ), as well as its GF f (z), is said to admit a span d if for some r , there holds Supp( f ) ⊆ r + dZ≥0 ≡ {r, r + d, r + 2d, . . .}. The largest span, p, is the period, all other spans being divisors of p. If the period is equal to 1, the sequence and its GF are said to be aperiodic. If f is analytic at 0, with span d, there exists a function g analytic at 0 such that f (z) = z r g(z d ), for some r ∈ Z≥0 . With E := Supp( f ), the maximal span [the period] is determined as p = gcd(E − E) (pairwise differences) as well as p = gcd(E − {r }) where r := min(E). For instance sin(z) has period 2, cos(z) + cosh(z) 5 has period 4, z 3 e z has period 5, and so on. In the context of periodicities, a basic property is expressed by what we have chosen to name figuratively the “Daffodil Lemma”. By virtue of this lemma, the span of a function f with non-negative coefficients is related to the behaviour of | f (z)| as z varies along circles centred at the origin (Figure IV.11). Lemma IV.1 (“Daffodil Lemma”). Let f (z) be analytic in |z| < ρ and have nonnegative coefficients at 0. Assume that f does not reduce to a monomial and that for some non-zero non-positive z satisfying |z| < ρ, one has | f (z)| = f (|z|). Then, the following hold: (i) the argument of z must be commensurate to 2π , i.e., z = Reiθ with θ/(2π ) = rp ∈ Q (an irreducible fraction) and 0 < r < p; (ii) f admits p as a span. Proof. This classical lemma is a simple consequence of the strong triangle inequality. Indeed, for Part (i) of the statement, with z = Reiθ , the equality | f (z)| = f (|z|) implies that the complex numbers f n R n einθ , for n ∈ Supp( f ), all lie on the same ray (a half-line emanating from 0). This is impossible if θ/(2π ) is irrational, since, by assumption, the expansion of f contains at least two monomials (one cannot have n 1 θ ≡ n 2 θ (mod 2π )). Thus, θ/(2π ) = r/ p is a rational number. Regarding Part (ii), consider two distinct indices n 1 and n 2 in Supp( f ) and let θ/(2π ) = r/ p. Then, by the strong triangle inequality again, one must have (n 1 − n 2 )θ ≡ 0 (mod 2π ); that

IV. 6. LOCALIZATION OF SINGULARITIES

267

1.5 1 0.5 0 -1.5

-1

-0.5

0

0.5

1

1.5

-0.5 -1 -1.5

Figure IV.11. Illustration of the “Daffodil Lemma”: the images of circles z = Reiθ 25 (R = 0.4 . . 0.8) rendered by a polar plot of | f (z)| in the case of f (z) = z 7 e z + z 2 /(1 − z 10 )), which has span 5.

is, (n i − n j )r/ p = (k1 − k2 ), for some k1 , k2 ∈ Z ≥ 0. This is only possible if p divides n 1 − n 2 . Hence, p is a span. Berstel [53] first realized that rational generating functions arising from regular languages can only have dominant singularities of the form ρω j , where ω is a certain root of unity. This property in fact extends to many non-recursive specifications, as shown by Flajolet, Salvy, and Zimmermann in [255]. Proposition IV.3 (Commensurability of dominant directions). Let S be a constructible labelled class that is non-recursive, in the sense of Theorem IV.8. Assume that the EGF S(z) has a finite radius of convergence ρ. Then there exists a computable integer d ≥ 1 such that the set of dominant singularities of S(z) is contained in the set {ρω j }, where ωd = 1. Proof. (Sketch; see [53, 255]) By definition, a non-recursive class S is obtained from 1 and Z by means of a finite number of union, product, sequence, set, and cycle constructions. We have seen earlier, in Section IV. 4 (p. 249), an inductive algorithm that determines radii of convergence. It is then easy to enrich that algorithm and determine simultaneously (by induction on the specification) the period of its GF and the set of dominant directions. The period is determined by simple rules. For instance, if S = T ⋆ U (S = T · U ) and T, U are infinite series with respective periods p, q, one has the implication Supp(T ) ⊆ a + pZ,

Supp(U ) ⊆ b + qZ

with ξ = gcd( p, q). Similarly, for S = S EQ(T ), Supp(T ) ⊆ a + pZ

H⇒

H⇒

Supp(S) ⊆ a + b + ξ Z,

Supp(S) ⊆ δZ,

where now δ = gcd(a, p). Regarding dominant singularities, the case of a sequence construction is typical. It corresponds to g(z) = (1 − f (z))−1 . Assume that f (z) = z a h(z p ), with p the

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maximal period, and let ρ > 0 be such that f (ρ) = 1. The equations determining any dominant singularity ζ are f (ζ ) = 1, |ζ | = ρ. In particular, the equations imply | f (ζ )| = f (|ζ |), so that, by the Daffodil Lemma, the argument of ζ must be of the form 2πr/s. An easy refinement of the argument shows that, for δ = gcd(a, p), all the dominant directions coincide with the multiples of 2π/δ. The discussion of cycles is entirely similar since log(1 − f )−1 has the same dominant singularities as (1 − f )−1 . Finally, for exponentials, it suffices to observe that e f does not modify the singularity pattern of f , since exp(z) is an entire function.

IV.34. Daffodil lemma and unlabelled classes. Proposition IV.3 applies to any unlabelled class S that admits a non-recursive specification, provided its radius of convergence ρ satisfies ρ < 1. (When ρ = 1, there is a possibility of having the unit circle as a natural boundary, a property that is otherwise decidable from the specification.) The case of regular specifications will be investigated in detail in Section V. 3, p. 300.

Exact formulae. The error terms appearing in the asymptotic expansion of coefficients of meromorphic functions are already exponentially small. By peeling off the singularities of a meromorphic function layer by layer, in order of increasing modulus, one is led to extremely precise, sometimes even exact, expansions for the coefficients. Such exact representations are found for Bernoulli numbers Bn , surjection numbers Rn , as well as Secant numbers E 2n and Tangent numbers E 2n+1 , defined by ∞ X zn z Bn = (Bernoulli numbers) z n! e −1 n=0 ∞ X zn 1 R = (Surjection numbers) n n! 2 − ez n=0 (39) ∞ X z 2n 1 E 2n = (Secant numbers) (2n)! cos(z) n=0 ∞ X z 2n+1 E 2n+1 = tan(z) (Tangent numbers). (2n + 1)! n=0

Bernoulli numbers. These numbers traditionally written Bn can be defined by their EGF B(z) = z/(e z − 1), and they are central to Euler–Maclaurin expansions (p. 726). The function B(z) has poles at the points χk = 2ikπ , with k ∈ Z \ {0}, and the residue at χk is equal to χk , z χk ∼ (z → χk ). z e −1 z − χk

The expansion theorem for meromorphic functions is applicable here: start with the Cauchy integral formula, and proceed as in the proof of Theorem IV.10, using as external contours a large circle of radius R that passes half-way between poles. As R tends to infinity, the integrand tends to 0 (as soon as n ≥ 2) because the Cauchy kernel z −n−1 decreases as an inverse power of R while the EGF remains O(R). In the limit, corresponding to an infinitely large contour, the coefficient integral becomes equal to the sum of all residues of the meromorphic function over the whole of the complex plane.

IV. 6. LOCALIZATION OF SINGULARITIES

269

P From this argument, we get the representation Bn = −n! k∈Z\{0} χk−n . This verifies that Bn = 0 if n is odd and n ≥ 3. If n is even, then grouping terms two by two, we get the exact representation (which also serves as an asymptotic expansion): (40)

∞

X 1 B2n = (−1)n−1 21−2n π −2n . (2n)! k 2n k=1

Reverting the equality, we have also established that ζ (2n) = (−1)n−1 22n−1 π 2n

B2n , (2n)!

with

ζ (s) =

∞ X 1 , ks k=1

Bn = n![z n ]

ez

z , −1

a well-known identity that provides values of the Riemann zeta function ζ (s) at even integers as rational multiples of powers of π . Surjection numbers. In the same vein, the surjection numbers have EGF R(z) = (2 − e z )−1 with simple poles at χk = log 2 + 2ikπ

where

R(z) ∼

1 1 . 2 χk − z

Since R(z) stays bounded on circles passing half-way in between poles, we find the P exact formula, Rn = 12 n! k∈Z χk−n−1 . An equivalent real formulation is n+1 X ∞ cos((n + 1)θk ) 2kπ 1 Rn 1 , θk := arctan( + = ), (41) 2 2 2 (n+1)/2 n! 2 log 2 log 2 (log 2 + 4k π ) k=1 which exhibits infinitely many harmonics of fast decaying amplitude.

IV.35. Alternating permutations, tangent and secant numbers. The relation (40) also provides a representation of the tangent numbers since E 2n−1 = (−1)n−1 B2n 4n (4n − 1)/(2n). The secant numbers E 2n satisfy ∞ X

k=1

(−1)k (π/2)2n+1 = E 2n , 2 (2n)! (2k + 1)2n+1

which can be read either as providing an asymptotic expansion of E 2n or as an evaluation of the sums on the left (the values of a Dirichlet L-function) in terms of π . The asymptotic number of alternating permutations (pp. 5 and 143) is consequently known to great accuracy.

IV.36. Solutions to the equation tan(x) = x. Let P xn be the nth positive root of the equation tan(x) = x. For any integer r ≥ 1, the sum S(r ) := n xn−2r is a computable rational number. For instance: S2 = 1/10, S4 = 1/350, S6 = 1/7875. [From mathematical folklore.] IV. 6.2. Localization of zeros and poles. We gather here a few results that often prove useful in determining the location of zeros of analytic functions, and hence of poles of meromorphic functions. A detailed treatment of this topic may be found in Henrici’s book [329, §4.10]. Let f (z) be an analytic function in a region and let γ be a simple closed curve interior to , and on which f is assumed to have no zeros. We claim that the quantity Z f ′ (z) 1 dz (42) N( f ; γ ) = 2iπ γ f (z)

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exactly equals the number of zeros of f inside γ counted with multiplicity. [Proof: the function f ′ / f has its poles exactly at the zeros of f , and the residue at each pole α equals the multiplicity of α as a root of f ; the assertion then results from the residue theorem.] Since a primitive function (antiderivative) of f ′ / f is log f , the integral also represents the variation of log f along γ , which is written [log f ]γ . This variation itself reduces to 2iπ times the variation of the argument of f along γ , since log(r eiθ ) = log r + iθ and the modulus r has variation equal to 0 along a closed contour ([log r ]γ = 0). The quantity [θ ]γ is, by its definition, 2π multiplied by the number of times the transformed contour f (γ ) winds about the origin, a number known as the winding number. This observation is known as the Argument Principle: Argument Principle. The number of zeros of f (z) (counted with multiplicities) inside the simple loop γ equals the winding number of the transformed contour f (γ ) around the origin. By the same argument, if f is meromorphic in ∋ γ , then N ( f ; γ ) equals the difference between the number of zeros and the number of poles of f inside γ , multiplicities being taken into account. Figure IV.12 exemplifies the use of the argument principle in localizing zeros of a polynomial. By similar devices, we get Rouch´e’s theorem: Rouch´e’s theorem. Let the functions f (z) and g(z) be analytic in a region containing in its interior the closed simple curve γ . Assume that f and g satisfy |g(z)| < | f (z)| on the curve γ . Then f (z) and f (z) + g(z) have the same number of zeros inside the interior domain delimited by γ . An intuitive way to visualize Rouch´e’s Theorem is as follows: since |g| < | f |, then f (γ ) and ( f + g)(γ ) must have the same winding number.

IV.37. Proof of Rouch´e’s theorem. Under the hypothesis of Rouch´e’s theorem, for 0 ≤ t ≤ 1, the function h(z) = f (z) + tg(z) is such that N (h; γ ) is both an integer and an analytic, hence continuous, function of t in the given range. The conclusion of the theorem follows. IV.38. The Fundamental Theorem of Algebra. Every complex polynomial p(z) of degree n has exactly n roots. A proof follows by Rouch´e’s theorem from the fact that, for large enough |z| = R, the polynomial assumed to be monic is a “perturbation” of its leading term, z n . [Other proofs can be based on Liouville’s Theorem (Note IV.7, p. 237) or on the Maximum Modulus Principle (Theorem VIII.1, p. 545).]

IV.39. Symmetric function of the zeros. Let Sk ( f ; γ ) be the sum of the kth powers of the roots of equation f (z) = 0 inside γ . One has Sk ( f ; γ ) =

1 2iπ

Z

by a variant of the proof of the Argument Principle.

f ′ (z) k z dz, f (z)

These principles form the basis of numerical algorithms for locating zeros of analytic functions, in particular the ones closest to the origin, which are of most interest to us. One can start from an initially large domain and recursively subdivide it until roots have been isolated with enough precision—the number of roots in a subdomain being at each stage determined by numerical integration; see Figure IV.12 and refer for instance to [151] for a discussion. Such algorithms even acquire the status of full

IV. 6. LOCALIZATION OF SINGULARITIES

271

0.8 1.5

0.6 0.4

1

0.2

0.5

0 0.2 0.4 0.6 0.8 -0.2

1

1.2 1.4 1.6 1.8

-0.5 00

1

0.5

1.5

2

2.5

3

-0.5

-0.4

-1

-0.6

-1.5

-0.8

4

8 6

2

4 2

-2

-1

00 -2

1

2

3

4

5

-8 -6 -4 -2 00 -2

2

4

6

8

10

-4 -6

-4

-8

Figure IV.12. The transforms of γ j = {|z| = 410j } by P4 (z) = 1 − 2z + z 4 , for j = 1, 2, 3, 4, demonstrate, via winding numbers, that P4 (z) has no zero inside |z| < 0.4, one zero inside |z| < 0.8, two zeros inside |z| < 1.2 and four zeros inside |z| < 1.6. The actual zeros are at ρ4 = 0.54368, 1 and 1.11514 ± 0.77184i.

proofs if one operates with guaranteed precision routines (using, for instance, careful implementations of interval arithmetics). IV. 6.3. Patterns in words: a case study. Analysing the coefficients of a single generating function that is rational is a simple task, often even bordering on the trivial, granted the exponential–polynomial formula for coefficients (Theorem IV.9, p. 256). However, in analytic combinatorics, we are often confronted with problems that involve an infinite family of functions. In that case, Rouch´e’s Theorem and the Argument Principle provide decisive tools for localizing poles, while Theorems IV.3 (Residue Theorem, p. 234) and IV.10 (Expansion of meromorphic functions, p. 258) serve to determine effective error terms. An illustration of this situation is the analysis of patterns in words for which GFs have been derived in Chapters I (p. 60) and III (p. 212). Example IV.11. Patterns in words: asymptotics. All patterns are not born equal. Surprisingly, in a random sequence of coin tossings, the pattern HTT is likely to occur much sooner (after 8 tosses on average) than the pattern HHH (needing 14 tosses on average); see the preliminary

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IV. COMPLEX ANALYSIS, RATIONAL AND MEROMORPHIC ASYMPTOTICS

Length (k) k=3 k=4

types aab, abb, bba, baa aba, bab aaa, bbb aaab, aabb, abbb, bbba, bbaa, baaa aaba, abba, abaa, bbab, baab, babb abab, baba aaaa, bbbb

c(z) 1 1 + z2 1 + z + z2

ρ 0.61803 0.56984 0.54368

1

0.54368

1 + z3 1 + z2 1 + z + z2 + z3

0.53568 0.53101 0.51879

Figure IV.13. Patterns of length 3, 4: autocorrelation polynomial and dominant poles of S(z).

discussion in Example I.12 (p. 59). Questions of this sort are of obvious interest in the statistical analysis of genetic sequences [414, 603]. Say you discover that a sequence of length 100,000 on the four letters A,G,C,T contains the pattern TACTAC twice. Can this be assigned to chance or is this likely to be a meaningful signal of some yet unknown structure? The difficulty here lies in quantifying precisely where the asymptotic regime starts, since, by Borges’s Theorem (Note I.35, p. 61), sufficiently long texts will almost certainly contain any fixed pattern. The analysis of rational generating functions supplemented by Rouch´e’s theorem provides definite answers to such questions, under Bernoulli models at least. We consider here the class W of words over an alphabet A of cardinality m ≥ 2. A pattern p of some length k is given. As seen in Chapters I and III, its autocorrelation polynomial P j is central to enumeration. This polynomial is defined as c(z) = k−1 j=0 c j z , where c j is 1 if p coincides with its jth shifted version and 0 otherwise. We consider here the enumeration of words containing the pattern p at least once, and dually of words excluding the pattern p. In other words, we look at problems such as: What is the probability that a random text of length n does (or does not) contain your name as a block of consecutive letters? The OGF of the class of words excluding p is, we recall, (43)

c(z) S(z) = k . z + (1 − mz)c(z)

(Proposition I.4, p. 61), and we shall start with the case m = 2 of a binary alphabet. The function S(z) is simply a rational function, but the location and nature of its poles is yet unknown. We only know a priori that it should have a pole in the positive interval somewhere between 12 and 1 (by Pringsheim’s Theorem and since its coefficients are in the interval [1, 2n ], for n large enough). Figure IV.13 gives a small list, for patterns of length k = 3, 4, of the pole ρ of S(z) that is nearest to the origin. Inspection of the figure suggests ρ to be close to 21 as soon as the pattern is long enough. We are going to prove this fact, based on Rouch´e’s Theorem applied to the denominator of (43). As regards termwise domination of coefficients, the autocorrelation polynomial lies between 1 (for less correlated patterns like aaa. . . ab) and 1 + z + · · · + z k−1 (for the special case aaa. . . aa). We set aside the special case of p having only equal letters, i.e., a “maximal” autocorrelation polynomial—this case is discussed at length in the next chapter. Thus, in this scenario, the autocorrelation polynomial starts as 1 + z ℓ + · · · for some ℓ ≥ 2. Fix the

IV. 6. LOCALIZATION OF SINGULARITIES

273 1

1

0.5

0.5

0 -1

-0.5

0 0

0.5

1

-1

-0.5

-0.5

0

0.5

1

-0.5

-1

-1

Figure IV.14. Complex zeros of z 31 + (1 − 2z)c(z) represented as joined by a polygonal line: (left) correlated pattern a(ba)15 ; (right) uncorrelated pattern a(ab)15 .

number A = 0.6, which proves suitable for our subsequent analysis. On |z| = A, we have 1 A2 2 3 . (44) |c(z)| ≥ 1 − (A + A + · · · ) = 1 − = 1− A 10

In addition, the quantity (1 − 2z) ranges over the circle of diameter [−0.2, 1.2] as z varies along |z| = A, so that |1 − 2z| ≥ 0.2. All in all, we have found that, for |z| = A, |(1 − 2z)c(z)| ≥ 0.02.

On the other hand, for k > 7, we have |z k | < 0.017 on the circle |z| = A. Then, among the two terms composing the denominator of (43), the first is strictly dominated by the second along |z| = A. By virtue of Rouch´e’s Theorem, the number of roots of the denominator inside |z| ≤ A is then same as the number of roots of (1 − 2z)c(z). The latter number is 1 (due to the root 21 ) since c(z) cannot be 0 by the argument of (44). Figure IV.14 exemplifies the extremely well-behaved characters of the complex zeros. In summary, we have found that for all patterns with at least two different letters (ℓ ≥ 2) and length k ≥ 8, the denominator has a unique root in |z| ≤ A = 0.6. The same property for lengths k satisfying 4 ≤ k ≤ 7 is then easily verified directly. The case ℓ = 1 where we are dealing with long runs of identical letters can be subjected to an entirely similar argument (see also Example V.4, p. 308, for details). Therefore, unicity of a simple pole ρ of S(z) in the interval (0.5, 0.6) is granted, for a binary alphabet. It is then a simple matter to determine the local expansion of S(z) near z = ρ, e 3 , z→ρ ρ − z

S(z) ∼

e := 3

c(ρ) , 2c(ρ) − (1 − 2ρ)c′ (ρ) − kρ k−1

from which a precise estimate for coefficients results from Theorems IV.9 (p. 256) and IV.10 (p. 258). The computation finally extends almost verbatim to non-binary alphabets, with ρ being now close to 1/m. It suffices to use the disc of radius A = 1.2/m. The Rouch´e part of the argument grants us unicity of the dominant pole in the interval (1/m, A) for k ≥ 5 when m = 3, and for k ≥ 4 and any m ≥ 4. (The remaining cases are easily checked individually.)

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IV. COMPLEX ANALYSIS, RATIONAL AND MEROMORPHIC ASYMPTOTICS

Proposition IV.4. Consider an m–ary alphabet. Let p be a fixed pattern of length k ≥ 4, with autocorrelation polynomial c(z). Then the probability that a random word of length n does not contain p as a pattern (a block of consecutive letters) satisfies n 5 (45) PWn (p does not occur) = 3p(mρ)−n−1 + O , 6 6 ) of the equation z k + (1 − mz)c(z) = 0 and where ρ ≡ ρp is the unique root in ( m1 , 5m 3p := mc(ρ)/(mc(ρ) − c′ (ρ)(1 − mρ) − kρ k−1 ).

Despite their austere appearance, these formulae have indeed a fairly concrete content. First, the equation satisfied by ρ can be put under the form mz = 1 + z k /c(z), and, since ρ is close to 1/m, we may expect the approximation (remember the use of “≈” as meaning “numerically approximately equal”, but not implying strict asymptotic equivalence) mρ ≈ 1 +

1 , γ mk

where γ := c(m −1 ) satisfies 1 ≤ γ < m/(m − 1). By similar principles, the probabilities in (45) are approximately −n k 1 PWn (p does not occur) ≈ 1 + ≈ e−n/(γ m ) . γ mk For a binary alphabet, this tells us that the occurrence of a pattern of length k starts becoming likely when n is of the order of 2k , that is, when k is of the order of log2 n. The more precise moment when this happens must depend (via γ ) on the autocorrelation of the pattern, with strongly correlated patterns having a tendency to occur a little late. (This vastly generalizes our empirical observations of Chapter I.) However, the mean number of occurrences of a pattern in a text of length n does not depend on the shape of the pattern. The apparent paradox is easily resolved, as we already observed in Chapter I: correlated patterns tend to occur late, while being prone to appear in clusters. For instance, the “late” pattern aaa, when it occurs, still has probability 21 to occur at the next position as well and cash in another occurrence; in contrast no such possibility is available to the “early” uncorrelated pattern aab, whose occurrences must be somewhat spread out. Such analyses are important as they can be used to develop a precise understanding of the behaviour of data compression algorithms (the Lempel–Ziv scheme); see Julien Fayolle’s contribution [204] for details. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

IV.40. Multiple pattern occurrences. A similar analysis applies to the generating function S hsi (z) of words containing a fixed number s of occurrences of a pattern p. The OGF is obtained by expanding (with respect to u) the BGF W (z, u) obtained in Chapter III, p. 212, by means of an inclusion–exclusion argument. For s ≥ 1, one finds S hsi (z) = z k

N (z)s−1 , D(z)s+1

D(z) = z k + (1 − mz)c(z),

which now has a pole of multiplicity s + 1 at z = ρ.

N (z) = z k + (1 − mz)(c(z) − 1)),

IV.41. Patterns in Bernoulli sequences—asymptotics. Similar results hold when letters are assigned non-uniform probabilities, p j = P(a j ), for a j ∈ A. The weighted autocorrelation polynomial is then defined by protrusions, as in Note III.39 (p. 213). Multiple pattern occurrences can be also analysed.

IV. 7. SINGULARITIES AND FUNCTIONAL EQUATIONS

275

IV. 7. Singularities and functional equations In the various combinatorial examples discussed so far in this chapter, we have been dealing with functions that are given by explicit expressions. Such situations essentially cover non-recursive structures as well as the very simplest recursive ones, such as Catalan or Motzkin trees, whose generating functions are expressible in terms of radicals. In fact, as we shall see extensively in this book, complex analytic methods are instrumental in analysing coefficients of functions implicitly specified by functional equations. In other words: the nature of a functional equation can often provide information regarding the singularities of its solution. Chapter V will illustrate this philosophy in the case of rational functions defined by systems of positive equations; a very large number of examples will then be given in Chapters VI and VII, where singularities that are much more general than poles are treated. In this section, we discuss three representative functional equations, 1 f (z) = ze f (z) , f (z) = z + f (z 2 + z 3 ), f (z) = , 1 − z f (z 2 ) associated, respectively, to Cayley trees, balanced 2–3 trees, and P´olya’s alcohols. These illustrate the use of fundamental inversion or iteration properties for locating dominant singularities and derive exponential growth estimates of coefficients. IV. 7.1. Inverse functions. We start with a generic problem already introduced on p. 249: given a function ψ analytic at a point y0 with z 0 = ψ(y0 ) what can be said about its inverse, namely the solution(s) to the equation ψ(y) = z when z is near z 0 and y near y0 ? Let us examine what happens when ψ ′ (y0 ) 6= 0, first without paying attention to analytic rigour. One has locally (“≈” means as usual “approximately equal”) (46)

ψ(y) ≈ ψ(y0 ) + ψ ′ (y0 )(y − y0 ),

so that the equation ψ(y) = z should admit, for z near z 0 , a solution satisfying 1 (z − z 0 ). (47) y ≈ y0 + ′ ψ (y0 ) If this is granted, the solution being locally linear, it is differentiable, hence analytic. The Analytic Inversion Lemma10 provides a firm foundation for such calculations. Lemma IV.2 (Analytic Inversion). Let ψ(z) be analytic at y0 , with ψ(y0 ) = z 0 . Assume that ψ ′ (y0 ) 6= 0. Then, for z in some small neighbourhood 0 of z 0 , there exists an analytic function y(z) that solves the equation ψ(y) = z and is such that y(z 0 ) = y0 . Proof. (Sketch) The proof involves ideas analogous to those used to establish Rouch´e’s Theorem and the Argument Principle (see especially the argument justifying Equation (42), p. 269). As a preliminary step, define the integrals ( j ∈ Z≥0 ) Z 1 ψ ′ (y) (48) σ j (z) := y j dy, 2iπ γ ψ(y) − z 10A more general statement and several proof techniques are also discussed in Appendix B.5: Implicit Function Theorem, p. 753.

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IV. COMPLEX ANALYSIS, RATIONAL AND MEROMORPHIC ASYMPTOTICS

where γ is a small enough circle centred at y0 in the y-plane. First consider σ0 . This function satisfies σ0 (z 0 ) = 1 [by the Residue Theorem] and is a continuous function of z whose value can only be an integer, this value being the number of roots of the equation ψ(y) = z. Thus, for z close enough to z 0 , one must have σ0 (z) ≡ 1. In other words, the equation ψ(y) = z has exactly one solution, the function ψ is locally invertible and a solution y = y(z) that satisfies y(z 0 ) = y0 is well-defined. Next examine σ1 . By the Residue Theorem once more, the integral defining σ1 (z) is the sum of the roots of the equation ψ(y) = z that lie inside γ , that is, in our case, the value of y(z) itself. (This is also a particular case of Note IV.39, p. 270.) Thus, one has σ1 (z) ≡ y(z). Since the integral defining σ1 (z) depends analytically on z for z close enough to z 0 , analyticity of y(z) results.

IV.42. Details. Let ψ be analytic in an open disc D centred at y0 . Then, there exists a small circle γ centred at y0 and contained in D such that ψ(y) 6= y0 on γ . [Zeros of analytic functions are isolated, a fact that results from the definition of an analytic expansion]. The integrals σ j (z) are thus well defined for z restricted to be close enough to z 0 , which ensures that there exists a δ > 0 such that |ψ(y) − z| > δ for all y ∈ γ . One can then expand the integrand as a power series in (z − z 0 ), integrate the expansion termwise, and form in this way the analytic expansions of σ0 , σ1 at z 0 . (This line of proof follows [334, I, §9.4].) IV.43. Inversion and majorant series. The process corresponding to (46) and (47) can be transformed into a sound proof: first derive a formal power series solution, then verify that the formal solution is locally convergent using the method of majorant series (p. 250).

The Analytic Inversion Lemma states the following: An analytic function locally admits an analytic inverse near any point where its first derivative is non-zero. However, as we see next, a function cannot be analytically inverted in a neighbourhood of a point where its first derivative vanishes. Consider now a function ψ(y) such that ψ ′ (y0 ) = 0 but ψ ′′ (y0 ) 6= 0, then, by the Taylor expansion of ψ, one expects 1 (49) ψ(y) ≈ ψ(y0 ) + (y − y0 )2 ψ ′′ (y0 ). 2 Solving formally for y now indicates a locally quadratic dependency 2 (z − z 0 ), (y − y0 )2 ≈ ′′ ψ (y0 ) and the inversion problem admits two solutions satisfying s 2 √ (50) y ≈ y0 ± z − z0. ′′ ψ (y0 ) What this informal argument suggests is that the solutions have a singularity at z 0 , and, in order for them to be suitably specified, one must somehow restrict their domain of √ definition: the case of z (the root(s) of y 2 − z = 0) discussed on p. 230 is typical. Given some point z 0 and a neighbourhood of z 0 , the slit neighbourhood along direction θ is the set \θ := z ∈ arg(z − z 0 ) 6≡ θ mod 2π, z 6= z 0 . We state:

IV. 7. SINGULARITIES AND FUNCTIONAL EQUATIONS

277

Lemma IV.3 (Singular Inversion). Let ψ(y) be analytic at y0 , with ψ(y0 ) = z 0 . Assume that ψ ′ (y0 ) = 0 and ψ ′′ (y0 ) 6= 0. There exists a small neighbourhood 0 of z 0 such that the following holds: for any fixed direction θ , there exist two functions, \θ \θ y1 (z) and y2 (z) defined on 0 that satisfy ψ(y(z)) = z; each is analytic in 0 , has a singularity at the point z 0 , and satisfies limz→z 0 y(z) = y0 . Proof. (Sketch) Define the functions σ j (z) as in the proof of the previous lemma, Equation (48). One now has σ0 (z) = 2, that is, the equation ψ(y) = z possesses two roots near y0 , when z is near z 0 . In other words ψ effects a double covering of a small neighbourhood of y0 onto the image neighbourhood 0 = ψ() ∋ z 0 . By possibly restricting , we may furthermore assume that ψ ′ (y) only vanishes at y0 in (zeros of analytic functions are isolated) and that is simply connected. \θ Fix any direction θ and consider the slit neighbourhood 0 . Fix a point ζ in this slit domain; it has two preimages, η1 , η2 ∈ . Pick up the one named η1 . Since ψ ′ (η1 ) is non-zero, the Analytic Inversion Lemma applies: there is a local analytic \θ inverse y1 (z) of ψ. This y1 (z) can then be uniquely continued11 to the whole of 0 , and similarly for y2 (z). We have thus obtained two distinct analytic inverses. Assume a contrario that y1 (z) can be analytically continued at z 0 . It would then admit a local expansion X y1 (z) = cn (z − z 0 )n , n≥0

while satisfying ψ(y1 (z)) = z. But then, composing the expansions of ψ and y would entail (z → z 0 ), ψ(y1 (z)) = z 0 + O (z − z 0 )2

which cannot coincide with the identity function (z). A contradiction has been reached. The point z 0 is thus a singular point for y1 (as well as for y2 ).

IV.44. Singular inversion and majorant series. In a way that parallels Note IV.43, the process summarized by Equations (49) and (50) can be justified by the method of majorant series, which leads to an alternative proof of the Singular Inversion Lemma.

IV.45. Higher order branch points. If all derivatives of ψ till order r − 1 inclusive vanish at y0 , there are r inverses, y1 (z), . . . , yr (z), defined over a slit neighbourhood of z 0 . Tree enumeration. We can now consider the problem of obtaining information on the coefficients of a function y(z) defined by an implicit equation (51)

y(z) = zφ(y(z)),

when φ(u) is analytic at u = 0. In order for the problem to be well-posed (i.e., algebraically, in terms of formal power series, as well as analytically, near the origin, there should be a unique solution for y(z)), we assume that φ(0) 6= 0. Equation (51) may then be rephrased as u (52) ψ(y(z)) = z where ψ(u) = , φ(u) 11The fact of slitting makes the resulting domain simply connected, so that analytic continuation 0

becomes uniquely defined. In contrast, the punctured domain 0 \ {z 0 } is not simply connected, so that the argument cannot be applied to it. As a matter of fact, y1 (z) gets continued to y2 (z), when the ray of angle θ is crossed: the point z 0 where two determinations meet is a branch point.

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IV. COMPLEX ANALYSIS, RATIONAL AND MEROMORPHIC ASYMPTOTICS 2

φ(u)

y(z)

ψ(u)

1.5 0.3

6

y 1 0.2

4

0.5 0.1 2 0

0.5

1

u

1.5

2

0

0.5

1

u

1.5

2

0

0.1

z

0.2

0.3

Figure IV.15. Singularities of inverse functions: φ(u) = eu (left); ψ(u) = u/φ(u) (centre); y = Inv(ψ) (right).

so that it is in fact an instance of the inversion problem for analytic functions. Equation (51) occurs in the counting of various types of trees, as seen in Subsections I. 5.1 (p. 65), II. 5.1 (p. 126), and III. 6.2 (p. 193). A typical case is φ(u) = eu , which corresponds to labelled non-plane trees (Cayley trees). The function φ(u) = (1+u)2 is associated to unlabelled plane binary trees and φ(u) = 1+u +u 2 to unary– binary trees (Motzkin trees). A full analysis was developed by Meir and Moon [435], themselves elaborating on earlier ideas of P´olya [488, 491] and Otter [466]. In all these cases, the exponential growth rate of the number of trees can be automatically determined. Proposition IV.5. Let φ be a function analytic at 0, having non-negative Taylor coefficients, and such that φ(0) 6= 0. Let R ≤ +∞ be the radius of convergence of the series representing φ at 0. Under the condition, (53)

lim

x→R −

xφ ′ (x) > 1, φ(x)

there exists a unique solution τ ∈ (0, R) of the characteristic equation, (54)

τ φ ′ (τ ) = 1. φ(τ )

Then, the formal solution y(z) of the equation y(z) = zφ(y(z)) is analytic at 0 and its coefficients satisfy the exponential growth formula: n 1 τ 1 [z n ] y(z) ⊲⊳ where ρ = = ′ . ρ φ(τ ) φ (τ ) Note that condition (53) is automatically realized as soon as φ(R − ) = +∞, which covers our earlier examples as well as all the cases where φ is an entire function (e.g., a polynomial). Figure IV.15 displays graphs of functions on the real line associated to a typical inversion problem, that of Cayley trees, where φ(u) = eu .

Proof. By Note IV.46 below, the function xφ ′ (x)/φ(x) is an increasing function of x for x ∈ (0, R). Condition (53) thus guarantees the existence and unicity of a solution

IV. 7. SINGULARITIES AND FUNCTIONAL EQUATIONS

Type binary tree Motzkin tree gen. Catalan tree Cayley tree

φ(u) (1 + u)2 1 + u + u2 1 1−u eu

(R) (∞) (∞)

τ 1 1

(1)

1 2

(∞)

1

ρ 1 4 1 3 1 4 e−1

yn ⊲⊳ ρ −n yn ⊲⊳ 4n yn ⊲⊳ 3n yn ⊲⊳ 4n yn ⊲⊳ en

279

(p. 67) (p. 68) (p. 65) (p. 128)

Figure IV.16. Exponential growth for classical tree families.

of the characteristic equation. (Alternatively, rewrite the characteristic equation as φ0 = φ2 τ 2 + 2φ3 τ 3 + · · · , where the right side is clearly an increasing function.) Next, we observe that the equation y = zφ(y) admits a unique formal power series solution, which furthermore has non-negative coefficients. (This solution can for instance be built by the method of indeterminate coefficients.) The Analytic Inversion Lemma (Lemma IV.2) then implies that this formal solution represents a function, y(z), that is analytic at 0, where it satisfies y(0) = 0. Now comes the hunt for singularities and, by Pringsheim’s Theorem, one may restrict attention to the positive real axis. Let r ≤ +∞ be the radius of convergence of y(z) at 0 and set y(r ) := limx→r − y(x), which is well defined (although possibly infinite), given positivity of coefficients. Our goal is to prove that y(r ) = τ . — Assume a contrario that y(r ) < τ . One would then have ψ ′ (y(r )) 6= 0. By the Analytic Inversion Lemma, y(z) would be analytic at r , a contradiction. — Assume a contrario that y(r ) > τ . There would then exist r ∗ ∈ (0, r ) such that ψ ′ (y(r ∗ )) = 0. But then y would be singular at r ∗ , by the Singular Inversion Lemma, also a contradiction.

Thus, one has y(r ) = τ , which is finite. Finally, since y and ψ are inverse functions, one must have r = ψ(τ ) = τ/φ(τ ) = ρ, by continuity as x → r − , which completes the proof.

Proposition IV.5 thus yields an algorithm that produces the exponential growth rate associated to tree functions. This rate is itself invariably a computable number as soon as φ is computable (i.e., its sequence of coefficients is computable). This computability result complements Theorem IV.8 (p. 251), which is relative to nonrecursive structures only. As an example of application of Proposition IV.5, general Catalan trees correspond to φ(y) = (1 − y)−1 , whose radius of convergence is R = 1. The characteristic equation is τ/(1 − τ ) = 1, which implies τ = 1/2 and ρ = 1/4. We obtain (not a surprise!) yn ⊲⊳ 4n , a weak asymptotic formula for the Catalan numbers. Similarly, for Cayley trees, φ(u) = eu and R = +∞. The characteristic equation reduces to (τ − 1)eτ = 0, so that τ = 1 and ρ = e−1 , giving a weak form of Stirling’s formula: [z n ]y(z) = n n−1 /n! ⊲⊳ en . Figure IV.16 summarizes the application of the method to a few already encountered tree families.

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IV. COMPLEX ANALYSIS, RATIONAL AND MEROMORPHIC ASYMPTOTICS

As our previous discussion suggests, the dominant singularity of tree generating functions is, under mild conditions, of the square-root type. Such a singular behaviour can then be analysed by the methods of Chapter VI: the coefficients admit an asymptotic form [z n ] y(z) ∼ C · ρ −n n −3/2 ,

with a subexponential factor of the form n −3/2 ; see Section VI. 7, p. 402.

IV.46. Convexity of GFs, Boltzmann models, and the Variance Lemma. Let φ(z) be a non-constant analytic function with non-negative coefficients and a non-zero radius of convergence R, such that φ(0) 6= 0. For x ∈ (0, R) a parameter, define the Boltzmann random variable 4 (of parameter x) by the property (55)

P(4 = n) =

φn x n , φ(x)

with

E(s 4 ) =

φ(sx) φ(x)

the probability generating function of 4. By differentiation, the first two moments of 4 are E(4) =

xφ ′ (x) , φ(x)

E(42 ) =

x 2 φ ′′ (x) xφ ′ (x) + . φ(x) φ(x)

There results, for any non-constant GF φ, the general convexity inequality valid for 0 < x < R: d xφ ′ (x) (56) > 0, dx φ(x) due to the fact that the variance of a non-degenerate random variable is always positive. Equivalently, the function log(φ(et )) is convex for t ∈ (−∞, log R). (In statistical physics, a Boltzmann model (of parameter x) corresponds to a class 8 (with OGF φ) from which elements are drawn according to the size distribution (55). An alternative derivation of (56) is given in Note VIII.4, p. 550.)

IV.47. A variant form of the inversion problem. Consider the equation y = z+φ(y), where φ

is assumed to have non-negative coefficients and be entire, with φ(u) = O(u 2 ) at u = 0. This corresponds to a simple variety of trees in which trees are counted by the number of their leaves only. For instance, we have already encountered labelled hierarchies (phylogenetic trees in Section II. 5, p. 128) corresponding to φ(u) = eu −1−u, which gives rise to one of “Schr¨oder’s problems”. Let τ be the root of φ ′ (τ ) = 1 and set ρ = τ − φ(τ ). Then, [z n ]y(z) ⊲⊳ ρ −n . For the EGF L of labelled hierarchies (L = z + e L − 1 − L), this gives L n /n! ⊲⊳ (2 log 2 − 1)−n . (Observe that Lagrange inversion also provides [z n ]y(z) = n1 [wn−1 ](1 − y −1 φ(y))−n .)

IV. 7.2. Iteration. The study of iteration of analytic functions was launched by Fatou and Julia in the first half of the twentieth century. Our reader is certainly aware of the beautiful images associated with the name of Mandelbrot whose works have triggered renewed interest in these questions, now classified as belonging to the field of “complex dynamics” [31, 156, 443, 473]. In particular, the sets that appear in this context are often of a fractal nature. Mathematical objects of this sort are occasionally encountered in analytic combinatorics. We present here the first steps of a classic analysis of balanced trees published by Odlyzko [459] in 1982. Example IV.12. Balanced trees. Consider the class E of balanced 2–3 trees defined as trees whose node degrees are restricted to the set {0, 2, 3}, with the additional property that all leaves are at the same distance from the root (Note I.67, p. 91). We adopt as notion of size the number

IV. 7. SINGULARITIES AND FUNCTIONAL EQUATIONS

281

1

x0 x1 x2 x3 x4 x5 x6 x7 x8 x9 x10

0.8

0.6

0.4

0.2

0 0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

= = = . = . = . = . = . = . = . = . =

0.6 0.576 0.522878976 0.416358802 0.245532388 0.075088357 0.006061629 0.000036966 0.000000001 1.867434390 × 10−18 3.487311201 × 10−36

Figure IV.17. The iterates of a point x0 ∈ (0, ϕ1 ), here x0 = 0.6, by σ (z) = z 2 + z 3 converge fast to 0.

of leaves (also called external nodes), the list of all 4 trees of size 8 being:

Given an existing tree, a new tree is obtained by substituting in all possible ways to each external node (2) either a pair (2, 2) or a triple (2, 2, 2), and symbolically, one has E[2] = 2 + E 2 → (22 + 222) .

In accordance with the specification, the OGF of E satisfies the functional equation E(z) = z + E(z 2 + z 3 ),

(57)

corresponding to the seemingly innocuous recurrence n X k Ek with En = n − 2k k=0

E 0 = 0, E 1 = 1.

Let σ (z) = z 2 + z 3 . Equation (57) can be expanded by iteration in the ring of formal power series, (58)

E(z) = z + σ (z) + σ [2] (z) + σ [3] (z) + · · · ,

where σ [ j] (z) denotes the jth iterate of the polynomial σ : σ [0] (z) = z, σ [h+1] (z) = σ [h] (σ (z)) = σ (σ [h] (z)). Thus, E(z) is nothing but the sum of all iterates of σ . The problem is to determine the radius of convergence of E(z), and, by Pringsheim’s theorem, the quest for dominant singularities can be limited to the positive real line. For z > 0, the polynomial σ (z) has a unique fixed point, ρ = σ (ρ), at √ 1 1+ 5 ρ= where ϕ= ϕ 2 is the golden ratio. Also, for any positive x satisfying x < ρ, the iterates σ [ j] (x) do converge to 0; see Figure IV.17. Furthermore, since σ (z) ∼ z 2 near 0, these iterates converge to 0 doubly

282

IV. COMPLEX ANALYSIS, RATIONAL AND MEROMORPHIC ASYMPTOTICS

0.9

0.8

0.7

0.6 0

1

2

3

4

5

6

Figure IV.18. Left: the fractal domain of analyticity of E(z) (inner domain in white and gray, with lighter areas representing slower convergence of the iterates of σ ) and its circle of convergence. Right: the ratio E n /(ϕ n n −1 ) plotted against log n for n = 1 . . 500 confirms that E n ⊲⊳ ϕ n and illustrates the periodic fluctuations of (60). exponentially fast (Note IV.48). By the triangle inequality, we have |σ (z)| ≤ σ (|z|), so that the sum in (58) is a normally converging sum of analytic functions, and is thus itself analytic for |z| < ρ. Consequently, E(z) is analytic in the whole of the open disc |z| < ρ. It remains to prove that the radius of convergence of E(z) is exactly equal to ρ. To that purpose it suffices to observe that E(z), as given by (58), satisfies E(x) → +∞

as

x → ρ−.

Let N be an arbitrarily large but fixed integer. It is possible to select a positive x N sufficiently close to ρ with x N < ρ, such that the N th iterate σ [N ] (x N ) is larger than 21 (the function σ [N ] (x) admits ρ as a fixed point and it is continuous and increasing at ρ). Given the sum expression (58), this entails the lower bound E(x N ) > N2 for such an x N < ρ. Thus E(x) is unbounded as x → ρ − and ρ is a singularity. The dominant positive real singularity of E(z) is thus ρ = ϕ −1 , and the Exponential Growth Formula gives the following estimate. Proposition IV.6. The number of balanced 2–3 trees satisfies: √ !n 1+ 5 n (59) [z ] E(z) ⊲⊳ . 2 It is notable that this estimate could be established so simply by a purely qualitative examination of the basic functional equation and of a fixed point of the associated iteration scheme. The complete asymptotic analysis of the E n requires the full power of singularity analysis methods to be developed in Chapter VI. Equation (60) below states the end result, which involves fluctuations that are clearly visible on Figure IV.18 (right). There is overconvergence of the representation (58), that is, convergence in certain domains beyond the disc of convergence

IV. 7. SINGULARITIES AND FUNCTIONAL EQUATIONS

283

of E(z). Figure IV.18 (left) displays the domain of analyticity of E(z) and reveals its fractal nature (compare with Figure VII.23, p. 536). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

IV.48. Quadratic convergence. First, for x ∈ [0, 21 ], one has σ (x) ≤ 23 x 2 , so that σ [ j] (x) ≤ j

j

(3/2)2 −1 x 2 . Second, for x ∈ [0, A], where A is any number < ρ, there is a number k A such k−k A that σ [k A ] (x) < 12 , so that σ [k] (x) ≤ (3/2) (3/4)2 . Thus, for any A < ρ, the series of iterates of σ is quadratically convergent when z ∈ [0, A].

IV.49. The asymptotic number of 2–3 trees. This analysis is from [459, 461]. The number of 2–3 trees satisfies asymptotically n ϕn ϕ , (60) En = (log n) + O n n2 . where is a periodic function with mean value (ϕ log(4−ϕ))−1 = 0.71208 and period log(4− . ϕ) = 0.86792. Thus oscillations are inherent in E n ; see Figure IV.18 (right).

IV. 7.3. Complete asymptotics of a functional equation. George P´olya (1887– 1985) is mostly remembered by combinatorialists for being at the origin of P´olya theory, a branch of combinatorics that deals with the enumeration of objects invariant under symmetry groups. However, in his classic article [488, 491] which founded this theory, P´olya discovered at the same time a number of startling applications of complex analysis to asymptotic enumeration12. We detail one of these now. Example IV.13. P´olya’s alcohols. The combinatorial problem of interest here is the determination of the number Mn of chemical P isomeres of alcohols Cn H2n+1 O H without asymmetric carbon atoms. The OGF M(z) = n Mn z n that starts as (EIS A000621) (61)

M(z) = 1 + z + z 2 + 2z 3 + 3z 4 + 5z 5 + 8z 6 + 14z 7 + 23z 8 + 39z 9 + · · · ,

is accessible through a functional equation, 1 . 1 − z M(z 2 ) which we adopt as our starting point. Iteration of the functional equation leads to a continued fraction representation, 1 M(z) = , z 1− z2 1− z4 1− .. . from which P´olya found: (62)

M(z) =

Proposition IV.7. Let M(z) be the solution analytic around 0 of the functional equation 1 . 1 − z M(z 2 ) Then, there exist constants K , β, and B > 1, such that . β = 1.68136 75244, Mn = K · β n 1 + O(B −n ) , M(z) =

. K = 0.36071 40971.

12In many ways, P´olya can be regarded as the grandfather of the field of analytic combinatorics.

284

IV. COMPLEX ANALYSIS, RATIONAL AND MEROMORPHIC ASYMPTOTICS

We offer two proofs. The first one is based on direct consideration of the functional equation and is of a fair degree of applicability. The second one, following P´olya, makes explicit a special linear structure present in the problem. As suggested by the main estimate, the dominant singularity of M(z) is a simple pole. First proof. By positivity of the functional equation, M(z) dominates coefficientwise any P GF (1 − z M <m (z 2 ))−1 , where M <m (z) := 0≤ j<m Mn z n is the mth truncation of M(z). In particular, one has the domination relation (use M 0). Thus ∂z (z M(z ) z=ρ

1 , 1 − z/ρ

1 . ρ M(ρ 2 ) + 2ρ 3 M ′ (ρ 2 ) √ . The argument shows at the same time that M(z) is meromorphic in |z| < ρ = 0.77. That 2 ρ is the only pole of M(z) on |z| = ρ results from the fact that z M(z ) = z + z 3 + · · · can be subjected to the type of argument encountered in the context of the Daffodil Lemma (see the discussion of quasi-inverses in the proof of Proposition IV.3, p. 267). The translation of the singular expansion (63) then yields the statement. IV.51. The growth constant of molecules. The quantity ρ can be obtained as the limit of P 2n+1 the ρm satisfying m = 1, together with ρ ∈ [ 41 , 0.69]. In each case, only a n=0 Mn ρm . few of the Mn (provided by the functional equation) are needed. One obtains: ρ10 = 0.595, . . . ρ20 = 0.594756, ρ30 = 0.59475397, ρ40 = 0.594753964. This algorithms constitutes a . geometrically convergent scheme with limit ρ = 0.59475 39639. (63)

M(z) ∼ K z→ρ

K :=

IV. 7. SINGULARITIES AND FUNCTIONAL EQUATIONS

285

Second proof. First, a sequence of formal approximants follows from (62) starting with 1,

1 , 1−z

1 − z2 , = z 1 − z − z2 1− 1 − z2

1

1

1−

z

=

1 − z2 − z4 , 1 − z − z2 − z4 + z5

z2 1 − z4 which permits us to compute any number of terms of the series M(z). Closer examination of (62) suggests to set ψ(z 2 ) , M(z) = ψ(z) 1−

where ψ(z) = 1 − z − z 2 − z 4 + z 5 − z 8 + z 9 + z 10 − z 16 + · · · . Back substitution into (62) yields ψ(z 2 ) 1 ψ(z 2 ) ψ(z 2 ) = = or , ψ(z) ψ(z) ψ(z 2 ) − zψ(z 4 ) ψ(z 4 ) 1−z ψ(z 2 ) which shows ψ(z) to be a solution of the functional equation ψ(z) = ψ(z 2 ) − zψ(z 4 ),

ψ(0) = 1.

The coefficients of ψ satisfy the recurrence ψ4n = ψ2n ,

ψ4n+1 = −ψn ,

ψ4n+2 = ψ2n+1 ,

ψ4n+3 = 0,

which implies that their values are all contained in the set {0, −1, +1}. Thus, M(z) appears to be the quotient of two function, ψ(z 2 )/ψ(z), each analytic in the unit disc, and M(z) is meromorphic in the unit disc. A numerical evaluation then shows that . ψ(z) has its smallest positive real zero at ρ = 0.59475, which is a simple root. The quantity ρ is thus a pole of M(z) (since, numerically, ψ(ρ 2 ) 6= 0). Thus n ψ(ρ 2 ) 1 ψ(ρ 2 ) M(z) ∼ H⇒ M ∼ − . n (z − ρ)ψ ′ (ρ) ρψ ′ (ρ) ρ Numerical computations then yield P´olya’s estimate. Et voil`a! . . . . . . . . . . . . . . . . . . . . . . . . . .

The example of P´olya’s alcohols is exemplary, both from a historical point of view and from a methodological perspective. As the first proof of Proposition IV.7 demonstrates, quite a lot of information can be pulled out of a functional equation without solving it. (A similar situation will be encountered in relation to coin fountains, Example V.9, p. 330.) Here, we have made great use of the fact that if f (z) is analytic in |z| < r and some a priori bounds imply the strict inequalities 0 < r < 1, then one can regard functions like f (z 2 ), f (z 3 ), and so on, as “known” since they are analytic in the disc of convergence of f and even beyond, a situation also evocative of our earlier discussion of P´olya operators in Section IV. 4, p. 249. Globally, the lesson is that functional equations, even complicated ones, can be used to bootstrap the local singular behaviour of solutions, and one can often do so even in the absence of any explicit generating function solution. The transition from singularities to coefficient asymptotics is then a simple jump.

IV.52. An arithmetic exercise. The coefficients ψn = [z n ]ψ(z) can be characterized simply

in terms of the binary representation of n. Find the asymptotic proportion of the ψn for n ∈ [1 . . 2 N ] that assume each of the values 0, +1, and −1.

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IV. COMPLEX ANALYSIS, RATIONAL AND MEROMORPHIC ASYMPTOTICS

IV. 8. Perspective In this chapter, we have started examining generating functions under a new light. Instead of being merely formal algebraic objects—power series—that encode exactly counting sequences, generating functions can be regarded as analytic objects— transformations of the complex plane—whose singularities provide a wealth of information concerning asymptotic properties of structures. Singularities provide a royal road to coefficient asymptotics. We could treat here, with a relatively simple apparatus, singularities that are poles. In this perspective, the two main statements of this chapter are the theorems relative to the expansion of rational and meromorphic functions, (Theorems IV.9, p. 256, and IV.10, p. 258). These are classical results of analysis. Issai Schur (1875–1941) is to be counted among the very first mathematicians who recognized their rˆole in combinatorial enumerations (denumerants, Example IV.6, p. 257). The complex analytic thread was developed much further by George P´olya in his famous paper of 1937 (see [488, 491]), which Read in [491, p. 96] describes as a “landmark in the history of combinatorial analysis”. There, P´olya laid the groundwork of combinatorial chemistry, the enumeration of objects under group actions, and, last but not least, the complex asymptotic theory of graphs and trees. Thanks to complex analytic methods, many combinatorial classes amenable to symbolic descriptions can be thoroughly analysed, with regard to their asymptotic properties, by means of a selected collection of basic theorems of complex analysis. The case of structures such as balanced trees and molecules, where only a functional equation of sorts is available, is exemplary. The present chapter then serves as the foundation stone of a rich theory to be developed in future chapters. Chapter V will elaborate on the analysis of rational and meromorphic functions, and present a coherent theory of paths in graphs, automata, and transfer matrices in the perspective of analytic combinatorics. Next, the method of singularity analysis developed in Chapter VI considerably extends the range of applicability of the Second Principle to functions having singularities appreciably more complicated that poles (e.g., those involving fractional powers, logarithms, iterated logarithms, and so on). Applications will be given to recursive structures, including many types of trees, in Chapter VII. Chapter VIII, dedicated to saddle-point methods will then complete the picture of univariate asymptotics by providing a unified treatment of counting GFs that are either entire functions (hence, have no singularity at a finite distance) or manifest a violent growth at their singularities (hence, fall outside of the scope of meromorphic or singularity-analysis asymptotics). Finally, in Chapter IX, the corresponding perturbative methods will be put to use in order to distil limit laws for parameters of combinatorial structures. Bibliographic notes. This chapter has been designed to serve as a refresher of basic complex analysis, with special emphasis on methods relevant for analytic combinatorics. See Figure IV.19 for a concise summary of results. References most useful for the discussion given here include the books of Titchmarsh [577] (oriented towards classical analysis), Whittaker and Watson [604] (stressing special functions), Dieudonn´e [165], Hille [334], and Knopp [373]. Henrici [329] presents complex analysis under the perspective of constructive and numerical methods, a highly valuable point of view for this book.

IV. 8. PERSPECTIVE

287

Basics. The theory of analytic functions benefits from the equivalence between two notions, analyticity and differentiability. It is the basis of a powerful integral calculus, much different from its real variable counterpart. The following two results can serve as “axioms” of the theory. T HEOREM IV.1 [Basic Equivalence Theorem] (p. 232): Two fundamental notions are equivalent, namely, analyticity (defined by convergent power series) and holomorphy (defined by differentiability). Combinatorial generating functions, a priori determined by their expansions at 0 thus satisfy the rich set of properties associated with these two equivalent notions. T HEOREM IV.2 [Null Integral Property] (p. 234): The integral of an analytic function along a simple loop (closed path that can be contracted to a single point) is 0. Consequently, integrals are largely independent of particular details of the integration contour. Residues. For meromorphic functions (functions with poles), residues are essential. Coefficients of a function can be evaluated by means of integrals. The following two theorems provide connections between local properties of a function (e.g., coefficients at one point) and global properties of the function elsewhere (e.g., an integral along a distant curve). T HEOREM IV.3 [Cauchy’s residue theorem] (p. 234): In the realm of meromorphic functions, integrals of a function can be evaluated based on local properties of the function at a few specific points, its poles. T HEOREM IV.4 [Cauchy’s Coefficient Formula] (p. 237): This is an almost immediate consequence of Cauchy’s residue theorem: The coefficients of an analytic function admit of a representation by a contour integral. Coefficients can then be evaluated or estimated using properties of the function at points away from the origin. Singularities and growth. Singularities (places where analyticity stops), provide essential information on the growth rate of a function’s coefficients. The “First Principle” relates the exponential growth rate of coefficients to the location of singularities. T HEOREM IV.5 [Boundary singularities] (p. 240): A function (given by its series expansion at 0) always has a singularity on the boundary of its disc of convergence. T HEOREM IV.6 [Pringsheim’s Theorem] (p. 240): This theorem refines the previous one for functions with non-negative coefficients. It implies that, in the case of combinatorial generating functions, the search for a dominant singularity can be restricted to the positive real axis. T HEOREM IV.7 [Exponential Growth Formula] (p. 244): The exponential growth rate of coefficients is dictated by the location of the singularities nearest to the origin—the dominant singularities. T HEOREM IV.8 [Computability of growth] (p. 251): For any combinatorial class that is nonrecursive (iterative), the exponential growth rate of coefficients is invariably a computable number. This statement can be regarded as the first general theorem of analytic combinatorics. Coefficient asymptotics. The “Second Principle” relates subexponential factors of coefficients to the nature of singularities. For rational and meromorphic functions, everything is simple. T HEOREM IV.9 [Expansion of rational functions] (p. 256): Coefficients of rational functions are explicitly expressible in terms of the poles, given their location (values) and nature (multiplicity). T HEOREM IV.10 [Expansion of meromorphic functions] (p. 258): Coefficients of meromorphic functions admit of a precise asymptotic form with exponentially small error terms, given the location and nature of the dominant poles. Figure IV.19. A summary of the main results of Chapter IV.

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IV. COMPLEX ANALYSIS, RATIONAL AND MEROMORPHIC ASYMPTOTICS

De Bruijn’s classic booklet [143] is a wonderfully concrete introduction to effective asymptotic theory, and it contains many examples from discrete mathematics thoroughly worked out using a complex analytic approach. The use of such analytic methods in combinatorics was pioneered in modern times by Bender and Odlyzko, whose first publications in this area go back to the 1970s. The state of affairs in 1995 regarding analytic methods in combinatorial enumeration is superbly summarized in Odlyzko’s scholarly chapter [461]. Wilf devotes Chapter 5 of his Generatingfunctionology [608] to this question. The books by Hofri [335], Mahmoud [429], and Szpankowski [564] contain useful accounts in the perspective of analysis of algorithms. See also our book [538] for a light introduction and the chapter by Vitter and Flajolet [598] for more on this specific topic.

Despite all appearances they [generating functions] belong to algebra and not to analysis. Combinatorialists use recurrence, generating functions, and such transformations as the Vandermonde convolution; others to my horror, use contour integrals, differential equations, and other resources of mathematical analysis. — J OHN R IORDAN [513, p. viii] and [512, Pref.]

V

Applications of Rational and Meromorphic Asymptotics

Analytic methods are extremely powerful and when they apply, they often yield estimates of unparalleled precision. — A NDREW O DLYZKO [461]

V. 1. V. 2. V. 3. V. 4. V. 5. V. 6. V. 7.

A roadmap to rational and meromorphic asymptotics The supercritical sequence schema Regular specifications and languages Nested sequences, lattice paths, and continued fractions Paths in graphs and automata Transfer matrix models Perspective

290 293 300 318 336 356 373

The primary goal of this chapter is to provide combinatorial illustrations of the power of complex analytic methods, and specifically of the rational–meromorphic framework developed in the previous chapter. At the same time, we shift gears and envisage counting problems at a new level of generality. Precisely, we organize combinatorial problems into wide families of combinatorial types amenable to a common treatment and associated with a common collection of asymptotic properties. Without attempting a formal definition, we call schema any such family determined by combinatorial and analytic conditions that covers an infinity of combinatorial classes. First, we discuss a general schema of analytic combinatorics known as the supercritical sequence schema, which provides a neat illustration of the power of meromorphic asymptotics (Theorem IV.10, p. 258), while being of wide applicability. This schema unifies the analysis of compositions, surjections, and alignments; it applies to any class which is defined as a sequence, provided components satisfy a simple analytic condition (“supercriticality”). For instance, one can predict very precisely (and easily) the number of ways in which an integer can be decomposed additively as a sum of primes (or twin primes), this even though many details of the distribution of primes are still surrounded in mystery. The next schema comprises regular specifications and languages, which a priori lead to rational generating functions and are thus systematically amenable to Theorem IV.9 (p. 256), to the effect that coefficients are described as exponential polynomials. In the case of regular specifications, much additional structure is present, especially positivity. Accordingly, counting sequences are of a simple exponential– polynomial form and fluctuations can be systematically circumvented. Applications presented in this chapter include the analysis of longest runs, attached to maximal 289

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sequences of good (or bad) luck in games of chance, pure birth processes, and the occurrence of hidden patterns (subsequences) in random texts. We then consider an important subset of regular specifications, corresponding to nested sequences, that combinatorially describe a variety of lattice paths. Such nested sequences naturally lead to nested quasi-inverses, which are none other than continued fractions. A wealth of combinatorial, algebraic, and analytic properties then surround such constructions. A prime illustration is the complete analysis of height in Dyck paths and general Catalan trees; other interesting applications relate to coin fountain and interconnection networks. Finally, the last two sections examine positive linear systems of generating functions, starting with the simplest case of finite graphs and automata, and concluding with the general framework of transfer matrices. Although the resulting generating functions are once more bound to be rational, there is benefit in examining them as defined implicitly (rather than solving explicitly) and work out singularities directly. The spectrum of matrices (the set of eigenvalues) then plays a central rˆole. An important case is the irreducible linear system schema, which is closely related to the Perron–Frobenius theory of non-negative matrices, whose importance has been long recognized in the theory of finite Markov chains. A general discussion of singularities can then be conducted, leading to valuable consequences on a variety of models— paths in graphs, finite automata, and transfer matrices. The last example discussed in this chapter treats locally constrained permutations, where rational functions combined with inclusion–exclusion provide an entry to the world of value-constrained permutations. In the various combinatorial examples encountered in this chapter, the generating functions are meromorphic in some domain extending beyond their disc of convergence at 0. As a consequence, the asymptotic estimates of coefficients involve main terms that are explicit exponential–polynomials and error terms that are exponentially smaller. This is a situation well summarized by Odlyzko’s aphorism quoted on p. 289: “Analytic methods [. . . ] often yield estimates of unparalleled precision”. V. 1. A roadmap to rational and meromorphic asymptotics The key character in this chapter is the combinatorial sequence construction S EQ. Since its translation into generating functions involves a quasi-inverse, (1 − f )−1 , the construction should in many cases be expected to induce polar singularities. Also, linear systems of equations, of which the simplest case is X = 1 + AX , are solvable by means of inverses: the solution is X = (1 − A)−1 in the scalar case, and it is otherwise expressible as a quotient of determinants (by Cramer’s rule) in the matrix case. Consequently, linear systems of equations are also conducive to polar singularities. This chapter accordingly develops along two main lines. First, we study nonrecursive families of combinatorial problems that are, in a suitable sense, driven by a sequence construction (Sections V. 2–V. 4). Second, we examine families of recursive problems that are naturally described by linear systems of equations (Sections V. 5– V. 6). Clearly, the general theorems giving the asymptotic forms of coefficients of rational and meromorphic functions apply. As we shall see, the additional positivity

V. 1. A ROADMAP TO RATIONAL AND MEROMORPHIC ASYMPTOTICS

291

structure arising from combinatorics entails notable simplifications in the asymptotic form of counting sequences. The supercritical sequence schema. This schema, fully described in Section V. 2 (p. 293) corresponds to the general form F = S EQ(G), together with a simple analytic condition, “supercriticality”, attached to the generating function G(z) of G. Under this condition, the sequence (Fn ) happens to be predictable and an asymptotic estimate, (1)

Fn = cβ n + O(B n ),

0 ≤ B < β,

c ∈ R>0 ,

applies with β such that G(1/β) = 1. Integer compositions, surjections, and alignments presented in Chapters I and II can then be treated in a unified manner. The supercritical sequence schema even covers situations where G is not necessarily constructible: this includes compositions into summands that are prime numbers or twin primes. Parameters, like the number of components and more generally profiles, are under these circumstances governed by laws that hold with a high probability. Regular specification and languages. This topic is treated in Section V. 3 (p. 300). Regular specifications are non-recursive specifications that only involve the constructions (+, ×, S EQ). In the unlabelled case, they can always be interpreted as describing a regular language in the sense of Section I. 4, p. 49. The main result here is the following: given a regular specification R, it is possible to determine constructively a number D, so that an asymptotic estimate of the form (2)

Rn = P(n)β n + O(B n ),

0 ≤ B < β,

P a polynomial,

holds, once the index n is restricted to a fixed congruence class modulo D. (Naturally, the quantities P, β, B may depend on the particular congruence class considered.) In other words, a “pure” exponential polynomial form holds for each of the D “sections” [subsequences defined on p. 302] of the counting sequence (Rn )n≥0 . In particular, irregular fluctuations, which might otherwise arise from the existence of several dominant poles sharing the same modulus but having incommensurable arguments (see the discussion in Subsection IV. 6.1, p. 263 dedicated to multiple singularities), are simply not present in regular specifications and languages. Similar estimates hold for profiles of regular specifications, where the profile of an object is understood to be the number of times any fixed construction is employed. Nested sequences, lattice paths, and continued fractions. The material considered in Section V. 4 (p. 318) could be termed the S EQ ◦ · · · ◦ S EQ schema, corresponding to nested sequences. The associated GFs are chains of quasi-inverses; that is, continued fractions. Although the general theory of regular specifications applies, the additional structure resulting from nested sequences implies, in essence, uniqueness and simplicity of the dominant pole, resulting directly in an estimate of the form (3)

Sn = cβ n + O(B n ),

0 ≤ B < β,

c ∈ R>0 ,

for objects enumerated by nested sequences. This schema covers lattice paths of bounded height, their weighted versions, as well as several other bijectively equivalent classes, like interconnection networks. In each case, profiles can be fully characterized, the estimates being of a simple form.

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V. APPLICATIONS OF RATIONAL AND MEROMORPHIC ASYMPTOTICS

Paths in graphs and automata. The framework of paths in directed graphs expounded in Section V. 5 (p. 336) is of considerable generality. In particular, it covers the case of finite automata introduced in Subsection I. 4.2, p. 56. Although, in the abstract, the descriptive power of this framework is formally equivalent to the one of regular specifications (Appendix A.7: Regular languages, p. 733), there is great advantage in considering directly problems whose natural formulation is recursive and phrased in terms of graphs or automata. (The reduction of automata to regular expressions is non-trivial so that it does not tend to preserve the original combinatorial structure.) The algebraic theory is that of matrices of the form (I − zT )−1 , where T is a matrix with non-negative entries. The analytic theory behind the scene is now that of positive matrices and the companion Perron–Frobenius theory. Uniqueness and simplicity of dominant poles of generating functions can be guaranteed under easily testable structural conditions—principally, the condition of irreducibility that corresponds to a strong connectedness of the system. Then a pure exponential polynomial form holds, (4)

Cn ∼ cλn1 + O(3n ),

0 ≤ 3 < λ1 ,

c ∈ R>0 ,

where λ1 is the (unique) dominant eigenvalue of the transition matrix T . Applications include walks over various types of graphs (the interval graph, the devil’s staircase) and words excluding one or several patterns (walks on the De Bruijn graph). Transfer matrices. This framework, whose origins lie in statistical physics, is an extension of automata and paths in graphs. What is retained is the notion of a finite state system, but transitions can now take place at different speeds. Algebraically, one is dealing with matrices of the form (I − T (z))−1 , where T is a matrix whose entries are polynomials (in z) with non-negative coefficients. Perron–Frobenius theory can be adapted to cover such cases, that, to a probabilist, look like a mixture of Markov chain and renewal theory. The consequence, for this category of models, is once more an estimate of the type (4), under irreducibility conditions; namely (5)

Dn ∼ cµn1 + O(M n ),

0 ≤ M < µ1 ,

c ∈ R>0 ,

where µ1 = 1/σ and σ is the smallest positive value of z such that T (z) has dominant eigenvalue 1. A striking application of transfer matrices is a study, with an experimental mathematics flavour, of self-avoiding walks and polygons in the plane: it turns out to be possible to predict, with a high degree of confidence (but no mathematical certainty, yet), what the number of polygons is and which distribution of area is to be expected. A combination of the transfer matrix approach with a suitable use of inclusion–exclusion (Subsection V. 6.4, p. 367) finally provides a solution to the classic m´enage problem of combinatorial theory as well as to many related questions regarding value-constrained permutations. Browsing notes. We, authors, recommend that our gentle reader first gets a bird’s eye view of this chapter, by skimming through sections, before descending to ground level and studying examples in detail—some of the latter are indeed somewhat technically advanced (e.g., they make use of Mellin transforms and/or develop limit laws). The contents of this chapter are not needed for Chapters VI–VIII, so that the reader who is impatient to penetrate further the logic of analytic combinatorics can at any

V. 2. THE SUPERCRITICAL SEQUENCE SCHEMA

293

time have a peek at Chapters VI–VIII. We shall see in Chapter IX (specifically, Section IX. 6, p. 650) that all the schemas considered here are, under simple nondegeneracy conditions, associated to Gaussian limit laws. Sections V. 2 to V. 6 are organized following a common pattern: first, we discuss “combinatorial aspects”, then “analytic aspects”, and finally “applications”. Each of Sections V. 2 to V. 5 is furthermore centred around two analytic–combinatorial theorems, one describing asymptotic enumeration, the other quantifying the asymptotic profiles of combinatorial structures. We examine in this way the supercritical sequence schema (Section V. 2), general regular specifications (Section V. 3), nested sequences (Section V. 4), and path-in-graphs models (Section V. 5). The last section (Section V. 6) departs slightly from this general pattern, since transfer matrices are reducible rather simply to the framework of paths in graphs and automata, so that we do not need specifically new statements. V. 2. The supercritical sequence schema This schema is combinatorially the simplest treated in this chapter, since it plainly deals with the sequence construction. An auxiliary analytic condition, named “supercriticality” ensures that meromorphic asymptotics applies and entails strong statistical regularities. The paradigm of supercritical sequences unifies the asymptotic properties of a number of seemingly different combinatorial types, including integer compositions, surjections, and alignments. V. 2.1. Combinatorial aspects. We consider a sequence construction, which may be taken in either the unlabelled or the labelled universe. In either case, we have F = S EQ(G)

H⇒

F(z) =

with G(0) = 0. It will prove convenient to set f n = [z n ]F(z),

1 , 1 − G(z)

gn = [z n ]G(z),

so that the number of Fn structures is f n in the unlabelled case and n! f n otherwise. From Chapter III, the BGF of F–structures with u marking the number of G– components is (6)

F = S EQ(uG)

H⇒

F(z, u) =

1 . 1 − uG(z)

We also have access to the BGF of F with u marking the number of Gk –components: (7) F hki = S EQ (uGk + (G \ Gk )) H⇒ F hki (z, u) =

1 . 1 − G(z) + (u − 1)gk z k

V. 2.2. Analytic aspects. We restrict attention to the case where the radius of convergence ρ of G(z) is non-zero, in which case, the radius of convergence of F(z) is also non-zero by virtue of closure properties of analytic functions. Here is the basic concept of this section.

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V. APPLICATIONS OF RATIONAL AND MEROMORPHIC ASYMPTOTICS

Definition V.1. Let F, G be generating functions with non-negative coefficients that are analytic at 0, with G(0) = 0. The analytic relation F(z) = (1 − G(z))−1 is said to be supercritical if G(ρ) > 1, where ρ = ρG is the radius of convergence of G. A combinatorial schema F = S EQ(G) is said to be supercritical if the relation F(z) = (1−G(z))−1 between the corresponding generating functions is supercritical.

Note that G(ρ) is well defined in R∪{+∞} as the limit limx→ρ − G(x) since G(x) increases along the positive real axis, for x ∈ (0, ρ). (The value G(ρ) corresponds to what has been denoted earlier by τG when discussing “signatures” in Section IV. 4, p. 249.) From now on we assume that G(z) is strongly aperiodic in the sense that there does not exist an integer d ≥ 2 such that G(z) = h(z d ) for some h analytic at 0. (Put otherwise, the span of 1 + G(z), as defined on p. 266, is equal to 1.) This condition entails no loss of analytic generality. Theorem V.1 (Asymptotics of supercritical sequence). Let the schema F = S EQ(G) be supercritical and assume that G(z) is strongly aperiodic. Then, one has 1 · σ −n 1 + O(An ) , [z n ]F(z) = ′ σ G (σ ) where σ is the root in (0, ρG ) of G(σ ) = 1 and A is a number less than 1. The number X of G–components in a random F–structure of size n has mean and variance satisfying G ′′ (σ ) 1 · (n + 1) − 1 + + O(An ) σ G ′ (σ ) G ′ (σ )2 σ G ′′ (σ ) + G ′ (σ ) − σ G ′ (σ )2 Vn (X ) = · n + O(1). σ 2 G ′ (σ )3 In particular, the distribution of X on Fn is concentrated. En (X )

=

Proof. See also [260, 547]. The basic observation is that G increases continuously from G(0) = 0 to G(ρG ) = τG (with τG > 1 by assumption) when x increases from 0 to ρG . Therefore, the positive number σ , which satisfies G(σ ) = 1 is well defined. Then, F is analytic at all points of the interval (0, σ ). The function G being analytic at σ , satisfies, in a neighbourhood of σ 1 G(z) = 1 + G ′ (σ )(z − σ ) + G ′′ (σ )(z − σ )2 + · · · . 2! so that F(z) has a pole at z = σ ; also, this pole is simple since G ′ (σ ) > 0, by positivity of the coefficients of G. Thus, we have 1 1 1 ≡ . F(z) ∼ − ′ ′ z→ρ G (σ )(z − σ ) σ G (σ ) 1 − z/σ Pringsheim’s theorem (Theorem IV.6, p. 240) then implies that the radius of convergence of F must coincide with σ . There remains to show that F(z) is meromorphic in a disc of some radius R > σ with the point σ as the only singularity inside the disc. This results from the assumption that G is strongly aperiodic. In effect, as a consequence of the Daffodil Lemma (Lemma IV.3, p. 267), one has G(σ eiθ ) 6= 1, for all θ 6≡ 0 (mod 2π ) . Thus, by compactness, there exists a closed disc of radius R > σ in which F is analytic except

V. 2. THE SUPERCRITICAL SEQUENCE SCHEMA

295

for a unique pole at σ . We can now apply the main theorem of meromorphic function asymptotics (Theorem IV.10, p. 258) to deduce the stated formula with A = σ/R. Next, the number of G–components in a random F structure of size n has BGF given by (6), and by differentiation, we get 1 G(z) 1 n 1 n ∂ [z ] [z ] = En (X ) = . f ∂u 1 − uG(z) f (1 − G(z))2 n

u=1

n

The problem is now reduced to extracting coefficients in a univariate generating function with a double pole at z = σ , and it suffices to expand the GF locally at σ : 1 1 G(z) 1 ∼ ≡ 2 ′ 2 . 2 ′ 2 2 z→ρ (1 − G(z)) G (σ ) (z − σ ) σ G (σ ) (1 − z/σ )2

The variance calculation is similar, with a triple pole being involved.

When a sequence construction is supercritical, the√number of components is in the mean of order n while its standard deviation is O( n). Thus, the distribution is concentrated (in the sense of Section III. 2.2, p. 161). In fact, there results from a general theorem of Bender [35] that the distribution of the number of components is asymptotically Gaussian, a property to be established in Section IX. 6, p. 650. Profiles of supercritical sequences. We have seen in Chapter III that integer compositions and integer partitions, when sampled at random, tend to assume rather different aspects. Given a sequence construction, F = S EQ(G), the profile of an element α ∈ F is the vector (X h1i , X h2i , . . .) where X h ji (α) is the number of G– components in α that have size j. In the case of (unrestricted) integer compositions, it could be proved elementarily (Example III.6, p. 167) that, on average, for size n, the number of 1-summands is ∼ n/2, the number of 2-summands is ∼ n/4, and so on. Now that meromorphic asymptotics is available, such a property can be placed in a much wider perspective. Theorem V.2 (Profiles of supercritical sequences). Consider a supercritical sequence construction, F = S EQ(G), with G(z) strongly aperiodic, as in Theorem V.1. The number of G–components of any fixed size k in a random F–object of size n satisfies (8)

En (X hki ) =

gk σ k n + O(1), σ G ′ (σ )

Vn (X hki ) = O(n),

where σ in (0, σG ) is such that G(σ ) = 1, and gk = [z k ]G(z).

Proof. The BGF with u marking the number of G–components of size k is given in (7). The mean value is then obtained as a quotient, gk z k 1 n 1 n ∂ hki [z ] F(z, u) [z ] = En (X ) = . fn ∂u fn (1 − G(z))2 u=1

The GF of cumulated values has a double pole at z = σ , and the estimate of the mean value follows. The variance is estimated similarly, after two successive differentiations and the analysis of a triple pole. P hki The total number of components X satisfies X = X , and, by Theorem V.1, its mean is asymptotic to n/(σ G ′ (σ )). Thus, Equation (8) indicates that, at least

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V. APPLICATIONS OF RATIONAL AND MEROMORPHIC ASYMPTOTICS

in some average-value sense, the “proportion” of components of size k among all components is given by gk σ k .

V.1. Proportion of k–components and convergence in probability. For any fixed k, the random hki variable X n / X n converges in probability to the value gk σ k , ( ) hki hki Xn Xn P k k k −→ gk σ , i.e., lim P gk σ (1 − ǫ) ≤ ≤ gk σ (1 + ǫ) = 1, n→∞ Xn Xn for any ǫ > 0. The proof is an easy consequence of the Chebyshev inequalities (the distributions hki of X n and X n are both concentrated).

V. 2.3. Applications. We examine here two types of applications of the supercritical sequence schema. Example V.1 makes explicit the asymptotic enumeration and the analysis of profiles of compositions, surjections and alignments. What stands out is the way the mean profile of a structure reflects the underlying inner construction K in schemas of the form S EQ(K(Z)). Example V.2 discusses compositions into restricted summands, including the striking case of compositions into primes. Example V.1. Compositions, surjections, and alignments. The three classes of interest here are integer compositions (C), surjections (R) and alignments (O), which are specified as C = S EQ(S EQ≥1 (Z)),

R = S EQ(S ET≥1 (Z)),

O = S EQ(C YC(Z))

and belong to either the labelled universe (C) or to the labelled universe (R and O). The generating functions (of type OGF, EGF, and EGF, respectively) are C(z) =

1 z , 1 − 1−z

R(z) =

1 , 1 − (e z − 1)

O(z) =

1 . 1 − log(1 − z)−1

A direct application of Theorem V.1 (p. 294) gives us back the known results Cn = 2n−1 ,

1 1 Rn ∼ (log 2)−n−1 , n! 2

1 On = e−1 (1 − e−1 )−n−1 , n!

corresponding to σ equal to 12 , log 2, and 1 − e−1 , respectively. Similarly, the expected number of summands in a random composition of the integer n is ∼ n/2; the expected cardinality of the range of a random surjection whose domain has cardinality n is asymptotic to βn with β = 1/(2 log 2); the expected number of components in a random alignment of size n is asymptotic to n/(e − 1). Theorem V.2 also applies, providing the mean number of components of size k in each case. The following table summarizes the conclusions. Structures

specification

Compositions

S EQ(S EQ≥1 (Z))

Surjections

S EQ(S ET≥1 (Z))

Alignments

S EQ(C YC(Z))

law (gk σ k ) 1 2k 1 (log 2)k k! 1 (1 − e−1 )k k

type Geometric

σ 1 2

Poisson

log 2

Logarithmic

1 − e−1

Note that the stated laws necessitate k ≥ 1. The geometric and Poisson law are classical; the logarithmic distribution (also called “logarithmic-series distribution”) of a parameter λ > 0 is

V. 2. THE SUPERCRITICAL SEQUENCE SCHEMA

297

Figure V.1. Profile of structures drawn at random represented by the sizes of their components in sorted order: (from left to right) a random composition, surjection, and alignment of size n = 100. by definition the law of a discrete random variable Y such that P(Y = k) =

λk 1 , −1 k log(1 − λ)

k ≥ 1.

The way the internal construction K in the schema S EQ(K(Z)) determines the asymptotic proportion of component of each size, Sequence 7→ Geometric;

Set 7→ Poisson;

Cycle 7→ Logarithmic,

stands out. Figure V.1 exemplifies the phenomenon by displaying components sorted by size and represented by vertical segments of corresponding lengths for three randomly drawn objects of size n = 100. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Example V.2. Compositions with restricted summands, compositions into primes. Unrestricted integer compositions are well understood as regards enumeration: their number is exactly Cn = 2n−1 , their OGF is C(z) = (1 − z)/(1 − 2z), and compositions with k summands are enumerated by binomial coefficients. Such simple exact formulae disappear when restricted compositions are considered, but, as we now show, asymptotics is much more robust to changes in specifications. Let S be a subset of the integers Z≥1 such that gcd(S) = 1, i.e., not all members of S are multiples of a common divisor d ≥ 2. In order to avoid trivialities, we also assume that S has at least two elements. The class C S of compositions with summands constrained to the set S then satisfies: X 1 C S = S EQ(S EQ S (Z)) H⇒ C S (z) = zs . , S(z) = 1 − S(z) s∈S

By assumption, S(z) is strongly aperiodic, so that Theorem V.1 (p. 294) applies directly. There is a well-defined number σ such that S(σ ) = 1,

0 < σ < 1,

and the number of S–restricted compositions satisfies 1 · σ −n 1 + O(An ) . ′ σ S (σ ) Among the already discussed cases, S = {1, 2} gives rise to Fibonacci numbers Fn and, more generally, S = {1, . . . , r } corresponds to partitions with summands at most r . In this case, the

(9)

CnS := [z n ]C S (z) =

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V. APPLICATIONS OF RATIONAL AND MEROMORPHIC ASYMPTOTICS

10 16 20 732 30 36039 40 1772207 50 87109263 60 4281550047 70 210444532770 80 10343662267187 90 508406414757253 100 24988932929490838

15 73 4 360 57 17722 61 871092 48 42815 49331 21044453 0095 1034366226 5182 5084064147 81706 24988932929 612479

Figure V.2. The pyramid relative to compositions into prime summands for n = 10 . . 100: (left: exact values; right: asymptotic formula rounded).

OGF, C {1,...,r } (z) =

1 r

1 − z 1−z 1−z

=

1−z 1 − 2z + zr +1

is a simple variant of the OGF associated to longest runs in strings, which is studied at length in Example V.4, p. 308. The treatment of the latter can be copied almost verbatim to the effect that the largest component in a random composition of n is found to be log2 n + O(1), both on average and with high probability. Compositions into primes. Here is a surprising application of the general theory. Consider the case where S is taken to be the set of prime numbers, Prime = {2, 3, 5, 7, 11, . . .}, thereby defining the class of compositions into prime summands. The sequence starts as 1, 0, 1, 1, 1, 3, 2, 6, 6, 10, 16, 20, 35, 46, 72, 105, corresponding to G(z) = z 2 +z 3 +z 5 +· · · , and is EIS A023360 in Sloane’s Encyclopedia. The formula (9) provides the asymptotic shape of the number of such compositions (Figure V.2). It is also worth noting that the constants appearing in (9) are easily determined to great accuracy, as we now explain. By (9) and the preceding equation, the dominant singularity of the OGF of compositions into primes is the positive root σ < 1 of the characteristic equation X S(z) ≡ z p = 1. p Prime

Fix a threshold value m 0 (for instance m 0 = 10 or 100) and introduce the two series X X zm0 z s , S + (z) := zs + S − (z) := . 1−z s∈S, s<m 0

s∈S, s<m 0

Clearly, for x ∈ (0, 1), one has S − (x) < S(x) < S + (x). Define then two constants σ − , σ + by the conditions

S − (σ − ) = 1,

S + (σ + ) = 1,

0 < σ − , σ + < 1.

These constants are algebraic numbers that are accessible to computation. At the same time, they satisfy σ + < σ < σ − . As the order of truncation, m 0 , increases, the values of σ + , σ − provide better and better approximations to σ , together with an interval in which σ provably lies. For instance, m 0 = 10 is enough to determine that 0.66 < σ < 0.69, and the choice

V. 2. THE SUPERCRITICAL SEQUENCE SCHEMA

80000

100000

60000

80000

40000

60000

299

40000

20000

20000 0 0 –20000

–20000 –40000

–40000

–60000

–60000

–80000

–80000

–100000

–100000

–120000

–120000

Figure V.3. Errors in the approximation of the number of compositions into primes for n = 70 . . 100: left, the values of CnPrime − g(n); right, the correction arising from the next two poles, which are complex conjugate, and its continuous extrapolation g2 (n), for n ∈ [70, 100]. . m 0 = 100 gives σ to 15 guaranteed digits of accuracy, namely, σ = 0.67740 17761 30660. Then, the asymptotic formula (9) instantiates as . . (10) CnPrime ∼ g(n), g(n) := λ · β n , λ = 0.30365 52633, β = 1.47622 87836. . (The constant β ≡ σ −1 = 1.47622 is akin to the family of Backhouse constants described in [211].) Once more, the asymptotic approximation is very good, as is exemplified by the “pyramid” of Figure V.2. The difference between CnPrime and its approximation g(n) from Equation (10) is plotted on the left-hand part of Figure V.3. The seemingly haphazard oscillations that manifest themselves are well explained by the principles discussed in Section IV. 6.1 (p. 263). It appears that the next poles of the OGF are complex conjugate and lie near −0.76 ± 0.44i, having modulus about 0.88. The corresponding residues then jointly contribute a quantity of the form . g2 (n) = c · An sin(ωn + ω0 ), A = 1.13290, for some constants c, ω, ω0 . Comparing the left-hand and right-hand parts of Figure V.3, we see that this next layer of poles explains quite well the residual error CnPrime − g(n). Here is finally a variant of compositions into primes that demonstrates in a striking way the scope of the method. Define the set Prime2 of “twinned primes” as the set of primes that belong to a twin prime pair, that is, p ∈ Prime2 if one of p − 2, p + 2 is prime. The set Prime2 starts as 3, 5, 7, 11, 13, 17, 19, 29, 31, . . . (prime numbers like 23 or 37 are thus excluded). The asymptotic formula for the number of compositions of the integer n into summands that are twinned primes is Prime2

Cn

∼ 0.18937 · 1.29799n ,

where the constants are found by methods analogous to the case of all primes. It is quite remarkable that the constants involved are still computable real numbers (and of low complexity, even), this despite the fact that it is not known whether the set of twinned primes is finite or

300

V. APPLICATIONS OF RATIONAL AND MEROMORPHIC ASYMPTOTICS

Prime2

infinite. Incidentally, a sequence that starts like Cn

,

1, 0, 0, 1, 0, 1, 1, 1, 2, 1, 3, 4, 3, 7, 7, 8, 14, 15, 21, 28, 33, 47, 58, . . . and coincides till index 22 included (!), but not beyond, was encountered by MacMahon1, as the authors discovered, much to their astonishment, from scanning Sloane’s Encyclopedia, where it appears as EIS A002124. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

V.2. Random generation of supercritical sequences. Let F = S EQ(G) be a supercritical sequence scheme. Consider a sequence of i.i.d. (independently identically distributed) random variables Y1 , Y2 , . . . each of them obeying the discrete law P(Y = k) = gk σ k ,

k ≥ 1.

A sequence is said to be hitting n if Y1 + · · · + Yr = n for some r ≥ 1. The vector (Y1 , . . . , Yr ) for a sequence conditioned to hit n has the same distribution as the sequence of the lengths of components in a random F –object of size n. For probabilists, this explains the shape of the formulae in Theorem V.1, which resemble renewal relations [205, Sec. XIII.10]. It also implies that, given a uniform random generator for G–objects, one can generate a random F –object of size n in O(n) steps on average [177]. This applies to surjections, alignments, and compositions in particular.

V.3. Largest components in supercritical sequences. Let F = S EQ(G) be a supercritical sequence. Assume that gk = [z k ]G(z) satisfies the asymptotic “smoothness” condition gk ∼ cρ −k k β , k→∞

c, ρ ∈ R>0 , β ∈ R.

Then the size L of the largest G component in a random F –object satisfies, for size n, 1 EFn (L) = (log n + β log log n) + o(log log n). log(ρ/σ ) This covers integer compositions (ρ = 1, β = 0) and alignments (ρ = 1, β = −1). [The analysis generalizes the case of longest runs in Example V.4 (p. 308) and is based on similar −1 P principles. The GF of F objects with L ≤ m is F hmi (z) = 1 − k≤m gk z k , according to Section III.7. For m large enough, this has a dominant singularity which is a simple pole at σm such that σm − σ ∼ c1 (σ/ρ)m m β . There follows a double-exponential approximation PFn (L ≤ m) ≈ exp −c2 nm β (σ/ρ)m

in the “central” region. See Example V.4 (p. 308) for a particular instance and Gourdon’s study [305] for a general theory.]

V. 3. Regular specifications and languages The purpose of this section is the general study of the (+, ×, S EQ) schema, which covers all regular specifications. As we show now, “pure” exponential–polynomial forms (ones with a single dominating exponential) can always be extracted. Theorems V.3 and V.4 below provide a universal framework for the asymptotic analysis of regular classes. Additional structural conditions to be introduced in later sections (nested sequences, irreducibility of the dependency graph and of transfer matrices) will then be seen to induce further simplifications in asymptotic formulae. 1See “Properties of prime numbers deduced from the calculus of symmetric functions”, Proc. London Math. Soc., 23 (1923), 290-316). MacMahon’s sequence corresponds to compositions into arbitrary odd primes, and 23 is the first such prime that is not twinned.

V. 3. REGULAR SPECIFICATIONS AND LANGUAGES

301

V. 3.1. Combinatorial aspects. For convenience and without loss of analytic generality, we consider here unlabelled structures. According to Chapter I (Definition I.10, p. 51, and the companion Proposition I.2, p. 52), a combinatorial specification is regular if it is non-recursive (“iterative”) and it involves only the constructions of Atom, Union, Product, and Sequence. A language L is S–regular if it is combinatorially isomorphic to a class M described by a regular specification. Alternatively, a language is S–regular if all the operations involved in its description (unions, catenation products and star operations) are unambiguous. The dictionary translating constructions into OGFs is (11)

F + G 7→ F + G,

F × G 7→ F × G,

S EQ(F) 7→ (1 − F)−1 ,

and for languages, under the essential condition of non-ambiguity (Appendix A.7: Regular languages, p. 733), (12)

L ∪ M 7→ L + M,

L · M 7→ L × M,

L⋆ 7→ (1 − L)−1 .

The rules (11) and (12) then give rise to generating functions that are invariably rational functions. Consequently, given a regular class C, the exponential–polynomial form of coefficients expressed by Theorem IV.9 (p. 256) systematically applies, and one has (13)

Cn ≡ [z n ]C(z) =

m X

5 j (n)α −n j ,

j=1

for a family of algebraic numbers α j (the poles of C(z)) and a family of polynomials 5 j . As we know from the discussion of periodicities in Section IV. 6.1 (p. 263), the collective behaviour of the sum in (13) depends on whether or not a single α dominates. In the case where several dominant singularities coexist, fluctuations of sorts (either periodic or irregular) may manifest themselves. In contrast, if a single α dominates, then the exponential–polynomial formula acquires a transparent asymptotic meaning. Accordingly, we set: P Definition V.2. An exponential–polynomial form mj=1 5 j (n)α −n j is said to be pure if |α1 | < |α j |, for all j ≥ 2. In that case, a single exponential dominates asymptotically all the other ones. As we see next for regular languages and specifications, the corresponding counting coefficients can always be described by a finite collection of pure exponential– polynomial forms. The fundamental reason is that we are dealing with a special subset of rational functions, one that enjoys strong positivity properties.

V.4. Positive rational functions. Define the class Rat+ of positive rational functions as

the smallest class containing polynomials with positive coefficients (R≥0 [z]) and closed under sum, product, and quasi-inverse, where Q( f ) = (1 − f )−1 is applied to elements f such that f (0) = 0. The OGF of any regular class with positive weights attached to neutral structures and atoms is in Rat+ . Conversely, any function in Rat+ is the OGF of a positively weighted regular class. The notion of a Rat+ function is for instance relevant to the analysis of weighted word models and Bernoulli trials (Section III. 6.1, p. 189).

302

V. APPLICATIONS OF RATIONAL AND MEROMORPHIC ASYMPTOTICS

V. 3.2. Analytic aspects. First we need the notion of sections of a sequence. Definition V.3. Let ( f n ) be a sequence of numbers. Its section of parameters D, r , where D ∈ Z>0 and r ∈ Z≥0 is the subsequence ( f n D+r ). The numbers D and r are referred to as the modulus and the base, respectively. The main theorem describing the asymptotic behaviour of regular classes is a consequence of Proposition IV.3 (p. 267) and is originally due to Berstel. (See Soittola’s article [546] as well as the books by Eilenberg [189, Ch VII] and Berstel– Reutenauer [56] for context.) Theorem V.3 (Asymptotics of regular classes). Let S be a class described by a regular specification. Then there exists an integer D such that each section of modulus D of Sn that is not eventually 0 admits a pure exponential–polynomial form: for n larger than some n 0 , and any such section of base r , one has Sn = 5(n)β n +

m X

P j (n)β nj

j=1

n ≡ r mod D,

where the quantities β, β j , with β > |β j |, and the polynomials 5, P j , with 5(x) 6≡ 0, depend on the base r . Proof. (Sketch.) Let α1 be the dominant pole of S(z) that is positive. Proposition IV.3 (p. 267) asserts that any dominant pole, α is such that α/|α| is a root of unity. Let D0 D0 , where be such that the dominant singularities are all contained in the set {α1 ω j−1 } j=1 ω = exp(2iπ/D0 ). By collecting all contributions arising from dominant poles in the general expansion (13) and by restricting n to a fixed congruence class modulo D0 , namely n = ν D0 + r with 0 ≤ r < D0 , one gets (14)

−D0 ν

Sν D0 +r = 5[r ] (n)α1

+ O(A−n ).

There 5[r ] is a polynomial depending on r and the remainder term represents an exponential polynomial with growth at most O(A−n ) for some A > α1 . The sections with modulus D0 that are not eventually 0 can then be categorized into two classes. — Let R6=0 be the set of those values of r such that 5[r ] is not identically 0. The set R6=0 is non-empty (else the radius of convergence of S(z) would be larger than α1 .) For any base r ∈ R6=0 , the assertion of the theorem is then established with β = 1/α1 . — Let R0 be the set of those values of r such that 5[r ] (x) ≡ 0, with 5[r ] as given by (14). Then one needs to examine the next layer of poles of S(z), as detailed below. Consider a number r such that r ∈ R0 , so that the polynomial 5[r ] is identically 0. First, we isolate in the expansion of S(z) those indices that are congruent to r modulo D0 . ThisPis achieved by means two power series P of anHadamard product, which, givenP a(z) = an z n and b(z) = bn z , is defined as the series c(z) = cn z n such that

V. 3. REGULAR SPECIFICATIONS AND LANGUAGES

303

cn = an bn and is written c = a ⊙ b. In symbols: X X X (15) an z n ⊙ bn z n = an bn z n . n≥0

n≥0

We have: (16)

g(z) = S(z) ⊙

n≥0

zr 1 − z D0

.

A classical theorem [57, 189] from the theory of positive rational functions (in the sense of Note V.4) asserts that such functions are closed under Hadamard product. (A dedicated construction for (16) is also possible and is left as an exercise to the reader.) Then the resulting function G(z) is of the form g(z) = z r γ (z D0 ),

with the rational function γ (z) being analytic at 0. Note that we have [z ν ]γ (z) = Sν D0 +r , so that γ is exactly the generating function of the section of base r of S(z). One verifies next that γ (z), which is obtained by the substitution z 7→ z 1/D0 in g(z)z −r , is itself a positive rational function. Then, by a fresh application of Berstel’s Theorem (Proposition IV.3, p. 267), this function, if not a polynomial, has a radius of convergence ρ with all its dominant poles σ being such that σ/ρ is a root of unity of order D1 , for some D1 ≥ 1. The argument originally applied to S(z) can thus be repeated, with γ (z) replacing S(z). In particular, one finds at least one section (of modulus D1 ) of the coefficients of γ (z) that admits a pure exponential–polynomial form. The other sections of modulus D1 can themselves be further refined, and so on In other words, successive refinements of the sectioning process provide at each stage at least one pure exponential–polynomial form, possibly leaving a few congruence classes open for further refinements. Define the layer index of a rational function f as the integer κ( f ), such that κ( f ) = card |ζ | f (ζ ) = ∞ . (This index is thus the number of different moduli of poles of f .) It is seen that each successive refinement step decreases by at least 1 the layer index of the rational function involved, thereby ensuring termination of the whole refinement process. Finally, the collection of the iterated sectionings obtained can be reduced to a single sectioning according to a common modulus D, which is the least common multiple of the collection of all the finite products D0 D1 · · · that are generated by the algorithm. For instance the coefficients (Figure V.4) of the function (17)

L(z) =

1 z + , 2 4 (1 − z)(1 − z − z ) 1 − 3z 3

associated to the regular language a ⋆ (bb + cccc)⋆ + d(ddd + eee + f f f )⋆ , exhibit an apparently irregular behaviour, with the expansion of L(z) starting as 1 + 2z + 2z 2 + 2z 3 + 7z 4 + 4z 5 + 7z 6 + 16z 7 + 12z 8 + 12z 9 + 47z 10 + 20z 11 + · · · .

304

V. APPLICATIONS OF RATIONAL AND MEROMORPHIC ASYMPTOTICS

50

8

40 6

30 4 20

2 10

0

0 0

5

10

15

20

25

0

20

40

60

80

100

120

140

Figure V.4. Plots of log Fn with Fn = [z n ]F(z) and F(z) as in (17) display fluctuations that disappear as soon as sections of modulus 6 are considered.

The first term in (17) has a periodicity modulo 2, while the second one has an obvious periodicity modulo 3. In accordance with the theorem, the sections modulo 6 each admit a pure exponential–polynomial form and, consequently, they become easy to describe (Note V.5).

V.5. √ Sections and asymptotic regimes. For the function L(z) of (17), one finds, with ϕ := (1 +

5)/2 and c1 , c2 ∈ R>0 ,

L n = 3−1/3 · 3n/3 + O(ϕ n/2 ) L n = c1 ϕ n/2 + O(1) L n = c2 ϕ n/2 + O(1)

(n ≡ 1, 4 mod 6), (n ≡ 0, 2 mod 6), (n ≡ 3, 5 mod 6),

in accordance with the general form predicted by Theorem V.3.

V.6. Extension to Rat+ functions. The conclusions of Theorem V.3 hold for any function in Rat+ in the sense of Note V.4. V.7. Soittola’s Theorem. This is a converse to Theorem V.3 proved in [546]. Assume that coefficients of an arbitrary rational function f (z) are non-negative and that there exists a sectioning such that each section admits a pure exponential–polynomial form. Then f (z) is in Rat+ in the sense of Note V.4; in particular, f is the OGF of a (weighted) regular class. Theorem V.3 is useful for interpreting the enumeration of regular classes and languages. It serves a similar purpose with regards to structural parameters of regular classes. Indeed, consider a regular specification C augmented with a mark u that is, as usual, a neutral object of size 0 (see Chapter III). We let C(z, u) be the corresponding BGF of C, so that Cn,k = [z n u k ]C(z, u) is the number of C–objects of size n that bear k marks. A suitable placement of marks makes it possible to record the number of times any given construction enters an object. For instance, in the augmented specification of binary words, C = (S EQ 0 and all θ satisfying |θ | ≤ π − φ, there holds

3(ceiθ u) = 1. u→+∞ 3(u) (Powers of logarithms and iterated logarithms are typically slowly varying functions.) Under uniformity assumptions on (22), the following estimate holds [248]: n α−1 1 ∼ 3(n). (23) [z n ](1 − z)−α 3 1−z Ŵ(α) For instance, we have: q p 1 log 1 exp log n exp z 1−z [z n ] . ∼ √ √ πn 1−z See also the discussion of Tauberian theory, p. 435. (22)

lim

VI.6. Iterated logarithms. For a general α 6∈ Z≤0 , the relation (23) admits as a special case

β δ 1 1 1 1 n α−1 1 log log log (log n)β (log log n)δ . ∼ z 1−z z z 1−z Ŵ(α) A full asymptotic expansion can be derived in this case. [z n ](1 − z)−α

Special cases. The conditions of Theorems VI.1 and VI.2 exclude explicitly the case when α is a negative integer: the formulae actually remain valid in this case, provided one interprets them as limit cases, making use of 1/ Ŵ(0) = 1/ Ŵ(−1) = · · · = 0 . Also, when β is a positive integer, the expansion of Theorem VI.2 terminates: in that situation, stronger forms are valid. Such cases are summarized in Figure VI.4 and discussed below. The case of integral α ∈ Z≤0 and general β 6∈ Z≥0 . When α is a negative integer, the coefficients of f (z) = (1 − z)−α eventually reduce to zero, so that the asymptotic coefficient expansion becomes trivial: this situation is implicitly covered by the statement of Theorem VI.1 since, in that case, 1/ Ŵ(α) = 0. When logarithms are present (with α ∈ Z≤0 still), the expansion of Theorem VI.2 regarding β 1 1 f (z) = (1 − z)−α log z 1−z

VI. 2. COEFFICIENT ASYMPTOTICS FOR THE STANDARD SCALE

387

remains valid provided we again take into account the equality 1/ Ŵ(α) = 0 in formula (21) after effecting simplifications by Gamma factors: it is only the first term of (21) that vanishes, and one has D2 D1 n α−1 β + (24) [z ] f (z) ∼ n + ··· , (log n) log n log2 n k 1 β d . For instance, we find where Dk is given by Dk = k ds k Ŵ(s) s=α [z n ]

z 2γ 1 1 + + O( ). =− 2 3 −1 log(1 − z) n log n n log n n log4 n

The case of general α 6∈ Z≤0 and integral β ∈ Z≥0 . When β = k is a nonnegative integer, the error terms can be further improved with respect to the ones predicted by the general statement of Theorem VI.2. For instance, we have: 1 1 log 1−z 1−z 1 1 [z n ] √ log 1 − z 1−z

[z n ]

= ∼

1 1 1 + O( 4 ) log n + γ + − 2 2n 12n n log n 1 log n + γ + 2 log 2 + O( ) . √ n πn

(In such a case, the expansion of Theorem VI.2 terminates since only its first (k + 1) terms are non-zero.) In fact, in the general case of non-integral α, there exists an expansion of the form E 1 (log n) n α−1 1 E 0 (log n) + ∼ + ··· , (25) [z n ](1 − z)−α logk 1−z Ŵ(α) n where the E j are polynomials of degree k, as can be proved by adapting the argument employed for general α (Note VI.8). The joint case of integral α ∈ Z≤0 and integral β ∈ Z≥0 . If α is a negative integer, the coefficients appear as finite differences of coefficients of logarithmic powers. Explicit formulae are then available elementarily from the calculus of finite differences when β is a positive integer. For instance, with α = −m for m ∈ Z≥0 , one has 1 m! (26) [z n ](1 − z)m log = (−1)m . 1−z n(n − 1) · · · (n − m) The case α = −m and β = k (with m, k ∈ Z≥0 ) is covered by (28) in Note VI.7 below: there is a formula analogous to (25), F1 (log n) 1 n m k −m−1 F0 (log n) + (27) [z ](1 − z) log ∼n + ··· , 1−z n but now with deg(F j ) = k − 1. Figure VI.5 provides the asymptotic form of coefficients of a few standard functions illustrating Theorems VI.1 and VI.2 as well as some of the “special cases”.

388

VI. SINGULARITY ANALYSIS OF GENERATING FUNCTIONS

Function

coefficients

(1 − z)3/2

1 1 3 45 1155 + O( 3 )) ( + + 512n 2 n π n 5 4 32n (0) 1 1 3 25 1 −√ ( + + + O( 3 )) 256n 2 n π n 3 2 16n 1 1 γ + 2 log 2 − 2 log n −√ ( log n + + O( )) 3 2 2 n πn 1 1 2 7 + O( 3 )) − (1 + + 2 2 4/3 9n 81n n 3Ŵ( )n

(1 − z) (1 − z)1/2 (1 − z)1/2 L(z) (1 − z)1/3 z/ L(z) 1 log(1 − z)−1 log2 (1 − z)−1 (1 − z)−1/3 (1 − z)−1/2 (1 − z)−1/2 L(z) (1 − z)−1

(1 − z)−1 L(z)

(1 − z)−1 L(z)2 (1 − z)−3/2 (1 − z)−3/2 L(z) (1 − z)−2

(1 − z)−2 L(z)

(1 − z)−2 L(z)2 (1 − z)−3

√

3

1 n log2 n

(−1 +

π 2 − 6γ 2 2γ 1 + + O( 3 )) log n 2 log2 n log n

(0) 1 n 1 1 1 1 (2 log n + 2γ − − 2 + O( 4 )) n n 6n n 1 1 (1 + O( )) n Ŵ( 13 )n 2/3 1 1 5 1 1 + + + O( 4 )) √ (1 − 8n πn 128n 2 1024n 3 n 1 log n + γ + 2 log 2 log n + O( 2 )) √ (log n + γ + 2 log 2 − 8n πn n 1 1 1 1 1 − + + O( 6 )) log n + γ + 2n 12n 2 120n 4 n log n π2 + O( ) log2 n + 2γ log n + γ 2 − 6 n r 1 n 3 7 + O( 3 )) (2 + − 4n 64n 2 n rπ n 3 log n 1 (2 log n + 2γ + 4 log 2 − 4 + + O( )) π 4n n n+1 1 1 n log n + (γ − 1)n + log n + + γ + O( ) 2 n log n π2 + O( )) n(log2 n + 2(γ − 1) log n + γ 2 − 2γ + 2 − 6 n 1 n2 + 3 n + 1 2 2

Figure VI.5. A table of some commonly encountered functions and the asymptotic forms of their coefficients. The following abbreviation is used: 1 L(z) := log . 1−z

VI. 3. TRANSFERS

389

VI.7. The method of Frobenius and Jungen. This is an alternative approach to the case β ∈ Z≥0 (see [360]). Start from the observation that k 1 ∂k −α log (1 − z) = (1 − z)−α , 1−z ∂α k then let the operators of differentiation ( ∂/∂α ) and coefficient extraction ( [z n ] ) commute (this can be justified by Cauchy’s coefficient formula upon differentiating under the integral sign). This yields k ∂k 1 Ŵ(n + α) , = (28) [z n ](1 − z)−α log k 1−z Ŵ(α)Ŵ(n + 1) ∂α which leads to an “exact” formula (Note VI.8 below).

VI.8. Shifted harmonic numbers. Define the α-shifted harmonic number by h n (α) :=

n−1 X j=0

1 . j +α

With L(z) := − log(1 − z), still, one has

n+α−1 h n (α) n n+α−1 [z n ](1 − z)−α L(z)2 = h ′n (α) + h n (α)2 . n (Note: h n (α) = ψ(α + n) − ψ(α), where ψ(s) := ∂s log Ŵ(s).) In particular, 1 1 1 2n [z n ] √ log [2 H2n − Hn ], = n 1−z 4 n 1−z [z n ](1 − z)−α L(z)

=

where Hn ≡ h n (1) is the usual harmonic number.

VI. 3. Transfers Our general objective is to translate an approximation of a function near a singularity into an asymptotic approximation of its coefficients. What is required at this stage is a way to extract coefficients of error terms (known usually in O(·) or o(·) form) in the expansion of a function near a singularity. This task is technically simple as a fairly coarse analysis suffices. As in the previous section, it relies on contour integration by means of Hankel-type paths; see for instance the summary in Equation (12), p. 381, above. A natural extension of the approach of the previous section is to assume the error terms to be valid in the complex plane slit along the real half line R≥1 . In fact, weaker conditions suffice: any domain whose boundary makes an acute angle with the half line R≥1 appears to be suitable. Definition VI.1. Given two numbers φ, R with R > 1 and 0 < φ < π2 , the open domain 1(φ, R) is defined as 1(φ, R) = {z |z| < R, z 6= 1, | arg(z − 1)| > φ}. A domain is a 1–domain at 1 if it is a 1(φ, R) for some R and φ. For a complex number ζ 6= 0, a 1–domain at ζ is the image by the mapping z 7→ ζ z of a 1–domain at 1. A function is 1–analytic if it is analytic in some 1–domain.

390

VI. SINGULARITY ANALYSIS OF GENERATING FUNCTIONS

............ ............. .................... ....... ...... . . . . . . .... .................................. ....... .....(1).......... .... ... ... ... ... ... ........ . ..... ..... ... ... ..φ R .... . ... . ... ..... 0 1............ .. .... ... ... . . . ... . ... ... ......... ... ........... ................ ..... .... ......... ..... ... ....... ...... . . . .......... . . . . ............................

............ ............. .................... ....... ...... . . . . . .... ... .... ... ..... .. .... 1/n ... . r ................. ......θ .... . ... .. ... ....1.......... 0 ... . . . . ... ... ... ... ... . . .... . ..... .... ....... ...... γ . . . . ........... . . . ..........................

Figure VI.6. A 1–domain and the contour used to establish Theorem VI.3.

Analyticity in a 1–domain (Figure VI.6, left) is the basic condition for transfer to coefficients of error terms in asymptotic expansions. Theorem VI.3 (Transfer, Big-Oh and little-oh). Let α, β be arbitrary real numbers, α, β ∈ R and let f (z) be a function that is 1–analytic. (i) Assume that f (z) satisfies in the intersection of a neighbourhood of 1 with its 1–domain the condition 1 β ) . f (z) = O (1 − z)−α (log 1−z Then one has: [z n ] f (z) = O(n α−1 (log n)β ). (ii) Assume that f (z) satisfies in the intersection of a neighbourhood of 1 with its 1–domain the condition 1 β f (z) = o (1 − z)−α (log ) . 1−z Then one has: [z n ] f (z) = o(n α−1 (log n)β ). Proof. (i) The starting point is Cauchy’s coefficient formula, Z dz 1 f (z) n+1 , f n ≡ [z n ] f (z) = 2iπ γ z

where γ is any simple loop around the origin which is internal to the 1–domain of f . We choose the positively oriented contour (Figure VI.6, right) γ = γ1 ∪ γ2 ∪ γ3 ∪ γ4 , with γ1 γ2 γ3 γ4

= = = =

1 z |z − 1| = , | arg(z − 1)| ≥ θ ] n 1 z ≤ |z − 1|, |z| ≤ r, arg(z − 1) = θ n z |z| = r, | arg(z − 1)| ≥ θ ] 1 ≤ |z − 1|, |z| ≤ r, arg(z − 1) = −θ z n

(inner circle) (top line segment) (outer circle) (bottom line segment).

VI. 3. TRANSFERS

391

If the 1 domain of f is 1(φ, R), we assume that 1 < r < R, and φ < θ < the contour γ lies entirely inside the domain of analyticity of f . For j = 1, 2, 3, 4, let Z 1 dz ( j) fn = f (z) n+1 . 2iπ γ j z

π 2,

so that

The analysis proceeds by bounding the absolute value of the integral along each of the four parts. In order to keep notations simple, we detail the proof in the case where β = 0. (1) Inner circle (γ1 ). From trivial bounds, the contribution from γ1 satisfies −α ! 1 1 = O n α−1 , | f n(1) | = O( ) · O n n

as the function is O(n α ) (by assumption on f (z)), the contour has length O(n −1 ), and z −n−1 remains O(1) on this part of the contour. (2) (2) Rectilinear parts (γ2 , γ4 ). Consider the contribution f n arising from the iθ part γ2 of the contour. Setting ω = e , and performing the change of variable z = 1 + ωt n , we find Z ∞ −α t 1 ωt −n−1 (2) K | fn | ≤ dt, 1 + n 2π 1 n for some constant K > 0 such that | f (z)| < K (1−z)−α over the 1–domain, which is granted by the growth assumption on f . From the relation 1 + ωt ≥ 1 + ℜ( ωt ) = 1 + t cos θ, n n n

there results the inequality | f n(2) |

K ≤ Jn n α−1 , 2π

where

Jn =

Z

∞

1

t

−α

t cos θ 1+ n

−n

dt.

For a given α, the integrals Jn are all bounded above by some constant since they admit a limit as n tends to infinity: Z ∞ Jn → t −α e−t cos θ dt. 1

The condition on θ that 0 < θ < π/2 precisely ensures convergence of the integral. Thus, globally, on the part γ2 of the contour, we have | f n(2) | = O(n α−1 ). (4)

A similar bound holds for f n relative to γ4 . (3) Outer circle (γ3 ). There, f (z) is bounded while z −n is of the order of r −n . (3) Thus, the integral f n is exponentially small.

392

VI. SINGULARITY ANALYSIS OF GENERATING FUNCTIONS

In summary, each of the four integrals of the split contour contributes O(n α−1 ). The statement of part (i) of the theorem thus follows, when β = 0. Entirely similar bounding techniques cover the case of logarithmic factors (β 6= 0). (ii) An adaptation of the proof shows that o(.) error terms may be translated similarly. All that is required is a further break-up of the rectilinear part at a distance log2 n/n from 1 (see the discussion surrounding Equation (20), p. 383 or [248] for details). An immediate corollary of Theorem VI.3 is the possibility of transferring asymptotic equivalence from singular forms to coefficients: Corollary VI.1 (sim–transfer). Assume that f (z) is 1–analytic and f (z) ∼ (1 − z)−α ,

as z → 1,

z ∈ 1,

with α 6∈ {0, −1, −2, · · · }. Then, the coefficients of f satisfy [z n ] f (z) ∼

n α−1 . Ŵ(α)

Proof. It suffices to observe that, with g(z) = (1 − z)−α , one has f (z) ∼ g(z)

iff

f (z) = g(z) + o(g(z)),

then apply Theorem VI.1 to the first term, and Theorem VI.3 (little-oh transfer) to the remainder.

VI.9. Transfer of nearly polynomial functions. Let f (z) be 1–analytic and satisfy the singular expansion f (z) ∼ (1 − z)r , where r ∈ Z≥0 . Then, f n = o(n −r −1 ). [This is a direct consequence of the little-oh transfer.]

VI.10. Transfer of large negative exponents. The 1–analyticity condition can be weakened for functions that are large at their singularity. Assume that f (z) is analytic in the open disc |z| < 1, and that in the whole of the open disc it satisfies Then, provided α > 1, one has

f (z) = O((1 − z)−α ).

[z n ] f (z) = O(n α−1 ).

[Hint. Integrate on the circle of radius 1 − n1 ; see also [248].]

VI. 4. The process of singularity analysis In Sections VI. 2 and VI. 3, we have developed a collection of statements granting us the existence of correspondences between properties of a function f (z) singular at an isolated point (z = 1) and the asymptotic behaviour of its coefficients f n = [z n ] f (z). Using the symbol ‘−→’ to represent such a correspondence4 , we 4 The symbol “H⇒” represents an unconditional logical implication and is accordingly used in this

book to represent the systematic correspondence between combinatorial specifications and generating function equations. In contrast, the symbol ‘−→’ represents a mapping from functions to coefficients, under suitable analytic conditions, like those of Theorems VI.1–VI.3.

VI. 4. THE PROCESS OF SINGULARITY ANALYSIS

393

can summarize some of our results relative to the scale {(1 − z)−α , α ∈ C \ Z≤0 } as follows: n α−1 −α f (z) = (1 − z) −→ f = + · · · (Theorem VI.1) n Ŵ(α) f (z) = O((1 − z)−α ) −→ f n = O(n α−1 ) (Theorem VI.3 (i)) f (z) = o((1 − z)−α ) f (z) ∼ (1 − z)−α

−→

f n = o(n α−1 )

−→

fn ∼

(Theorem VI.3 (ii))

n α−1

(Corollary VI.1). Ŵ(α) The important requirement is that the function should have an isolated singularity (the condition of 1–analyticity) and that the asymptotic property of the function near its singularity should be valid in an area of the complex plane extending beyond the disc of convergence of the original series, (in a 1–domain). Extensions to logarithmic powers and special cases like α ∈ Z≤0 are also, as we know, available. We let S denote the set of such singular functions: 1 1 1 ≡ L(z). (29) S = (1 − z)−α λ(z)β α, β ∈ C , λ(z) := log z 1−z z At this stage, we thus have available tools by which, starting from the expansion of a function at its singularity, also called singular expansion, one can justify the termby-term transfer from an approximation of the function to an asymptotic estimate of the coefficients5. We state the following theorem. Theorem VI.4 (Singularity analysis, single singularity). Let f (z) be function analytic at 0 with a singularity at ζ , such that f (z) can be continued to a domain of the form ζ · 10 , for a 1–domain 10 , where ζ · 10 is the image of 10 by the mapping z 7→ ζ z. Assume that there exist two functions σ, τ , where σ is a (finite) linear combination of functions in S and τ ∈ S, so that f (z) = σ (z/ζ ) + O (τ (z/ζ ))

as

z→ζ

Then, the coefficients of f (z) satisfy the asymptotic estimate

in ζ · 10 .

f n = ζ −n σn + O(ζ −n τn⋆ ),

where σn = [z n ]σ (z) has its coefficients determined by Theorems VI.1, VI.2 and τn⋆ = n a−1 (log n)b , if τ (z) = (1 − z)−a λ(z)b . We observe that the statement is equivalent to τn⋆ = [z n ]τ (z), except when a ∈ Z≤0 , where the 1/ Ŵ(a) factor should be omitted. Also, generically, we have τn⋆ = o(σn ), so that orders of growth of functions at singularities are mapped to orders of growth of coefficients. Proof. The normalized function g(z) = f (z/ζ ) is singular at 1. It is 1–analytic and satisfies the relation g(z) = σ (z) + O(τ (z)) as z → 1 within 10 . Theorem VI.3, (i) (the big-Oh transfer) applies to the O-error term. The statement follows finally since [z n ] f (z) = ζ −n [z n ]g(z). 5 Functions with a singularity of type (1 − z)−α , possibly with logarithmic factors, are sometimes called algebraic–logarithmic.

394

VI. SINGULARITY ANALYSIS OF GENERATING FUNCTIONS

Let f (z) be a function analytic at 0 whose coefficients are to be asymptotically analysed. 1. Preparation. This consists in locating dominant singularities and checking analytic continuation. 1a. Locate singularities. Determine the dominant singularities of f (z) (assumed not to be entire). Check that f (z) has a single singularity ζ on its circle of convergence. 1b. Check continuation. Establish that f (z) is analytic in some domain of the form ζ 10 . 2. Singular expansion. Analyse the function f (z) as z → ζ in the domain ζ · 10 and determine in that domain an expansion of the form f (z) = σ (z/ζ ) + O(τ (z/ζ ))

with

z→1

τ (z) = o(σ (z)).

For the method to succeed, the functions σ and τ should belong to the standard scale of functions S = {(1 − z)−α λ(z)β }, with λ(z) := z −1 log(1 − z)−1 . 3. Transfer Translate the main term term σ (z) using the catalogues provided by TheoremsVI.1 and VI.2. Transfer the error term (Theorem VI.3) and conclude that [z n ] f (z) = ζ −n σn + O ζ −n τn⋆ , n→+∞

where σn = [z n ]σ (z) and τn⋆ = [z n ]τ (z) provided the corresponding exponent α 6∈ Z≤0 (otherwise, the factor 1/ Ŵ(α) = 0 should be dropped).

Figure VI.7. A summary of the singularity analysis process (single dominant singularity).

The statement of Theorem VI.4 can be concisely expressed by the correspondence: (30)

f (z) = σ (z/ζ ) + O (τ (z/ζ )) z→1

−→

f n = ζ −n σn + O(ζ −n τn⋆ ). n→∞

The conditions of analytic continuation and validity of the expansion in a 1–domain are essential. Similarly, we have (31)

f (z) = σ (z/ζ )) + o (τ (z/ζ )) z→1

−→

f n = ζ −n σn + o(ζ −n τn⋆ ), n→∞

as a simple consequence of Theorem VI.3, part (ii) (little-oh transfer). The mappings (30) and (31) supplemented by the accompanying analysis constitute the heart of the singularity analysis process summarized in Figure VI.7. Many of the functions commonly encountered in analysis are found to be 1– √ analytic. This fact results from the property of the elementary functions (such as , log, tan) to be continuable to larger regions than what their expansions at 0 imply, as well as to the rich set of composition properties that analytic functions satisfy. Furthermore, asymptotic expansions at a singularity initially determined along the real axis by elementary real analysis often hold in much wider regions of the complex plane. The singularity analysis process is then likely to be applicable to a large number of generating functions that are provided by the symbolic method—most notably the iterative structures described in Section IV. 4 (p. 249). In such cases, singularity analysis greatly refines the exponential growth estimates obtained in Theorem IV.8

VI. 4. THE PROCESS OF SINGULARITY ANALYSIS

395

(p. 251). The condition is that singular expansions should be of a suitably moderate6 growth. We illustrate this situation now by treating combinatorial generating functions obtained by the symbolic methods of Chapters I and II, for which explicit expressions are available. Example VI.2. Asymptotics of 2–regular graphs. This example completes the discussion of Example VI.1, p. 379 relative to the EGF 2

R(z) =

e−z/2−z /4 . √ 1−z

We follow step by step the singularity analysis process, as summarized in Figure VI.7. 2

1. Preparation. The function R(z) being the product of e−z/2−z /4 (that is entire) and of (1 − z)−1/2 (that is analytic in the unit disc) is itself analytic in the unit disc. Also, since (1 − z)−1/2 is 1–analytic (it is well-defined and analytic in the complex plane slit along R≥1 ), R(z) is itself 1–analytic, with a singularity at z = 1. 2. Singular expansion. The asymptotic expansion of R(z) near z = 1 is obtained starting 2 from the standard (analytic) expansion of e−z/2−z /4 at z = 1, 2 e−3/4 e−3/4 e−z/2−z /4 = e−3/4 + e−3/4 (1 − z) + (1 − z)2 − (1 − z)3 + · · · . 4 12

The factor (1 − z)−1/2 is its own asymptotic expansion, clearly valid in any 1–domain. Performing the multiplication yields a complete expansion, (32)

√ e−3/4 e−3/4 e−3/4 + e−3/4 1 − z + R(z) ∼ √ (1 − z)3/2 − (1 − z)5/2 + · · · , 4 12 1−z

out of which terminating forms, with an O–error term, can be extracted. 3. Transfer. Take for instance the expansion of (32) limited to two terms plus an error term. The singularity analysis process allows the transfer of (32) to coefficients, which we can present in tabular form as follows: R(z) e−3/4 √

1

1−z √ + e−3/4 1 − z + O((1 − z)3/2 )

cn ≡ [z n ]R(z) e−3/4 1 n − 1/2 1 + · · · ∼ √ e−3/4 1− + −1/2 8n πn 128n 2 −3/4 −e n − 3/2 3 −3/4 ∼ √ +e 1+ + ··· −3/2 8n 2 π n3 1 . +O n 5/2

Terms are then collected with expansions suitably truncated to the coarsest error term, so that here a three-term expansion results. In the sequel, we shall no longer need to detail such computations and we shall content ourselves with putting in parallel the function’s expansion and the coefficient’s expansion, as in the following correspondence: √ e−3/4 e−3/4 5e−3/4 1 R(z) = √ . −→ cn = √ − √ +e−3/4 1 − z+O (1 − z)3/2 +O π n 8 π n3 n 5/2 1−z 6 For functions with fast growth at a singularity, the saddle-point method developed in Chapter VIII

becomes effectual.

396

VI. SINGULARITY ANALYSIS OF GENERATING FUNCTIONS

√ (1) (2) Here is a numerical check. Set cn := e−3/4 / π n and let cn represent the sum of the first two terms of the expansion of cn . One finds: n (1)

n!cn (2) n!cn n!cn

5

50

500

14.30212 12.51435 12

1.1462888618 · 1063 1.1319602511 · 1063 1.1319677968 · 1063

1.4542120372 · 101132 1.4523942721 · 101132 1.4523943224 · 101132

Clearly, a complete asymptotic expansion in descending powers of n can be obtained in this way. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Example VI.3. Asymptotics of unary–binary trees and Motzkin numbers. Unary–binary trees are unlabelled plane trees that admit the specification and OGF: √ 1 − z − (1 + z)(1 − 3z) U = Z(1 + U + U × U) H⇒ U (z) = . 2z (See Note I.39 (p. 68) and Subsection V. 4 (p. 318) for the lattice path version.) The GF U (z) is singular at z = −1 and z = 1/3, the dominant singularity being at z = 1/3. By branching properties of the square-root function, U (z) is analytic in a 1–domain like the one depicted below:

−1

0

1 3

Around the point 1/3, a singular expansion is obtained by multiplying (1 − 3z)1/2 and the analytic expansion of the factor (1 + z)1/2 /(2z). The singularity analysis process then applies and yields automatically: r √ 3 U (z) = 1 − 31/2 1 − 3z + O((1 − 3z)) −→ Un = 3n + O(3n n −2 ). 4π n 3 Further terms in the singular expansion of U (z) at z = 1/3 provide additional terms in the asymptotic expression of the Motzkin numbers Un ; for instance, the form r 1 3 n 1 − 15 + 505 − 8085 + 505659 + O Un = 3 3 2 3 4 16 n 4π n 512 n 8192 n 524288n n5 results from an expansion of U (z) till O((1 − 3z)11/2 ). The approximation provided by the first three terms is quite good: for n = 10, it estimates f 10 = 835. with an error less than 1. . . . .

VI.11. The population of Noah’s Ark. The number of one-source directed lattice animals (pyramids, Example I.18, p. 80) satisfies ! r 3n 1 1 1+z 1 n −1 = √ +O Pn ≡ [z ] . 1− 2 1 − 3z 16n n2 3π n

VI. 4. THE PROCESS OF SINGULARITY ANALYSIS

The expected size of the base of a random animal in An is ∼ number of animals with a compact source of size k?

q

397

4n 27π . What is the asymptotic

Example VI.4. Asymptotics of children’s rounds. Stanley [550] has introduced certain combinatorial configurations that he has nicknamed “children’s rounds”: a round is a labelled set of directed cycles, each of which has a centre attached. The specification and EGF are 1 R = S ET(Z ⋆ C YC(Z)) H⇒ R(z) = exp z log = (1 − z)−z . 1−z

The function R(z) is analytic in the C-plane slit along R≥1 , as is seen by elementary properties of the composition of analytic functions. The singular expansion at z = 1 is then mapped to an expansion for the coefficients: 1 + log(1 − z) + O((1 − z)1/2 ) 1−z A more detailed analysis yields R(z) =

−→

[z n ]R(z) = 1 −

1 1 [z n ]R(z) = 1 − − 2 (log n + γ − 1) + O n n

log2 n n3

!

1 + O(n −3/2 ). n

,

and an expansion to any order can be easily obtained. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

VI.12. The asymptotic shape of the rounds numbers. A complete asymptotic expansion has the form [z n ]R(z) ∼ 1 −

X P j (log n) , nj j≥1

where P j is a polynomial of degree j − 1. (The coefficients of P j are rational combinations of powers of γ , ζ (2), . . . , ζ ( j − 1).) The successive terms in this expansion are easily obtained by a computer algebra program. Example VI.5. Asymptotics of coefficients of an elementary function. Our final example is meant to show the way rather arbitrary compositions of basic functions can be treated by singularity analysis, much in the spirit of Section IV. 4, p. 249. Let C = Z ⋆ S EQ(C) be the class of general labelled plane trees. Consider the labelled class defined by substitution F = C ◦ C YC(C YC(Z))

H⇒

F(z) = C(L(L(z))).

√ 1 . Combinatorially, F is the class of trees There, C(z) = 21 (1 − 1 − 4z) and L(z) = log 1−z in which nodes are replaced by cycles of cycles, a rather artificial combinatorial object, and s 1 1 F(z) = 1 − 1 − 4 log . 1 2 1 − log 1−z

The problem is first to locate the dominant singularity of F(z), then to determine its nature, which can be done inductively on the structure of F(z). The dominant positive singularity ρ of F(z) satisfies L(L(ρ)) = 1/4 and one has −1/4 −1 . ρ = 1 − ee = 0.198443, given that C(z) is singular at 1/4 and L(z) has positive coefficients. Since L(L(z)) is analytic at ρ, a local expansion of F(z) is obtained next by composition of the singular expansion of C(z) at 1/4 with the standard Taylor expansion of L(L(z)) at ρ. We find 1 C1 ρ −n+1/2 1 1/2 3/2 n F(z) = −C1 (ρ−z) +O (ρ − z) 1+O , −→ [z ]F(z) = √ 3 2 n 2 πn

398

VI. SINGULARITY ANALYSIS OF GENERATING FUNCTIONS 5

1 −1/4

with C1 = e 8 − 2 e

. = 1.26566. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

VI.13. The asymptotic number of trains. Combinatorial trains were introduced in Section IV. 4 (p. 249) as a way to exemplify the power of complex asymptotic methods. One finds that, at its dominant singularity ρ, the EGF Tr(z) is of the form Tr(z) ∼ C/(1 − z/ρ), and, by singularity analysis, [z n ]Tr(z) ∼ 0.11768 31406 15497 · 2.06131 73279 40138n . (This asymptotic approximation is good to 15 significant digits for n = 50, in accordance with the fact that the dominant singularity is a simple pole.)

VI. 5. Multiple singularities The previous section has described in detail the analysis of functions with a single dominant singularity. The extension to functions that have finitely many (by necessity isolated) singularities on their circle of convergence follows along entirely similar lines. It parallels the situation of rational and meromorphic functions in Chapter IV (p. 263) and is technically simple, the net result being: In the case of multiple singularities, the separate contributions from each of the singularities, as given by the basic singularity analysis process, are to be added up. As in (29), p. 393, we let S be the standard scale of functions singular at 1, namely 1 1 S = (1 − z)−α λ(z)β α, β ∈ C , λ(z) := log . z 1−z

Theorem VI.5 (Singularity analysis, multiple singularities). Let f (z) be analytic in |z| < ρ and have a finite number of singularities on the circle |z| = ρ at points ζ j = ρeiθ j , for j = 1 . . r . Assume that there exists a 1–domain 10 such that f (z) is analytic in the indented disc r \ (ζ j · 10 ), D= j=1

with ζ · 10 the image of 10 by the mapping z 7→ ζ z. Assume that there exists r functions σ1 , . . . , σr , each a linear combination of elements from the scale S, and a function τ ∈ S such that f (z) = σ j (z/ζ j ) + O τ (z/ζ j ) as z → ζ j in D. Then the coefficients of f (z) satisfy the asymptotic estimate fn = [z n ]σ

r X j=1

ζ j−n σ j,n + O ρ −n τn⋆ ,

where each σ j,n = j (z) has its coefficients determined by Theorems VI.1, VI.2 and τn∗ = n a−1 (log n)b , if τ (z) = (1 − z)−a λ(z)b .

A function analytic in a domain like D is sometimes said to be star-continuable, a notion that naturally generalizes 1–analyticity for functions with several dominant singularities. Furthermore, a similar statement holds with o–error terms replacing Os.

VI. 5. MULTIPLE SINGULARITIES

399

γ

D: 0

0

Figure VI.8. Multiple singularities (r = 3): analyticity domain (D, left) and composite integration contour (γ , right).

Proof. Just as in the case of a single singularity, the proof bases itself on Cauchy’s coefficient formula Z dz f n = [z n ] f (z) n+1 , z γ where a composite contour γ depicted on Figure VI.8 is used. Estimates on each part of the contour obey exactly the same principles as in the proofs of Theorems VI.1– VI.3. Let γ ( j) be the open loop around ζ j that comes from the outer circle, winds about ζ j and joins again the outer circle; let r be the radius of the outer circle. (i) The contribution along the arcs of the outer circle is O(r −n ), that is, exponentially small. (ii) The contribution along the loop γ (1) (say) separates into Z dz 1 f (z) n+1 = I ′ + I ′′ 2iπ γ (1) Z z Z 1 1 dz dz I ′ := σ1 (z/ζ1 ) n+1 , I ′′ := ( f (z) − σ1 (z/ζ1 )) n+1 . 2iπ γ (1) 2iπ γ (1) z z

The quantity I ′ is estimated by extending the open loop to infinity by the same method as in the proof of Theorems VI.1 and VI.2: it is found to equal ζ1−n σ1,n plus an exponentially small term. The quantity I ′′ , corresponding to the error term, is estimated by the same bounding technique as in the proof of Theorem VI.3 and is found to be O(ρ −n τn⋆ ).

Collecting the various contributions completes the proof of the statement.

Theorem VI.5 expresses that, in the case of multiple singularities, each dominant singularity can be analysed separately; the singular expansions are then each transferred to coefficients, and the corresponding asymptotic contributions are finally collected. Two examples illustrating the process follow. Example VI.6. An artificial example. Let us demonstrate the modus operandi on the simple function ez (33) g(z) = p . 1 − z2

400

VI. SINGULARITY ANALYSIS OF GENERATING FUNCTIONS

There are two singularities at z = +1 and z = −1, with

e e−1 g(z) ∼ √ √ z → +1 and g(z) ∼ √ √ z → −1. 2 1−z 2 1+z The function is clearly star-continuable with the singular expansions being valid in an indented disc. We have e e [z n ] √ √ ∼ √ 2π n 2 1−z

and

e−1 e−1 (−1)n [z n ] √ √ . ∼ √ 2π n 2 1+z

To obtain the coefficient [z n ]g(z), it suffices to add up these two contributions (by Theorem VI.5), so that 1 [z n ]g(z) ∼ √ [e + (−1)n e−1 ]. 2π n If expansions at +1 (respectively −1) are written with an error term, which is of the form O((z − 1)1/2 ) (respectively, O((z + 1)1/2 ), there results an estimate of the coefficients gn = [z n ]g(z), which can be put under the form cosh(1) sinh(1) g2n = √ + O n −3/2 , g2n+1 = √ + O n −3/2 . πn πn

This makes explicit the dependency of the asymptotic form of gn on the parity of the index n. Clearly a full asymptotic expansion can be obtained. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Example VI.7. Permutations with cycles of odd length. Consider the specification and EGF r 1+z 1 1+z = log . F = S ET(C YCodd (Z)) H⇒ F(z) = exp 2 1−z 1−z

The singularities of f are at z = +1 and z = −1, the function being obviously star-continuable. By singularity analysis (Theorem VI.5), we have automatically: 1/2 √2 + O (1 − z)1/2 (z → 1) 21/2 F(z) = −→ [z n ]F(z) = √ + O n −3/2 . 1 − z πn O (1 + z)1/2 (z → −1)

For the next asymptotic order, the singular expansions √ 21/2 − 2−3/2 1 − z + O((1 − z)3/2 ) √ F(z) = 1 −√ z −1/2 1 + z + O((1 + z)3/2 ) 2

(z → 1) (z → −1)

yield

21/2 (−1)n 2−3/2 [z n ]F(z) = √ − + O(n −5/2 ). √ πn π n3 This example illustrates the occurrence of singularities that have different weights, in the sense of being associated with different exponents. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

The discussion of multiple dominant singularities ties well with the earlier discussion of Subsection IV. 6.1, p. 263. In the periodic case where the dominant singularities are at roots of unity, different regimes manifest themselves cyclically depending on congruence properties of the index n, like in the two examples above. When the dominant singularities have arguments that are not commensurate to π (a comparatively rare situation), irregular fluctuations appear, in which case the situation is

VI. 6. INTERMEZZO: FUNCTIONS AMENABLE TO SINGULARITY ANALYSIS

401

similar to what was already discussed, regarding rational and meromorphic functions, in Subsection IV. 6.1. VI. 6. Intermezzo: functions amenable to singularity analysis Let us say that a function is amenable to singularity analysis, or SA for short, if its satisfies the conditions of singularity analysis, as expressed by Theorem VI.4 (single dominant singularity) or Theorem VI.5 (multiple dominant singularities). The property of being of SA is preserved by several basic operations of analysis: we have already seen this feature in passing, when determining singular expansions of functions obtained by sums, products, or compositions in Examples VI.2–VI.5. As a starting example, it is easily recognized that the assumptions of 1–analyticity for two functions f (z), g(z) accompanied by the singular expansions f (z) ∼ c(1 − z)−α , z→1

g(z) ∼ d(1 − z)−δ , z→1

and the condition α, δ 6∈ Z≤0 imply for the coefficients of the sum n α−1 α>δ c Ŵ(α) α−1 n [z n ] ( f (z) + g(z)) ∼ (c + d) α = δ, c + d 6= 0 Ŵ(α) δ−1 n d α < δ. Ŵ(δ) Similarly, for products, we have

[z n ] ( f (z)g(z)) ∼ cd

n α+δ−1 , Ŵ(α + δ)

provided α + δ 6∈ Z≤0 . The simple considerations above illustrate the robustness of singularity analysis. They also indicate that properties are easy to state in the generic case where no negative integral exponents are present. However, if all cases are to be covered, there can easily be an explosion of the number of particular situations, which may render somewhat clumsy the enunciation of complete statements. Accordingly, in what follows, we shall largely confine ourselves to generic cases, as long as these suffice to develop the important mathematical technique at stake for each particular problem. In the remainder of this chapter, we proceed to enlarge the class of functions recognized to be of SA, keeping in mind the needs of analytic combinatorics. The following types of functions are treated in later sections. (i) Inverse functions (Section VI. 7). The inverse of an analytic function is, under mild conditions, of SA type. In the case of functions attached to simple varieties of trees (corresponding to the inversion of y/φ(y)), the singular expansion invariably has an exponent of 21 attached to it (a square-root singularity). This applies in particular to the Cayley tree function, in terms of which many combinatorial structures and parameters can be analysed.

402

VI. SINGULARITY ANALYSIS OF GENERATING FUNCTIONS

(ii) Polylogarithms (Section VI. 8). These functions are the generating functions of simple arithmetic sequences such as (n θ ) for an arbitrary θ ∈ C. The fact that polylogarithms are SA opens the possibility of estimating a large number of sums, which involve √ both combinatorial terms (e.g., binomial coefficients) and elements like n and log n. Such sums appear recurrently in the analysis of cost functionals of combinatorial structures and algorithms. (iii) Composition (Section VI. 9). The composition of SA functions often proves to be itself SA This fact has implications for the analysis of composition schemas and makes possible a broad extension of the supercritical sequence schema treated in Section V. 2, (p. 293). (iv) Differentiation, integration, and Hadamard products (Section VI. 10). These are three operations on analytic functions that preserve the property for a function to be SA. Applications are given to tree recurrences and to multidimensional walk problems. A main theme of this book is that elementary combinatorial classes tend to have generating functions whose singularity structure is strongly constrained—in most cases, singularities are isolated. The singularity analysis process is then a prime technique for extracting asymptotic information from such generating functions. VI. 7. Inverse functions Recursively defined structures lead to functional equations whose solutions may often be analysed locally near singularities. An important case is the one of functions defined by inversion. It includes the Cayley tree function as well as all generating functions associated to simple varieties of trees (Subsections I. 5.1 (p. 65), II. 5.1 (p. 126), and III. 6.2 (p. 193)). A common pattern in this context is the appearance of singularities of the square-root type, which proves to be universal among a broad class of problems involving trees and tree-like structures. Accordingly, by singularity analysis, the square-root singularity induces subexponential factors of the asymptotic form n −3/2 in expansions of coefficients—we shall further develop this theme in Chapter VII, pp. 452–493. Inverse functions. Singularities of functions defined by inversion have been located in Subsection IV. 7.1 (p. 275) and our treatment will proceed from there. The goal is to estimate the coefficients of a function defined implicitly by an equation of the form y(z) . (34) y(z) = zφ(y(z)) or equivalently z= φ(y(z)) The problem of solving (34) is one of functional inversion: we have seen (Lemmas IV.2 and IV.3, pp. 275–277) that an analytic function admits locally an analytic inverse if and only if its first derivative is non-zero. We operate here under the following assumptions: Condition (H1 ). The function φ(u) is analytic at u = 0 and satisfies (35)

φ(0) 6= 0,

[u n ]φ(u) ≥ 0,

φ(u) 6≡ φ0 + φ1 u.

VI. 7. INVERSE FUNCTIONS

403

(As a consequence, the inversion problem is well defined around 0. The nonlinearity of φ only excludes the case φ(u) = φ0 + φ1 u, corresponding to y(z) = φ0 z/(1 − φ1 z).) Condition (H2 ). Within the open disc of convergence of φ at 0, |z| < R, there exists a (then necessarily unique) positive solution to the characteristic equation: (36)

∃τ, 0 < τ < R,

φ(τ ) − τ φ ′ (τ ) = 0.

(Existence is granted as soon as lim xφ ′ (x)/φ(x) > 1 as x → R − ,with R the radius of convergence of φ at 0; see Proposition IV.5, p. 278.) Then (by Proposition IV.5, p. 278), the radius of convergence of y(z) is the corresponding positive value ρ of z such that y(ρ) = τ , that is to say, ρ=

(37)

τ 1 = ′ . φ(τ ) φ (τ )

We start with a calculation indicating in a plain context the occurrence of a square-root singularity. Example VI.8. A simple analysis of the Cayley tree function. The situation corresponding to the function φ(u) = eu , so that y(z) = ze y(z) (defining the Cayley tree function T (z)), is typical of general analytic inversion. From (36), the radius of convergence of y(z) is ρ = e−1 corresponding to τ = 1. The image of a circle in the y–plane, centred at the origin and having radius r < 1, by the function ye−y is a curve of the z–plane that properly contains the circle |z| = r e−r (see Figure VI.9) as φ(y) = e y , which has non-negative coefficients, satisfies for all θ ∈ [−π, +π ], φ(r eiθ ) ≤ φ(r )

the inequality being strict for all θ 6= 0. The following observation is the key to analytic continuation: Since the first derivative of y/φ(y) vanishes at 1, the mapping y 7→ y/φ(y) is angle-doubling, so that the image of the circle of radius 1 is a curve C that has a cusp at ρ = e−1 . (See Figure VI.9; Notes VI.18 and 19 provide interesting generalizations.) This geometry indicates that the solution of z = ye−y is uniquely defined for z inside C, so that y(z) is 1–analytic (see the proof of Theorem VI.6 below). A singular expansion for y(z) is then derived from reversion of the power series expansion of z = ye−y . We have (38)

ye−y = e−1 −

1 1 e−1 (y − 1)2 + (y − 1)3 − (y − 1)4 + · · · . 2e 3e 8

Observe both the absence of a linear term and the presence of a quadratic term (boxed). Then, solving z = ye−y for y gives y−1=

√

2 2(1 − ez)1/2 + (1 − ez) + O((1 − ez)3/2 ), 3

where the square root arises precisely from inversion of the quadratic term. (A full expansion can furthermore be obtained.) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

404

VI. SINGULARITY ANALYSIS OF GENERATING FUNCTIONS

1

1

0.5

0.5

-1

y:

-0.5

0

0.5 0

1

−→

0

z:

-1.5

-1

-0.5

0

0.5

-0.5

-0.5

-1

-1

1

Figure VI.9. The images of concentric circles by the mapping y 7→ z = ye−y . It is seen that y 7→ z = ye−y is injective on |y| ≤ 1 with an image extending beyond the circle |z| = e−1 [in grey], so that the inverse function y(z) is analytically continuable in a 1–domain around z = e−1 . Since the direct mapping ye−y is quadratic at 1 (with value e−1 , see (38)), the inverse function has a square-root singularity at e−1 (with value 1).

Analysis of inverse functions. The calculation of Example VI.8 now needs to be extended to the general case, y = zφ(y). This involves three steps: (i) all the dominant singularities are to be located; (ii) analyticity of y(z) in a 1–domain must be established; (iii) the singular expansion, obtained formally so far and involving a square-root singularity, needs to be determined. Step (i) requires a special discussion and is related to periodicities. A basic example like φ(u) = 1 + u 2 (binary trees), for which y(z) =

1−

√

1 − 4z 2 , 2z

shows that y(z) may have several dominant singularities—here, two conjugate singularities at − 12 and + 12 . The conditions for this to happen are related to our discussion of periodicities in Definition IV.5, p. 266. As a consequence of this definition, φ(u), which satisfies φ(0) 6= 0, is p–periodic if φ(u) = g(u p ) for some power series g (see p. 266) and p ≥ 2; it is aperiodic otherwise. An elementary argument developed in Note VI.17, p. 407, shows that the aperiodicity assumption entails no loss of analytic generality (periodicity does not occur for y(z) unless φ(u) is itself periodic, a case which, in addition, turns out to be reducible to the aperiodic situation). Theorem VI.6 (Singular Inversion). Let φ be a nonlinear function satisfying the conditions (H1 ) and (H2 ) of Equations (35) and (36), and let y(z) be the solution of y = zφ(y) satisfying y(0) = 0. Then, the quantity ρ = τ/φ(τ ) is the radius of convergence of y(z) at 0 (with τ the root of the characteristic equation), and the singular

VI. 7. INVERSE FUNCTIONS

405

expansion of y(z) near ρ is of the form X p y(z) = τ − d1 1 − z/ρ + (−1) j d j (1 − z/ρ) j/2 ,

d1 :=

j≥2

s

2φ(τ ) , φ ′′ (τ )

with the d j being some computable constants. Assume that, in addition, φ is aperiodic7. Then, one has s ! ∞ X φ(τ ) ρ −n ek n 1+ [z ]y(z) ∼ , √ 2φ ′′ (τ ) π n 3 nk k=1

for a family ek of computable constants. Proof. Proposition IV.5, p. 278, shows that ρ is indeed the radius of convergence of y(z). The Singular Inversion Lemma (Lemma IV.3, p. 277) also shows that y(z) can be continued to a neighbourhood of ρ slit along the ray R≥ρ . The singular expansion at ρ is determined as in Example VI.8. Indeed, the relation between z and y, in the vicinity of (z, y) = (ρ, τ ), may be put under the form τ y , (39) ρ − z = H (y), where H (y) := − φ(τ ) φ(y) the function H (y) in the right-hand side being such that H (τ ) = H ′ (τ ) = 0. Thus, the dependency between y and z is locally a quadratic one: ρ−z =

1 1 ′′ H (τ )(y − τ )2 + H ′′′ (τ )(y − τ )3 + · · · . 2! 3!

When this relation is locally inverted: a square-root appears: r h i √ H ′′ (τ ) − ρ−z = (y − τ ) 1 + c1 (y − τ ) + c2 (y − τ )2 + ... . 2 √ The determination with a − should be chosen there as y(z) increases to τ − as z → − ρ . This implies, by solving with respect to y − τ , the relation y − τ ∼ −d1⋆ (ρ − z)1/2 + d2⋆ (ρ − z) − d3⋆ (ρ − z)3/2 + · · · , p where d1⋆ = 2/H ′′ (τ ) with H ′′ (τ ) = τ φ ′′ (τ )/φ(τ )2 . The singular expansion at ρ results. It now remains to exclude the possibility for y(z) to have singularities other than ρ on the circle |z| = ρ, in the aperiodic case. Observe that y(ρ) is well defined (in fact y(ρ) = τ ), so that the series representing y(z) converges at ρ as well as on the whole circle (given positivity of the coefficients). If φ(z) is aperiodic, then so is y(z). Consider any point ζ such that |ζ | = ρ and ζ 6= ρ and set η = y(ζ ). We then have |η| < τ (by the Daffodil Lemma: Lemma IV.1, p. 266). The function y(z) is analytic 7If φ has maximal period p, then one must restrict n to n ≡ 1 mod p; in that case, there is an extra

factor of p in the estimate of yn : see Note VI.17 and Equation (40).

406

VI. SINGULARITY ANALYSIS OF GENERATING FUNCTIONS

Type

φ(u)

binary

(1 + u)2

unary–binary

1 + u + u2

q 1 − 3 13 − z + · · ·

general

(1 − u)−1

1 − 2

Cayley

eu

1−

singular expansion of y(z) q 1 − 4 14 − z + · · · q

coefficient [z n ]y(z)

1 −z 4

√ p 2e e−1 − z + · · ·

4n + O(n −5/2 ) √ π n3 3n+1/2 + O(n −5/2 ) √ 2 π n3 4n−1 + O(n −5/2 ) √ π n3 en + O(n −5/2 ) √ 2π n 3

Figure VI.10. Singularity analysis of some simple varieties of trees.

at ζ by virtue of the Analytic Inversion Lemma (Lemma IV.2, p. 275) and the property that d y 6= 0. dy φ(y) y=η

(This last property is derived from the fact that the numerator of the quantity on the left, φ(η) − ηφ ′ (η) = φ0 − φ2 η2 − 2φ3 η3 − 3φ4 η4 − · · · ,

cannot vanish, by the triangle inequality since |η| < τ .) Thus, under the aperiodicity assumption, y(z) is analytic on the circle |z| = ρ punctured at ρ. The expansion of the coefficients then results from basic singularity analysis. Figure VI.10 provides a table of the most basic varieties of simple trees and the corresponding asymptotic estimates. With Theorem VI.6, we now have available a powerful method that permits us to analyse not only implicitly defined functions but also expressions built upon them. This fact will be put to good use in Chapter VII, when analysing a number of parameters associated to simple varieties of trees.

VI.14. All kinds of graphs. In relation with the classes of graphs listed in Figure II.14, p. 134, one has the following correspondence between an EGF f (z) and the asymptotic form of n![z n ] f (z): function:

2 e T −T /2

coefficient:

e1/2 n n−2

log

1 1−T

1√ 2πn n−1/2 2

√

1 1−T

C1 n n−1/4

1 (1 − T )m C2 n n+(m−1)/2

(m ∈ Z≥1 ; C1 , C2 represent computable constants). In this way, the estimates of Subsection II. 5.3, p. 132, are justifiable by singularity analysis.

VI.15. Computability of singular expansions. Define h(w) :=

s

τ/φ(τ ) − w/φ(w) , (τ − w)2

VI. 7. INVERSE FUNCTIONS

407

√ so that y(z) satisfies ρ − z = (τ −y)h(y). The singular expansion of y can then be deduced by Lagrange inversion from the expansion of the negative powers of h(w) at w = τ . This technique yields for instance explicit forms for coefficients in the singular expansion of y = ze y .

VI.16. Stirling’s formula via singularity analysis. The solution to T = ze T analytic at 0 is

the Cayley tree function. It satisfies [z n ] = n n−1 /n! (by Lagrange inversion) and, at the same time, its singularity is known from Theorem VI.6 and Example VI.8. As a consequence: 139 1 en 1 n n−1 + − · · · . 1− ∼ √ + n! 12 n 288 n 2 51840 n 3 2π n 3

Thus Stirling’s formula also results from singularity analysis.

VI.17. Periodicities. Assume that φ(u) = ψ(u p )with ψ analytic at 0 and p ≥ 2. Let y =

y(z) be the root of y = zφ(y). Set Z = z p and let Y (Z ) be the root of Y = Z ψ(Y ) p . One has by construction y(z) = Y (z p )1/ p , given that y p = z p φ(y) p . Since Y (Z ) = Y1 Z +Y2 Z 2 +· · · , we verify that the non-zero coefficients of y(z) are among those of index 1, 1 + p, 1 + 2 p, . . . . If p is chosen maximal, then ψ(u) p is aperiodic. Then Theorem VI.6 applies to Y (Z ): the function Y (Z ) is analytically continuable beyond its dominant singularity at Z = ρ p ; it has a square root singularity at ρ p and no other singularity on |Z | = ρ p . Furthermore, since Y = Z ψ(Y ) p , the function Y (Z ) cannot vanish on |Z | ≤ ρ p , Z 6= 0. Thus, Y (Z )1/ p is √ analytic in |Z | ≤ ρ p , except at ρ p where it has a branch point. All computations done, we find that d1 ρ −n (40) [z n ]y(z) ∼ p · √ when n ≡ 1 (mod p). 2 π n3 The argument also shows that y(z) has p conjugate roots on its circle of convergence. (This is a kind of Perron–Frobenius property for periodic tree functions.)

VI.18. Boundary cases I. The case when τ lies on the boundary of the disc of convergence of φ may lead to asymptotic estimates differing from the usual ρ −n n −3/2 prototype. Without loss of generality, take φ aperiodic to have radius of convergence equal to 1 and assume that φ is of the form (41)

φ(u) = u + c(1 − u)α + o((1 − u)α ),

with

1 < α ≤ 2,

as u tends to 1 within |u| < 1. (Thus, continuation of φ(u) beyond |u| < 1 is not assumed.) The solution of the characteristic equation φ(τ ) − τ φ ′ (τ ) = 0 is then τ = 1. The function y(z) defined by y = zφ(y) is 1–analytic (by a mapping argument similar to the one exemplified by Figure VI.9 and related to the fact that φ “multiplies” angles near 1). The singular expansion of y(z) and the coefficients then satisfy n −1/α−1 . (42) y(z) = 1 − c−1/α (1 − z)1/α + o (1 − z)1/α −→ yn ∼ c−1/α −Ŵ(−1/α) [The case α = 2 was first observed by Janson [350]. Trees with α ∈ (1, 2) have been investigated in connection with stable L´evy processes [180]. The singular exponent α = 3/2 occurs for instance in planar maps (Subsection VII. 8.2, p. 513), so that GFs with coefficients of the form ρ −n n −5/3 would arise, if considering trees whose nodes are themselves maps.]

VI.19. Boundary cases II. Let φ(u) be the probability generating function of a random variable X with mean equal to 1 and such that φn ∼ λn −α−1 , with 1 < α < 2. Then, by a complex version of an Abelian theorem (see, e.g., [69, §1.7] and [232]), the singular expansion (41) holds when u → 1, |u| < 1, within a cone, so that the conclusions of (42) hold in that case. Similarly, if φ ′′ (1) exists, meaning that X has a second moment, then the estimate (42) holds with α = 2, and then coincides with what Theorem VI.6 predicts [350]. (In probabilistic terms, the condition of Theorem VI.6 is equivalent to postulating the existence of exponential moments for the one-generation offspring distribution.)

408

VI. SINGULARITY ANALYSIS OF GENERATING FUNCTIONS

VI. 8. Polylogarithms √ Generating functions involving sequences such as ( n) or (log n) can be subjected to singularity analysis. The starting point is the definition of the generalized polylogarithm, commonly denoted8 by Liα,r , where α is an arbitrary complex number and r a non-negative integer: X zn Liα,r (z) := (log n)r α , n n≥1

The series converges for |z| < 1, so that the function Liα,r is a priori analytic in the unit disc. The quantity Li1,0 (z) is the usual logarithm, log(1 − z)−1 , hence the established name, polylogarithm, assigned to these functions [406]. In what follows, we make use of the abbreviation Liα,0 (z) ≡ Liα (z), so that Li1 (z) ≡ Li1,0 (z) ≡ log(1−z)−1 is the GF of the sequence (1/n). Similarly, Li0,1 is the GF of the sequence √ (log n) and Li−1/2 (z) is the GF of the sequence ( n). Polylogarithms are continuable to the whole of the complex plane slit along the ray R≥1 , a fact established early in the twentieth century by Ford [268], which results from the integral representation (48), p. 409. They are amenable to singularity analysis [223] and their singular expansions involve the Riemann zeta function defined by ζ (s) =

∞ X 1 , ns n=1

for ℜ(s) > 1, and by analytic continuation elsewhere [578]. Theorem VI.7 (Singularities of polylogarithms). For all α ∈ Z and r ∈ Z≥0 , the function Liα,r (z) is analytic in the slit plane C \ R≥1 . For α 6∈ {1, 2, . . .}, there exists an infinite singular expansion (with logarithmic terms when r > 0) given by the two rules: ∞ X (−1) j X (1 − z)ℓ ζ (α − j)w j , w := Liα (z) ∼ Ŵ(1 − α)w α−1 + j! ℓ (43) j≥0 ℓ=1 r ∂ Liα,r (z) = (−1)r Liα (z) (r ≥ 0). ∂αr The expansion of Liα is conveniently described by the composition of two expansions (Figure VI.11, p. 410): the expansion of w = log z at z = 1, namely, w = (1 − z) + 1 2 2 (1 − z) + · · · , is to be substituted inside the formal power series involving powers of w. The exponents of (1− z) involved in the resulting expansion are {α −1, α, . . .}∪ {0, 1, . . .}. For α < 1, the main asymptotic term of Liα,r is, as z → 1, 1 , Liα,r (z) ∼ Ŵ(1 − α)(1 − z)α−1 L(z)r , L(z) := log 1−z 8The notation Li (z) is nowadays well established. It is evocative of the fact that polylogarithms of α

integer order m ≥ 2 are expressible by a logarithmic integral: Z dt (−1)m−1 1 log(1 − xt) logm−2 t Lim,0 (x) = (m − 1)! 0 t R dt (not to be confused with the unrelated “logarithmic integral function” li(z) := 0z log t ; see [3, p. 228]).

VI. 8. POLYLOGARITHMS

409

while, for α > 1, we have Liα,r (z) ∼ (1−)r ζ (r ) (α), since the sum defining Liα,r converges at 1. Proof. The analysis crucially relies on the Mellin transform (see Appendix B.7: Mellin transforms, p. 762). We start with the case r = 0 and consider several ways in which z may approach the singularity 1. Step (i) below describes the main ingredient needed in obtaining the expansion, the subsequent steps being only required for justifying it in larger regions of the complex plane. (i) When z → 1− along the real line. Set w = − log z and introduce X e−nw (44) 3(w) := Liα (e−w ) = . nα n≥1

This is a harmonic sum in the sense of Mellin transform theory, so that the Mellin transform of 3 satisfies (ℜ(s) > max(0, 1 − α)) Z ∞ (45) 3⋆ (s) ≡ 3(w)w s−1 dw = ζ (s + α)Ŵ(s). 0

The function 3(w) can be recovered from the inverse Mellin integral, Z c+i∞ 1 (46) 3(w) = ζ (s + α)Ŵ(s)w −s ds, 2iπ c−i∞

with c taken in the half-plane in which 3⋆ (s) is defined. There are poles at s = 0, −1, −2, . . . due to the Gamma factor and a pole at s = 1 − α due to the zeta function. Take d to be of the form −m − 21 and smaller than 1 − α. Then, a standard residue calculation, taking into account poles to the left of c and based on X 3(w) = Res ζ (s + α)Ŵ(s)w −s s=s (47)

s0 ∈{0,−1,...,−m}∪{1−α}

+

1 2iπ

Z

0

d+i∞

d−i∞

ζ (s + α)Ŵ(s)w −s ds,

then yields a finite form of the estimate (43) of Liα (as w → 0, corresponding to z → 1− ).

(ii) When z → 1− in a cone of angle less than π inside the unit disc. In that case, we observe that the identity in (46) remains valid by analytic continuation, since the integral there is still convergent (this property owes to the fast decay of Ŵ(s) towards ±i∞). Then the residue calculation (47), on which the expansion of 3(w) is based in the real case w > 0, still makes sense. The extension of the asymptotic expansion of Liα within the unit disc is thus granted. (iii) When z tends to 1 vertically. Details of the proof are given in [223]. What is needed is a justification of the validity of expansion (43), when z is allowed to tend to 1 from the exterior of the unit disc. The key to the analysis is a Lindel¨of integral representation of the polylogarithm (Notes IV.8 and IV.9, p. 237), which provides analytic continuation; namely, Z 1/2+i∞ s z π 1 ds. (48) Liα (−z) = − α 2iπ 1/2−i∞ s sin π s

410

VI. SINGULARITY ANALYSIS OF GENERATING FUNCTIONS

Li−1/2 (z) = Li0 (z)

=

X√ nz n

=

zn

≡

n≥1

X

n≥1

X

√ 3 π 1 π 1/2 − + ζ (− ) + O (1 − z) 2 2(1 − z)3/2 8(1 − z)1/2 √

1 −1 1−z

√ L(z) − γ 1 γ −1 − L(z) + + log 2π + O ((1 − z) L(z)) 1−z 2 2 n≥1 r X zn π 1 1√ √ π 1 − z + O (1 − z)3/2 = Li1/2 (z) = + ζ( ) − √ 1−z 2 4 n n≥1 X log n √ √ L(z) − γ − 2 log 2 1 γ π Li1/2,1 (z) = − ζ( ) + + log 8π + · · · √ zn = π √ 2 2 4 n 1−z n≥1 n Xz ≡ L(z) Li1 (z) = n Li0,1 (z)

=

log n z n =

n≥1

Li2 (z)

=

X zn n2

n≥1

=

π2 1 1 − (L(z) + 1)(1 − z) − ( + L(z))(1 − z)2 + · · · 6 4 2

Figure VI.11. Sample expansions of polylogarithms (L(z) := log(1 − z)−1 ).

The proof then proceeds with the analysis of the polylogarithm when z = ei(w−π ) and s = 1/2 + it, the integral (48) being estimated asymptotically as a harmonic integral (a continuous analogue of harmonic sums [614]) by means of Mellin transforms. The extension to a cone with vertex at 1, having a vertical symmetry and angle less than π , then follows by an analytic continuation argument. By unicity of asymptotic expansions (the horizontal cone of parts (i) and (ii) and the vertical cone have a non-empty intersection), the resulting expansion must coincide with the one calculated explicitly in part (i), above. To conclude, regarding the general case r ≥ 0, we may proceed along similar lines, with each log n factor introducing a derivative of the Riemann zeta function, hence a multiple pole at s = 1. It can then be checked that the resulting expansion coincides with what is given by formally differentiating the expansion of Liα a number of times equal to r . (See also Note VI.20 below.) Figure VI.11 provides a table of expansions relative to commonly encountered polylogarithms (the function Li2 is also known as a dilogarithm). Example VI.9 illustrates the use of polylogarithms for establishing a class of asymptotic expansions of which Stirling’s formula appears as a special case. Further uses of Theorem VI.7 will appear in the following sections. Example VI.9. Stirling’s formula, polylogarithms, and superfactorials. One has X log n! z n = (1 − z)−1 Li0,1 (z), n≥1

VI. 9. FUNCTIONAL COMPOSITION

411

to which singularity analysis is applicable. Theorem VI.7 then yields the singular expansion 1 − L(z) + γ − 1 + log 2π 1 L(z) − γ + Li0,1 (z) ∼ + ··· , 1−z 2 1−z (1 − z)2 from which Stirling’s formula reads off: √ 1 log n! ∼ n log n − n + log n + log 2π + · · · . 2 √ (Stirling’s constant log 2π comes out as neatly −ζ ′ (0).) Similarly, define the superfactorial function to be 11 22 · · · n n . One has X 1 Li−1,1 (z), log(12 22 · · · n n )z n = 1−z n≥1

to which singularity analysis is mechanically applicable. The analogue of Stirling’s formula then reads: 11 22 · · · n n

∼

A

=

1 2 1 1 2 1 An 2n + 2 n+ 12 e− 4n , ′ ζ (2) log(2π ) + γ 1 + . − ζ ′ (−1) = exp − exp 12 12 2π 2

The constant A is known as the Glaisher–Kinkelin constant [211, p. 135]. Higher order factorials can be treated similarly. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

VI.20. Polylogarithms of integral index and a general formula. Let α = m ∈ Z≥1 . Then: Lim (z) =

(−1)m m−1 w (log w − Hm−1 ) + (m − 1)!

X

j≥0, j6=m−1

(−1) j ζ (m − j)w j , j!

where Hm is the harmonic number and w = − log z. [The line of proof is the same as in Theorem VI.7, only the residue calculation at s = 1 differs.] The general formula, X ∂r Liα,r (z) ∼ (−1)r r Res ζ (s + α)Ŵ(s)w−s , w := − log z, ∂α z→1 s∈Z≥0 ∪{1−α}

holds for all α ∈ C and r ∈ Z≥0 and is amenable to symbolic manipulation.

VI. 9. Functional composition Let f and g be functions analytic at the origin that have non-negative coefficients. We consider the composition h = f ◦ g,

h(z) = f (g(z)),

assuming g(0) = 0. Let ρ f , ρg , ρh be the corresponding radii of convergence, and let τ f = f (ρ f ), and so on. We shall assume that f and g are 1–continuable and that they admit singular expansions in the scale of powers. There are three cases to be distinguished depending on the value of τg in comparison with ρ f . — Supercritical case, when τg > ρ f . In that case, when z increases from 0, there is a value r strictly less than ρg such that g(r ) attains the value ρ f , which triggers a singularity of f ◦ g. In other words r ≡ ρh = g (−1) (ρ f ). Around this point, g is analytic and a singular expansion of f ◦ g is obtained by combining the singular expansion of f with the regular expansion of g at r . The singularity type is that of the external function ( f ).

412

VI. SINGULARITY ANALYSIS OF GENERATING FUNCTIONS

— Subcritical case, when τg < ρ f . In this dual situation, the singularity of f ◦g is driven by that of the inside function g. We have ρh = ρg , τh = f (ρg ) and the singular expansion of f ◦ g is obtained by combining the regular expansion of f with the singular expansion of g at ρg . The singularity type is that of the internal function (g). — Critical case, when τg = ρ f . In this boundary case, there is a confluence of singularities. We have ρh = ρg , τh = τ f , and the singular expansion is obtained by applying the composition rules of the singular expansions involved. The singularity type is a mix of the types of the internal and external functions ( f, g). This classification extends the notion of a supercritical sequence schema in Section V. 2, p. 293, for which the external function reduces to f (z) = (1 − z)−1 , with ρ f = 1. In this chapter, we limit ourselves to discussing examples directly, based on the guidelines above supplemented by the plain algebra of generalized power series expansions. Finer probabilistic properties of composition schemas are studied at several places in Chapter IX starting on p. 629. Example VI.10. “Supertrees”. Let G be the class of general Catalan trees: √ 1 G = Z × S EQ(G) H⇒ G(z) = (1 − 1 − 4z). 2 The radius of convergence of G(z) is 1/4 and the singular value is G(1/4) = 1/2. The class ZG consists of planted trees, which are such that to the root is attached a stem and an extra node, with OGF equal to zG(z). We then introduce two classes of supertrees defined by substitution: H = G[ZG] K = G[(Z + Z)G]

H⇒ H⇒

H (z) = G(zG(z)) K (z) = G(2zG(z)).

These are “trees of trees”: the class H is formed of trees such that, on each node there is grafted a planted tree (by the combinatorial substitution of Section I. 6, p. 83); the class K similarly corresponds to the case when the stems can be of any two colours. Incidentally, combinatorial sum expressions are available for the coefficients, Hn =

⌊n/2⌋ X k=1

⌊n/2⌋ X 2k 2k − 22n − 3k − 1 1 2k − 2 2n − 3k − 1 , Kn = , n−k k−1 n−k−1 n−k k−1 n−k−1 k=1

the initial values being given by H (z) = z 2 + z 3 + 3z 4 + 7z 5 + 21z 6 + · · · ,

K (z) = 2z 2 + 2z 3 + 8z 4 + 18z 5 + 64z 6 + · · · .

Since ρG = 1/4 and τG = 1/2, the composition scheme is subcritical in the case of H and critical in the case of K. In the first case, the singularity is of square-root type and one finds easily: r √ 2− 2 1 1 4n H (z) ∼ −√ − z, −→ Hn ∼ √ . 4 8 4 8 2π n 3/2 z→ 1 4

In the second case, the two square-roots combine to produce a fourth root: 1/4 1 1 1 4n . −√ −z K (z) ∼ −→ Kn ∼ 2 4 8Ŵ( 43 )n 5/4 z→ 14 2

VI. 9. FUNCTIONAL COMPOSITION

413

Figure VI.12. A binary supertree is a “tree of trees”, with component trees all binary. The number of binary supertrees with 2n nodes has the unusual asymptotic form c4n n −5/4 . On a similar register, consider the class B of complete binary trees: p 1 − 1 − 4z 2 B = Z + Z × B × B H⇒ B(z) = , 2z and define the class of binary supertrees (Figure VI.12) by q p 1 − 2 1 − 4z 2 − 1 + 4z 2 p . S = B (Z × B) H⇒ S(z) = 1 − 1 − 4z 2

The composition is critical since z B(z) = 21 at the dominant singularity z = 21 . It is enough to consider the reduced function √ S(z) = S( z) = z + z 2 + 3z 3 + 8z 4 + 25z 5 + 80z 6 + 267z 7 + 911z 8 + · · · , whose coefficients constitute EIS A101490 and occur in Bousquet-M´elou’s study of integrated superbrownian excursion [83]. We find ! √ √ 4n 2 1 1/4 1/2 + ··· . S(z) ∼ 1− 2(1−4z) +(1−4z) +· · · −→ S n = 5/4 − √ n 4Ŵ( 3 ) 2 π n 1/4 4

For instance, a seven-term expansion yields a relative accuracy better than 10−4 for n ≥ 100, so that such approximations are quite usable in practice. The occurrence of the exponent − 54 in the enumeration of bicoloured and binary supertrees is noteworthy. Related constructions have been considered by Kemp [364] who obtained more generally exponents of the form −1 − 2−d by iterating the substitution construction (in connection with so-called “multidimensional trees”). It is significant that asymptotic terms of the form n p/q with q 6= 1, 2 appear in elementary combinatorics, even in the context of simple algebraic functions. Such exponents tend to be associated with non-standard limit laws, akin to the stable distributions of probability theory: see our discussion in Section IX. 12, p. 715. . . . . . . . . . . .

VI.21. Supersupertrees. Define supersupertrees by S [2] (z) = B(z B(z B(z))). We find automatically (with the help of B. Salvy’s program) −1 7 2n+1 [2] −13/4 4n n −9/8 , [z ]S (z) ∼ 2 Ŵ 8

414

VI. SINGULARITY ANALYSIS OF GENERATING FUNCTIONS

and further extensions involving an asymptotic term n −1−2

−d

are possible [364].

VI.22. Valuated trees. Consider the family of (rooted) general plane trees, whose vertices are decorated by integers from Z≥0 (called “values”) and such that the values of two adjacent vertices differ by ±1. Size is taken to be the number of edges. Let T j be the class of valuated trees whose root has value j and T = ∪T j . The OGFs T j (z) satisfy the system of equations T j = 1 + z(T j−1 + T j+1 )T j ,

so that T (z) solves T = 1 + 2zT 2 and is a simple variant of the Catalan OGF: √ 1 − 1 − 8z T (z) = . 4z Bouttier, Di Francesco, and Guitter [90, 91] found an amazing explicit form for the T j ; namely, (1 − Y j+1 )(1 − Y j+5 ) (1 + Y )4 , with Y = z . j+2 j+4 (1 − Y )(1 − Y ) 1 + Y2 In particular, each T j is an algebraic function. The function T0 counts maps (p. 513) that are Eulerian triangulations, or dually bipartite trivalent maps. The coefficients of the T j as well as the distributions of labels in such trees can be analysed asymptotically: see Bousquet-M´elou’s article [83] for a rich set of combinatorial connections. Tj = T

Schemas. Singularity analysis also enables us to discuss at a fair level of generality the behaviour of schemas, in a way that parallels the discussion of the supercritical sequence schema, based on a meromorphic analysis (Section V. 2, p. 293). We illustrate this point here by means of the supercritical cycle schema. Deeper examples relative to recursively defined structures are developed in Chapter VII. Example VI.11. Supercritical cycle schema. The schema H = C YC(G) forms labelled cycles from basic components in G: H = C YC(G)

H⇒

H (z) = log

1 . 1 − G(z)

Consider the case where G attains the value 1 before becoming singular, that is, τG > 1. This corresponds to a supercritical composition schema, which can be discussed in a way that closely parallels the supercritical sequence schema (Section V. 2, p. 293): a logarithmic singularity replaces a polar singularity. Let σ := ρ H , which is determined by G(σ ) = 1. First, one finds: H (z) ∼ log z→σ

1 − log(σ G ′ (σ )) + A(z), 1 − z/σ

where A(z) is analytic at z = σ . Thus:

σ −n . n (The error term implicit in this estimate is exponentially small). The BGF H (z, u) = log(1 − uG(z))−1 has the variable u marking the number of components in H–objects. In particular, the mean number of components in a random H–object of size n is ∼ λn, where λ = 1/(σ G ′ (σ )), and the distribution is concentrated around its mean. Similarly, the mean number of components with size k in a random Hn object is found to be asymptotic to λgk σ k , where gk = [z k ]G(z). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . [z n ]H (z) ∼

VI. 9. FUNCTIONAL COMPOSITION

415

Weights fk

1 k

f (z)

1 log 1−z

(49)

1 2k

4k k √1 1−z

1

Hk

k

k2

1 1−z

1 1 1−z log 1−z

z (1−z)2

z+z 2 . (1−z)3

Triangular arrays (k)

(50)

gn

g(z)

n−1

k n−k

k n−k

k−1

(n−k)!

z 1−z

ze z

z(1 + z)

k 2n−k−1 n n−1 √ 1− 1−4z 2

k 2n n n−k √ 1−2z− 1−4z 2z

n−k−1

k n(n−k)! T (z)

Figure VI.13. Typical weights (top) and triangular arrays (bottom) illustrating the P (k) discussion of combinatorial sums Sn = nk=1 f k gn .

Combinatorial sums. Singularity analysis permits us to discuss the asymptotic behaviour of entire classes of combinatorial sums at a fair level of generality, with asymptotic estimates coming out rather automatically. We examine here combinatorial sums of the form n X f k gn(k) , Sn = k=0

where f k is a sequence of numbers, usually of a simple form and called the weights, (k) while the gn are a triangular array of numbers, for instance Pascal’s triangle. As weights f k we shall consider sequences such that f (z) is 1–analytic with a singular expansion involving functions of the standard scale of Theorems VI.1, VI.2, VI.3. Typical examples9 for f (z) and ( f k ) are displayed in Figure VI.13, Equation (49). The triangular arrays discussed here are taken to be coefficients of the powers of some fixed function, namely, gn(k)

n

= [z ](g(z))

k

where

g(z) =

∞ X

gn z n ,

n=1

with g(z) an analytic function at the origin having non-negative coefficients and satisfying g(0) = 0. Examples are given in Figure VI.13, Equation (50). An interesting class of such arrays arises from the Lagrange Inversion Theorem (p. 732). Indeed, if g(z) is implicitly defined by g(z) = zG(g(z)), one has gn,k = nk [w n−k ]G(w)n ; the last three cases of (50) are obtained in this way (by taking G(w) as 1/(1 − w), (1 + w)2 , ew ). By design, the generating function of the Sn is simply S(z) =

∞ X n=0

Sn z n = f (g(z))

with

f (z) =

∞ X

fk zk .

k=0

Consequently, the asymptotic analysis of Sn results by inspection from the way singularities of f (z) and g(z) get transformed by composition. 9Weights such as log k and

√ k, also satisfy these conditions, as seen in Section VI. 8.

416

VI. SINGULARITY ANALYSIS OF GENERATING FUNCTIONS

Example VI.12. Bernoulli sums. Let φ be a function from Z≥0 to R and write f k := φ(k). Consider the sums n X 1 n Sn := φ(k) n . 2 k

k=0 10 If X n is a binomial random variable , X n ∈ Bin(n, 21 ), then Sn = E(φ(X n )) is exactly the expectation of φ(X n ). Then, by the binomial theorem, the OGF of the sequence (Sn ) is:

2 z . f 2−z 2−z Considering weights whose generating function, as in (49), has radius of convergence 1, what we have is a variant of the composition schema, with an additional prefactor. The composition scheme is of the supercritical type since the function g(z) = z/(2 − z), which has radius of convergence equal to 2, satisfies τg = ∞. The singularities of S(z) are then of the same type as those of the weight generating function f (z) and one verifies, in all cases of (49), that, to first asymptotic order, Sn ∼ φ(n/2): this is in agreement with the fact that the binomial distribution is concentrated near its mean n/2. Singularity analysis furthermore provides complete asymptotic expansions; for instance, 1 6 2 2 E Xn > 0 = + 2 + 3 + O(n −4 ) Xn n n n n 1 1 E HXn + O(n −3 ). = log + γ + − 2 2n 12n 2 See [208, 223] for more along these lines. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . S(z) =

Example VI.13. Generalized Knuth–Ramanujan Q-functions. For reasons motivated by analysis of algorithms, Knuth has encountered repeatedly sums of the form n(n − 1) n n(n − 1)(n − 2) Q n ({ f k }) = f 0 + f 1 + f 2 + f3 + ··· . n n2 n3 (See, e.g., [384, pp. 305–307].) There ( f k ) is a sequence of coefficients (usually of at most polynomial growth). For instance, the case f k ≡ 1 yields the expected time until the first collision in the birthday paradox problem (Section II. 3, p. 114). A closer examination shows that the analysis of such Q n is reducible to singularity analysis. Writing n! X n n−k−1 Q n ({ f k }) = f 0 + n−1 fk (n − k)! n k≥1

reveals the closeness with the last column of (50). Indeed, setting X fk F(z) = zk , k k≥1

one has (n ≥ 1)

n! Q n = f 0 + n−1 [z n ]S(z) where S(z) = F(T (z)), n and T (z) is the Cayley tree function (T = ze T ). For weights f k = φ(k) of polynomial growth, the schema is critical. Then, the singular expansion of S is obtained by composing the singular expansion of f with the expansion of T , 10 A binomial random variable (p. 775) is a sum of Bernoulli variables: X = Pn n j=1 Y j , where the

Y j are independent and distributed as a Bernoulli variable Y , with P(Y = 1) = p, P(Y = 0) = q = 1 − p.

VI. 9. FUNCTIONAL COMPOSITION

417

√ √ namely, T (z) ∼ 1− 2 1 − ez as z → e−1 . For instance, if φ(k) = k r for some integer r ≥ 1 then F(z) has an r th order pole at z = 1. Then, the singularity type of F(T (z)) is Z −r/2 where Z = (1 − ez), which is reflected by Sn ≍ en nr/2−1 (we use ‘≍’ to represent order-of-growth information, disregarding multiplicative constants). After the final normalization, we see that Q n ≍ n (r√+1)/2√ . Globally, for many weights of the form f k = φ(k), we expect Q n to be of the form nφ( n), in accordance with √ the fact that the expectation of the first collision in the birthday problem is on average near π n/2. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

VI.23. General Bernoulli sums. Let X n ∈ Bin(n; p) be a binomial random variable with general parameters p, q:

P(X n = k) =

n k n−k p q , k

Then with f k = φ(k), one has E(φ(X n )) = [z n ]

1 f 1 − qz

q = 1 − p.

pz 1 − qz

,

so that the analysis develops as in the case Bin(n; 12 ).

VI.24. Higher moments of the birthday problem. Take the model where there are n days in the year and let B be the random variable representing the first birthday collision. Then Pn (B > k) = k!n −k nk , and En (8(B)) = 8(1) + Q n ({18(k)}),

where

18(k) := 8(k + 1) − 8(k).

For instance En (B) = 1 + Q n (h1, 1, . . .i). We thus get moments of various functionals (here stated to two asymptotic terms) 8(x)

x q

E n (8(B)) via singularity analysis.

πn + 2 2 3

x2 + x 2n + 2

x3 + x2 q 3 3 π2n − 2n

x4 + x3 q 3 8n 2 − 7 π2n

VI.25. How to weigh an urn? The “shake-and-paint” algorithm. You are given an urn containing an unknown number N of identical looking balls. How to estimate this number in much fewer than O(N ) operations? A probabilistic solution due to Brassard and Bratley [92] uses a brush and some paint. Shake the urn, pull out a ball, then mark it with paint and replace it into the urn. Repeat until you find an already painted ball. Let X be the number of operations. √ One has E(X ) ∼ π N /2. Furthermore the quantity Y := X 2 /2 constitutes, by the previous note, an asymptotically unbiased estimator of N , in the sense that E(Y ) ∼ N . In other words, count the time√till an already painted ball is first found, and return half of the square of this time. One also has V(Y ) ∼ N . By performing the experiment m times (using m different colours of paint) and by taking the arithmetic average √ of the m estimates, one obtains an unbiased estimator whose typical relative accuracy is 1/m. For instance, m = 16 gives an accuracy of 25%. (Similar principles are used in the design of data mining algorithms.)

VI.26. Catalan sums. These are defined by Sn :=

X

k≥0

2n , fk n−k

S(z) = √

1 1 − 4z

f

! √ 1 − 2z − 1 − 4z . 2z

The case when ρ f = 1 corresponds to a critical composition, which can be discussed much in the same way as Ramanujan sums.

418

VI. SINGULARITY ANALYSIS OF GENERATING FUNCTIONS

VI. 10. Closure properties At this stage11, we have available composition rules for singular expansions under operations such as ±, ×, ÷: these are induced by corresponding rules for extended formal power series, where generalized exponents and logarithmic factors are allowed. Also, from Section VI. 7, inversion of analytic functions normally gives rise to squareroot singularities, and, from Section VI. 9, functions amenable to singularity analysis are essentially closed under composition. In this section we show that functions amenable to singularity analysis (SA functions) satisfy explicit closure properties under differentiation, integration, and Hadamard product. (The contents are liberally borrowed from an article of Fill, Flajolet, and Kapur [208], to which we refer for details.) In order to keep the developments simple, we shall mostly restrict attention to functions that are 1–analytic and admit a simple singular expansion of the form f (z) =

(51)

J X j=0

c j (1 − z)α j + O((1 − z) A ),

or a simple singular expansion with logarithmic terms (52)

f (z) =

J X j=0

c j (L(z)) (1 − z)α j + O((1 − z) A ),

L(z) := log

1 , 1−z

where each c j is a polynomial. These are the cases most frequently occurring in applications (the proof techniques are easily extended to more general situations). Subsection VI. 10.1 treats differentiation and integration; Subsection VI. 10.2 presents the closure of functions that admit simple expansions under Hadamard product. Finally, Subsection VI. 10.3 concludes with an examination of several interesting classes of tree recurrences, where all the closure properties previously established are put to use in order to quantify precisely the asymptotic behaviour of recurrences that are attached to tree models. VI. 10.1. Differentiation and integration. Functions that are SA happen to be closed under differentiation, this is in sharp contrast with real analysis. In the simple cases12 of (51) and (52), closure under integration is also granted. The general principle (Theorems VI.8 and VI.9 below) is the following: Derivatives and primitives of functions that are amenable to singularity analysis admit singular expansions obtained term by term, via formal differentiation and integration. The following statement is a version, tuned to our needs, of well-known differentiability properties of complex asymptotic expansions (see, e.g., Olver’s book [465, p. 9]). 11This section represents supplementary material not needed elsewhere in the book, so that it may be

omitted on first reading. 12 It is possible but unwieldy to treat a larger class, which then needs to include arbitrarily nested R R logarithms, since, for instance, d x/x = log x, d x/(x log x) = log log x, and so on.

VI. 10. CLOSURE PROPERTIES

419

radius: κ |1 − z| z

φ′

φ

1 Figure VI.14. The geometry of the contour γ (z) used in the proof of the differentiation theorem.

Theorem VI.8 (Singular differentiation). Let f (z) be 1–analytic with a singular expansion near its singularity of the simple form f (z) =

J X j=0

c j (1 − z)α j + O((1 − z) A ). r

d Then, for each integer r > 0, the derivative dz r f (z) is 1–analytic. The expansion of the derivative at the singularity is obtained through term-by-term differentiation: J

X Ŵ(α j + 1) dr r f (z) = (−1) (1 − z)α j −r + O((1 − z) A−r ). cj r dz Ŵ(α j + 1 − r ) j=0

Proof. All that is required is to establish the effect of differentiation on error terms, which is expressed symbolically as d O((1 − z) A ) = O((1 − z) A−1 ). dz By bootstrapping, only the case of a single differentiation (r = 1) needs to be considered. Let g(z) be a function that is regular in a domain 1(φ, η) where it is assumed to satisfy g(z) = O((1 − z) A ) for z ∈ 1. Choose a subdomain 1′ := 1(φ ′ , η′ ), where φ < φ ′ < π2 and 0 < η′ < η. By elementary geometry, for a sufficiently small κ > 0, the disc of radius κ|z −1| centred at a value z ∈ 1′ lies entirely in 1; see Figure VI.14. We fix such a small value κ and let γ (z) represent the boundary of that disc oriented positively. The starting point is Cauchy’s integral formula Z 1 dw , (53) g ′ (z) = g(w) 2πi C (w − z)2 a direct consequence of the residue theorem. Here C should encircle z while lying inside the domain of regularity of g, and we opt for the choice C ≡ γ (z). Then trivial

420

VI. SINGULARITY ANALYSIS OF GENERATING FUNCTIONS

bounds applied to (53) give |g ′ (z)| =

=

O ||γ (z)|| · (1 − z) A |1 − z|−2 O |1 − z| A−1 .

The estimate involves the length of the contour, ||γ (z)||, which is O(1 − z) by construction, as well as the bound on g itself, which is O((1 − z) A ) since all points of the contour are themselves at a distance exactly of the order of |1 − z| from 1.

VI.27. Differentiation and logarithms. Let g(z) satisfy g(z) = O (1 − z) A L(z)k ,

L(z) = log

for k ∈ Z≥0 . Then, one has

1 , 1−z

dr g(z) = O (1 − z) A−r L(z)k . r dz (The proof is similar to that of Theorem VI.8.)

It is well known that integration of asymptotic expansions is usually easier than differentiation. Here is a statement custom-tailored to our needs. Theorem VI.9 (Singular integration). Let f (z) be 1–analytic and admit an expansion near its singularity of the form f (z) = Rz

J X j=0

c j (1 − z)α j + O((1 − z) A ).

Then 0 f (t) dt is 1–analytic. Assume further that none of the quantities α j and A equal −1. R (i) If A < −1, then the singular expansion of f is Z z J X cj (1 − z)α j +1 + O (1 − z) A+1 . (54) f (t) dt = − αj + 1 0 j=0

R (ii) If A > −1, then the singular expansion of f is Z z J X cj (1 − z)α j +1 + L 0 + O (1 − z) A+1 , (55) f (t) dt = − αj + 1 0 j=0

where the “integration constant” L 0 has the value Z 1h i X X cj L 0 := + f (t) − c j (1 − t)α j dt. αj + 1 0 α j 1) = α n+α = (α < 1) α

costs ( f n ) α−1 α+1 n+α nα −n+1 ∼ α α+1 α − 1 Ŵ(α + 1) 1−α−1 1+α n+α n+1− ∼ n 1+α α 1−α α+1 α n + O(n α−1 ) fn = α−1 α+1 α n + O(n) fn = α−1 α K α n + O(n )

tn = n α

(2 < α)

tn = n α

(1 < α < 2)

tn = n α

(0 < α < 1)

K 0′ n − log n + O(1)

tn = log n

Figure VI.17. Tolls and costs for the binary search tree recurrence, with t0 = 0. Thus, the singular element (1 − z)β corresponds to a contribution β n−β −1 −c , β + 2 −β − 1

which is of order O(n −β−1 ). This chain of operations suffices to determine the leading order of f n when tn = n α and α > 1. The derivation above is representative of the main lines of the analysis, but it has left aside the determination of integration constants, which play a dominant rˆole when tn = n α and α < 1 (because a term of the form K /(1 − z)2 then dominates in f (z)). Introduce, in accordance with the statement of the Singular Integration Theorem (Theorem VI.9, p. 420) the quantity Z 1 ′ 2 ′ 2 K[t] := t (w)(1 − w) − t (w)(1 − w) dw, −

0

where f − represents the sum of singular terms of exponent < −1 in the singular expansion of f (z). Then, for tn = n α with 0 < α < 1, taking into account the integration constant (which gets multiplied by (1 − z)−2 , given the shape of L), we find for α < 1: f n ∼ K α n,

K α = K[Li−α ] = 2

∞ X

n=1

nα . (n + 1)(n + 2)

Similarly, the toll tn = log n gives rise to f n ∼ K 0′ n,

K 0′ = 2

∞ X

n=1

log n . = 1.2035649167. (n + 1)(n + 2)

This last estimate quantifies the entropy of the distribution of binary search trees, which is studied by Fill in [207], and discussed in the reference book by Cover and Thomas on information theory [134, p. 74-76]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Example VI.16. The binary tree recurrence. Consider a procedure that, given a (pruned) binary tree, performs certain calculations (without affecting the tree itself) at a cost of tn , for size n, then recursively calls itself on the left and right subtrees. If the binary tree to which the

VI. 10. CLOSURE PROPERTIES

Tolls (tn ) nα

costs ( f n )

( 23 < α)

Ŵ(α − 21 ) α+1/2 n + O(n α−1/2 ) Ŵ(α)

( 21 < α < 32 )

2 √ n 2 + O(n log n) π Ŵ(α − 21 ) α+1/2 n + O(n) Ŵ(α)

n 3/2 nα

1 √ n log n + O(n) π

n 1/2 nα

431

(0 < α < 21 )

log n

K α n + O(1) √ ′ K 0 n + O( n

Figure VI.18. Tolls and costs for the binary tree recurrence.

procedure is applied is drawn uniformly among all binary trees of size n the expectation of the total cost of the procedure satisfies the recurrence (69)

f n = tn +

n−1 X k=0

Ck Cn−1−k ( f k + f n−k ) Cn

with Cn =

1 2n . n+1 n

Indeed, the quantity pn,k =

Ck Cn−1−k Cn

represents the probability that a random tree of size n has a left subtree of size k and a right subtree of size n − k. It is then natural to introduce the generating functions X X t (z) = tn C n z n , f (z) = f n Cn z n , n≥0

n≥0

and the recurrence (69) translates into a linear equation: f (z) = t (z) + 2zC(z) f (z), with C(z) the OGF of Catalan numbers. Now, given a toll sequence (tn ) with ordinary generation function X τ (z) := tn z n , n≥0

the function t (z) is a Hadamard product: t (z) = τ (z)⊙C(z). Furthermore, C(z) is well known, so that the fundamental relation is √ 1 − 1 − 4z τ (z) ⊙ C(z) , C(z) = . (70) f (z) = L[τ (z)], where L[τ (z)] = √ 2z 1 − 4z This transform relates the ordinary generating function of tolls to the normalized generating function of the total costs via a Hadamard product.

432

VI. SINGULARITY ANALYSIS OF GENERATING FUNCTIONS

Tolls (tn ) nα

( 23 < α)

n 3/2 nα

( 21 < α < 32 )

n 1/2 nα log n

(0 < α < 21 )

costs ( f n ) Ŵ(α − 12 ) α+1/2 n + O(n α−1/2 ) √ 2Ŵ(α) r 2 2 n + O(n log n) π Ŵ(α − 12 ) α+1/2 n + O(n) √ 2Ŵ(α) 1 √ n log n + O(n) 2π b K α n + O(1) √ b′ n + O( n) K 0

Figure VI.19. Tolls and costs for the Cayley tree recurrence. The calculation for simple tolls like nr with r ∈ Z≥0 can be carried out elementarily. For the tolls tnα = n α what is required is the singular expansion of ∞ z X z n α 2n z n = Li−α (z) ⊙ C = . τ (z) ⊙ C 4 4 n+1 n 4 n=1

This is precisely covered by Theorems VI.7 (p. 408), VI.10 (p. 422), and VI.11 (p. 423). The results of Figure VI.18 follow, after routine calculations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Example VI.17. The Cayley tree recurrence. Consider n vertices labelled 1, . . . , n. There are (n − 1)!n n−2 sequences of edges, hu 1 , v 1 , i, hu 2 , v 2 , i, · · · , hu n−1 , v n−1 i,

that give rise to a tree over {1, . . . , n}, and the number of such sequences is (n − 1)!n n−2 since there are n n−2 unrooted trees of size n. At each stage k, the edges numbered 1 to k determine a forest. Each addition of an edge connects two trees [that then become rooted] and reduces the number of trees in the forest by 1, so that the forest evolves from the totally disconnected graph (at time 0) to an unrooted tree (at time n − 1). If we consider each of the sequences to be equally likely, the probability that u n−1 and v n−1 belong to components of size k and (n − k) is k−1 (n − k)n−k−1 n k 1 . 2(n − 1) k n n−2

(The reason is that there are k k−1 rooted trees of size k; the last added edge has n−1 possibilities and 2 possible orientations.) Assume that the aggregation of two trees into a tree of size equal to ℓ incurs a toll of tℓ . The total cost of the aggregation process for a final tree of size n satisfies the recurrence k−1 X n k (n − k)n−k−1 1 (71) f n = tn + pn,k ( f k + f n−k ), pn,k = . 2(n − 1) k n n−2 0 0, one has

VI. 11. TAUBERIAN THEORY AND DARBOUX’S METHOD

435

3(cx)/3(x) → 1 as x → +∞. (Examples of slowly varying functions are provided by powers of logarithms or iterated logarithms.) Theorem VI.13 (The HLK Tauberian theorem). Let f (z) be a power series with radius of convergence equal to 1, satisfying 1 1 (73) f (z) ∼ 3( ), (1 − z)α 1 − z for some α ≥ 0 with 3 a slowly varying function. Assume that the coefficients f n = [z n ] f (z) are all non-negative (this is the “side condition”). Then n X

(74)

k=0

nα 3(n). Ŵ(α + 1)

fk ∼

The conclusion (74) is consistent with the result given by singularity analysis: under the conditions, and if in addition analytic continuation is assumed, then n α−1 3(n), Ŵ(α) which by summation yields the estimate (74). It must be noted that a Tauberian theorem requires very little on the part of the function. However, it gives little, since it does not include error estimates. Also, the result it provides is valid in the more restrictive sense of mean values, or Ces`aro averages. (If further regularity conditions on the f n are available, for instance monotonicity, then the conclusion of (75) can then be deduced from (74) by purely elementary real analysis.) The method applies only to functions that are large enough at their singularity (the assumption α ≥ 0), and despite numerous efforts to improve the conclusions, it is the case that Tauberian theorems do not have much to offer in terms of error estimates. Appeal to a Tauberian theorem may be justified when a function has, apart from the positive half line, a very irregular behaviour near its circle of convergence, for instance when each point of the unit circle is a singularity. (The function is then said to admit the unit circle as a natural boundary.) An interesting example of this situation is discussed by Greene and Knuth [309] who consider the function ∞ Y zk , 1+ (76) f (z) = k fn ∼

(75)

k=1

which is the EGF of permutations having cycles all of different lengths. A little computation shows that ∞ k ∞ ∞ ∞ X Y z zk 1 X z 2k 1 X z 3k 1+ = − log + − ··· k k 2 3 k2 k3 k=1 k=1 k=1 k=1 1 − γ + o(1). ∼ log 1−z (Only the last line requires some care, see [309].) Thus, we have f (z) ∼

e−γ 1−z

−→

1 ( f 0 + f 1 + · · · + f n ) ∼ e−γ , n

436

VI. SINGULARITY ANALYSIS OF GENERATING FUNCTIONS

by virtue of Theorem VI.12. In fact, Greene and Knuth were able to supplement this argument by a “bootstrapping” technique and show a stronger result, namely f n → e−γ .

VI.31. Fine asymptotics of the Greene–Knuth problem. With f (z) as in (76), we have e−γ e−γ e−γ + + 2 (− log n − 1 − γ + log 2) n n i 1 1 h −γ 2 , + 3 e log n + c1 log n + c2 + 2(−1)n + (n) + O n n4 where c1 , c2 are computable constants and (n) has period 3. (The paper [227] derives a complete expansion based on a combination of Darboux’s method and singularity analysis.) [z n ] f (z)

=

Darboux’s method. The method of Darboux (also known as the Darboux–P´olya method) requires, as regularity condition, that functions be sufficiently differentiable (“smooth”) on their circle of convergence. What lies at the heart of the method is a simple relation between the smoothness of a function and the decrease of its Taylor coefficients. Theorem VI.14 (Darboux’s method). Assume that f (z) is continuous in the closed disc |z| ≤ 1 and is, in addition, k times continuously differentiable (k ≥ 0) on |z| = 1. Then 1 n (77) [z ] f (z) = o . nk Proof. Start from Cauchy’s coefficient formula Z 1 dz fn = f (z) n+1 . 2iπ C z Because of the continuity assumption, one may take as integration contour C the unit circle. Setting z = eiθ yields the Fourier version of Cauchy’s coefficient formula, Z 2π 1 (78) fn = f (eiθ )e−niθ dθ. 2π 0 The integrand in (78) is strongly oscillating. The Riemann–Lebesgue lemma of classical analysis [577, p. 403] shows that the integral tends to 0 as n → ∞. The argument above covers the case k = 0. For a general k, successive integrations by parts give Z 2π 1 [z n ] f (z) = f (k) (eiθ )e−niθ dθ, 2π(in)k 0 a quantity that is o(n k ), by Riemann–Lebesgue again.

Various consequences of Theorem VI.14 are given in reference texts also under the name of Darboux’s method. See for instance [129, 309, 329, 608]. We shall only illustrate the mechanism by rederiving in this framework the analysis of the EGF of 2–regular graphs (Example VI.2, p. 395). We have 2

(79)

√ e−3/4 e−z/2−z /4 =√ + e−3/4 1 − z + R(z). f (z) = √ 1−z 1−z

VI. 12. PERSPECTIVE

437

There R(z) is the product of (1 − z)3/2 with a function analytic at z = 1 that is 2 a remainder in the Taylor expansion of e−z/2−z /4 . Thus, R(z) is of class C1 , i.e., continuously differentiable once. By Theorem VI.14, we have 1 [z n ]R(z) = o , n so that 1 e−3/4 . +o (80) [z n ] f (z) = √ n πn Darboux’s method bears some resemblance to singularity analysis in that the estimates are derived from translating error terms in expansions. However, smoothness conditions, rather than plain order of growth information, are required by it. The method is often applied, in situations similar to (79)–(80), to functions that are products of the type h(z)(1−z)α with h(z) analytic at 1. In such particular cases, Darboux’s method is however subsumed by singularity analysis. It is inherent in Darboux’s method that it cannot be applied to functions whose singular expansion only involves terms that become infinite, while singularity analysis can. A clear example arises in the analysis of the common subexpression problem [257] where there occurs a function with a singular expansion of the form " # 1 1 c1 q 1+ + ··· . √ 1 1 − z log 1 log 1−z 1−z

VI.32. Darboux versus singularity analysis. This note provides an instance where Darboux’s method applies whereas singularity analysis does not. Let Fr (z) =

n ∞ X z2 . (2n )r

n=0

The function F0 (z) is singular at every point of the unit circle, and the same property holds for any Fr with r ∈ Z≥0 . [Hint: F0 , which satisfies the functional equation F(z) = z + F(z 2 ), grows unboundedly near 2n th roots of unity.] Darboux’s method can be used to derive 1 32 c 1 +o , c := F5 (z) = √ . [z n ] √ n 31 πn 1−z What is the best error term that can be obtained?

VI. 12. Perspective The method of singularity analysis expands our ability to extract coefficient asymptotics to a far wider class of functions than the meromorphic and rational functions of Chapters IV and V. This ability is the fundamental tool for analysing many of the generating functions provided by the symbolic method of Part A, and it is applicable at a considerable level of generality. The basic method is straightforward and appealing: we locate singularities, establish analyticity in a domain around them, expand the functions around the singularities, and apply general transfer theorems to take each term in the function expansion to a term in the asymptotic expansion of its coefficients. The method applies directly

438

VI. SINGULARITY ANALYSIS OF GENERATING FUNCTIONS

to a large variety of explicitly given functions, for instance combinations of rational functions, square roots, and logarithms, as well as to functions that are implicitly defined, like generating functions for tree structures, which are obtained by analytic inversion. Functions amenable to singularity analysis also enjoy rich closure properties, and the corresponding operations mirror the natural operations on generating functions implied by the combinatorial constructions of Chapters I–III. This approach again sets us in the direction of the ideal situation of having a theory where combinatorial constructions and analytic methods fully correspond, but, again, the very essence of analytic combinatorics is that the theorems that provide asymptotic results cannot be so general as to be free of analytic side conditions. In the case of singularity analysis, these side conditions have to do with establishing analyticity in a domain around singularities. Such conditions are automatically satisfied by a large number of functions with moderate (at most polynomial) growth near their dominant singularities, justifying precisely what we need: the term-by-term transfer from the expansion of a generating function at its singularity to an asymptotic form of coefficients, including error terms. The calculations involved in singularity analysis are rather mechanical. (Salvy [528] has indeed succeeded in automating the analysis of a large class of generating functions in this way.) Again, we can look carefully at specific combinatorial constructions and then apply singularity analysis to general abstract schemas, thereby solving whole classes of combinatorial problems at once. This process, along with several important examples, is the topic of Chapter VII, to come next. After that, we introduce, in Chapter VIII, the saddle-point method, which is appropriate for functions without singularities at a finite distance (entire functions) as well as those whose growth is rapid (exponential) near their singularities. Singularity analysis will surface again in Chapter IX, given its crucial technical rˆole in obtaining uniform expansions of multivariate generating functions near singularities. Bibliographic notes. Excellent surveys of asymptotic methods in enumeration have been given by Bender [36] and more recently Odlyzko [461]. A general reference to asymptotic analysis that has a remarkably concrete approach is De Bruijn’s book [143]. Comtet’s [129] and Wilf’s [608] books each devote a chapter to these questions. This chapter is largely based on the theory developed by Flajolet and Odlyzko in [248], where the term “singularity analysis” originates. An important early (and unduly neglected) reference is the study by Wong and Wyman [615]. The theory draws its inspiration from classical analytic number theory, for instance the prime number theorem where similar contours are used (see the discussion in [248] for sources). Another area where Hankel contours are used is the inversion theory of integral transforms [168], in particular in the case of algebraic and logarithmic singularities. Closure properties developed here are from the articles [208, 223] by Flajolet, Fill, and Kapur. Darboux’s method can often be employed as an alternative to singularity analysis. Although it is still a widely used technique in the literature, the direct mapping of asymptotic scales afforded by singularity analysis appears to us to be much more transparent. Darboux’s method is well explained in the books by Comtet [129], Henrici [329], Olver [465], and Wilf [608]. Tauberian theory is treated in detail in Postnikov’s monograph [494] and Korevaar’s encyclopaedic treatment [389], with an excellent introduction to be found in Titchmarsh’s book [577].

VII

Applications of Singularity Analysis Mathematics is being lazy. Mathematics is letting the principles do the work for you so that you do not have to do the work for yourself 1. ´ — G EORGE P OLYA I wish to God these calculations had been executed by steam. — C HARLES BABBAGE (1792–1871)

— The Bhagavad Gita XV.12 VII. 1. VII. 2. VII. 3. VII. 4. VII. 5. VII. 6. VII. 7. VII. 8. VII. 9. VII. 10. VII. 11.

A roadmap to singularity analysis asymptotics Sets and the exp–log schema Simple varieties of trees and inverse functions Tree-like structures and implicit functions Unlabelled non-plane trees and P´olya operators Irreducible context-free structures The general analysis of algebraic functions Combinatorial applications of algebraic functions Ordinary differential equations and systems Singularity analysis and probability distributions Perspective

441 445 452 467 475 482 493 506 518 532 538

Singularity analysis paves the way to the analysis of a large quantity of generating functions, as provided by the symbolic method expounded in Chapters I–III. In accordance with P´olya’s aphorism quoted above, it makes it possible to “be lazy” and “let the principles work for you”. In this chapter we illustrate this situation with numerous examples related to languages, permutations, trees, and graphs of various sorts. As in Chapter V, most analyses are organized into broad classes called schemas. First, we develop the general exp–log schema, which covers the set construction, either labelled or unlabelled, applied to generators whose dominant singularity is of logarithmic type. This typically non-recursive schema parallels in generality the supercritical schema of Chapter V, which is relative to sequences. It permits us to quantify various constructions of permutations, derangements, 2–regular graphs, mappings, and functional graphs, and provides information on factorization properties of polynomials over finite fields. 1Quoted in M Walter, T O’Brien, Memories of George P´olya, Mathematics Teaching 116 (1986) 2“There is an imperishable tree, it is said, that has its roots upward and its branches down and whose

leaves are the Hymns [Vedas]. He who knows it possesses knowledge.” 439

440

VII. APPLICATIONS OF SINGULARITY ANALYSIS

Next, we deal with recursively defined structures, whose study constitutes the main theme of this chapter. In that case, generating functions are accessible by means of equations or systems that implicitly define them. A distinctive feature of many such combinatorial types is that their generating functions have a square-root singularity, that is, the singular exponent equals 1/2. As a consequence, the counting sequences characteristically involve asymptotic terms of the form An n −3/2 , where the latter asymptotic exponent, −3/2, precisely reflects the singular exponent 1/2 in the function’s singular expansion, in accordance with the general principles of singularity analysis presented in Chapter VI. Trees are the prototypical recursively defined combinatorial type. Square-root singularities automatically arise for all varieties of trees constrained by a finite set of allowed node degrees, including binary trees, unary–binary trees, ternary trees, and many more. The counting estimates involve the characteristic n −3/2 subexponential factor, a property that holds in the labelled and unlabelled frameworks alike. Simple varieties of trees have many properties in common, beyond the subexponential growth factor of tree counts. Indeed, in √a random tree of some large size n, n, path length grows on average like almost all nodes are found to be at level about √ √ n n, and height is of order n, with high probability. These results serve to unify classical tree types—we say that such properties of random trees are universal3 among all simply generated families sharing the square-root singularity property. (This notion of universality, borrowed from physics, is also nowadays finding increasing popularity among probabilists, for reasons much similar to ours.) In this perspective, the motivation for organizing the theory along the lines of major schemas fits perfectly with the quest of universal laws in analytic combinatorics. In the context of simple varieties of trees, the square-root singularity arises from general properties of the inverse of an analytic function. Under suitable conditions, this characteristic feature can be extended to functions defined implicitly by a functional equation. Consequences are the general enumeration of non-plane unlabelled trees, including isomers of alkanes in theoretical chemistry, as well as secondary structures of molecular biology. Much of this chapter is devoted to context-free specifications and languages. In that case, a priori, generating functions are algebraic functions, meaning that they satisfy a system of polynomial equations, itself optionally reducible (by elimination) to a single equation. For solutions of positive polynomial systems, square-root singularities are found to be the rule under a simple technical condition of irreducibility that is evocative of the Perron–Frobenius conditions encountered in Chapter V in relation to finite-state and transfer-matrix models. As an illustration, we show how to develop a

3The following quotation illustrates well the notion of universality in physics: “[. . . ] this echoes the notion of universality in statistical physics. Phenomena that appear at first to be unconnected, such as magnetism and the phase changes of liquids and gases, share some identical features. This universal behaviour pays no heed to whether, say, the fluid is argon or carbon dioxide. All that matters are broad-brush characteristics such as whether the system is one-, two- or three-dimensional and whether its component elements interact via long- or short-range forces. Universality says that sometimes the details do not matter.” [From “Utopia Theory”, in Physics World, August 2003].

VII. 1. A ROADMAP TO SINGULARITY ANALYSIS ASYMPTOTICS

441

coherent theory of topological configurations in the plane (trees, forests, graphs) that satisfy a non-crossing constraint. For arbitrary algebraic functions (the ones that are not necessarily associated with positive coefficients and equations, or irreducible positive systems), a richer set of singular behaviours becomes possible: singular expansions involve fractional exponents (not just 1/2, corresponding to the square-root paradigm above). Singularity analysis is invariably applicable: algebraic functions are viewed as plane algebraic curves, and the famous Newton–Puiseux theorem of elementary algebraic geometry completely describes the types of singularities thay may occur. Algebraic functions also surface as solutions of various types of functional equations: this turns out to be the case for many classes of walks that generalize Dyck and Motzkin paths, via what is known as the kernel method, as well as for many types of planar maps (embedded planar graphs), via the so-called quadratic method. In all these cases, singular exponents of a predictable (rational) form are bound to occur, implying in turn numerous quantitative properties of random discrete structure and universality phenomena.. Differential equations and systems are associated to recursively defined structure, when either pointing constructions or order constraints appear. For counting generating functions, the equations are nonlinear, while the GFs associated to additive parameters lead to linear versions. Differential equations are also central in connection with the holonomic framework4 , which intervenes in the enumeration of many classes of “hard” objects, like regular graphs and Latin rectangles. Singularity analysis is once more instrumental in working out precise asymptotic estimates—the appearance of singular exponents that are algebraic (rather than rational) numbers is a characteristic feature of many such estimates. We examine here applications relative to quadtrees and to varieties of increasing trees, some of which are closely related to permutations as well as to algorithms and data structures for sorting and searching. VII. 1. A roadmap to singularity analysis asymptotics The singularity analysis theorems of Chapter VI, which may be coarsely summarized by the correspondence (1)

f (z) ∼ (1 − z/ρ)−α

−→

fn ∼

1 −n α−1 ρ n , Ŵ(α)

serve as our main asymptotic engine throughout this chapter. Singularity analysis is instrumental in quantifying properties of non-recursive as well as recursive structures. Our reader might be surprised not to encounter integration contours anymore in this chapter. Indeed, it now suffices to work out the local analysis of functions at their singularities, then the general theorems of singularity analysis (Chapter VI) effect the translation to counting sequences and parameters automatically.

4Holonomic functions (Appendix B.4: Holonomic functions, p. 748) are defined as solutions of linear

differential equations with coefficients that are rational functions.

442

VII. APPLICATIONS OF SINGULARITY ANALYSIS

The exp–log schema. This schema, examined in Section VII. 2, is relative to the labelled set construction, (2)

F = S ET(G)

H⇒

F(z) = exp (G(z)) ,

as well as its unlabelled counterparts, MS ET and PS ET: an F–structure is thus constructed (non-recursively) as an unordered assembly of G–components. In the case where the GF of components is logarithmic at its dominant singularity, 1 (3) G(z) ∼ κ log + λ, 1 − z/ρ an immediate computation shows that F(z) has a singularity of the power type, F(z) ∼ eλ (1 − z/ρ)−κ , which is clearly in the range of singularity analysis. The construction (2), supplemented by simple technical conditions surrounding (3), defines the exp–log schema. Then, for such F–structures that are assemblies of logarithmic components, the asymptotic counting problem is systematically solvable (Theorem VII.1, p. 446): the number of G–components in a large random F–structure is O(log n), both in the mean and in probability, while more refined estimates describe precisely the likely shape of profiles. This schema has a generality comparable to the supercritical schema examined in Section V. 2, p. 293, but the probabilistic phenomena at stake appear to be in sharp contrast: the number of components is typically small, being logarithmic for exp–log sets, as opposed to a linear growth in the case of supercritical sequences. The schema can be used to analyse properties of permutations, functional graphs, mappings, and polynomial over finite fields. Recursion and the universality of square-root singularity. A major theme of this chapter is the study of asymptotic properties of recursive structures. In a large number of cases, functions with a square root singularity are encountered, and given the usual correspondence, 1 ; f (z) ∼ −(1 − z)1/2 −→ fn ∼ √ 2 π n3 the corresponding coefficients are of the asymptotic form Cρ −n n −3/2 . Several schemas can be described to capture this phenomenon; we develop here, in order of increasing structural complexity, the ones corresponding to simple varieties of trees, implicit structures, P´olya operators, and irreducible polynomial systems. Simple varieties of trees and inverse functions. Our treatment of recursive combinatorial types starts with simple varieties of trees, studied in Section VII. 3. In the basic situation, that of plane unlabelled trees, the equation is Y = Z × S EQ (Y) H⇒ Y (z) = zφ(Y (z)), P ω with, as usual, φ(w) = ω∈ w . Thus, the OGF Y (z) is determined as the inverse of w/φ(w), where the function φ reflects the collection of all allowed node degrees (). From analytic function theory, we know that singularities of the inverse of an analytic function are generically of the square-root type (Subsection IV. 7.1, p. 275 and Section VI. 7, p. 402), and such is the case whenever is a “well-behaved” set (4)

VII. 1. A ROADMAP TO SINGULARITY ANALYSIS ASYMPTOTICS

443

of integers, in particular, a finite set. Then, the number of trees invariably satisfies an estimate of the form (5)

Yn = [z n ]Y (z) ∼ C An n −3/2 .

Square-root singularity is also attached to several universality phenomena, as evoked in the general introduction to this chapter. Tree-like structures and implicit functions. Functions defined implicitly by an equation of the form (6)

Y (z) = G(z, Y (z))

where G is bivariate analytic, has non-negative coefficients, and satisfies a natural set of conditions also lead to square-root singularity (Section VII. 4 and Theorem VII.3, p. 468)). The schema (6) obviously generalizes (4): simply take G(z, y) = zφ(y). Again, such functions invariably satisfy an estimate (5). Trees under symmetries and P´olya operators. The analytic methods mentioned above can be further extended to P´olya operators, which translate unlabelled set and cycle constructions; see Section VII. 5. A typical application is to the class of nonplane unlabelled trees whose OGF satisfies the infinite functional equation, ! H (z) H (z 2 ) H (z) = z exp + + ··· . 1 2 Singularity analysis applies more generally to varieties of non-plane unlabelled trees (Theorem VII.4, p. 479), which covers the enumeration of various types of interesting molecules in combinatorial chemistry. Context-free structures and polynomial systems. The generating function of any context-free class or language is known to be a component of a system of positive polynomial equations y1 = P1 (z, y1 , . . . , yr ) .. .. .. . . . yr = Pr (z, y1 , . . . , yr ).

The n −3/2 counting law is once more universal among such combinatorial classes under a basic condition of “irreducibility” (Section VII. 6 and Theorem VII.5, p. 483). In that case, the GFs are algebraic functions satisfying a strong positivity constraint; the corresponding analytic statement constitutes the important Drmota–Lalley–Woods Theorem (Theorem VII.6, p. 489). Note that there is a progression in the complexity of the schemas leading to square-root singularity. From the analytic standpoint, this can be roughly rendered by a chain inverse functions −→ implicit functions −→ systems.

It is, however, often meaningful to treat each combinatorial problem at its minimal level of generality, since expressions tend to become less and less explicit as complexity increases.

444

VII. APPLICATIONS OF SINGULARITY ANALYSIS

General algebraic functions. In essence, the coefficients of all algebraic functions can be analysed asymptotically (Section VII. 7). There are only minor limitations arising from the possible presence of several dominant singularities, like in the rational function case. The starting point is the characterization of the local behaviour of an algebraic function at any of its singularities, which is provided by the Newton– Puiseux theorem: if ζ is a singularity, then the branch Y (z) of an algebraic function admits near ζ a representation of the form X (7) Y (z) = Z r/s Z := (1 − z/ζ ), ck Z k/s , k≥0

for some r/s ∈ Q, so that the singular exponent is invariably a rational number. Singularity analysis is systematically applicable, so that the nth coefficient of Y is expressible as a finite linear combination of terms, each of the asymptotic form p ∈ Q \ {−1, −2, . . .}; (8) ζ −n n p/q , q see also Figure VII.1. The various quantities (like ζ, r, s) entering the asymptotic expansion of the coefficients of an algebraic function turn out to be effectively computable. Beside providing a wide-encompassing conceptual framework of independent interest, the general theory of algebraic coefficient asymptotics is applicable whenever the combinatorial problems considered are not amenable to any of the special schemas previously described. For instance, certain kinds of supertrees (these are defined as trees composed with trees, Example VII.10, p. 412) lead to the singular type Z 1/4 , which is reflected by an unusual subexponential factor of n −5/4 present in asymptotic counts. Maps, which are planar graphs drawn in the plane (or on the sphere), satisfy a universality law with a singular exponent equal to 3/2, which is associated to counting sequences involving an asymptotic n −5/2 factor. Differential equations and systems. When recursion is combined with pointing or with order constraints, enumeration problems translate into integro-differential equations. Section VII. 9 examines the types of singularities that may occur in two important cases: (i) linear differential equations; (ii) nonlinear differential equations. Linear differential equations arise from the analysis of parameters of splitting processes that extend the framework of tree recurrences (Subsection VI. 10.3, p. 427), and we treat the geometric quadtree structure in this perspective. An especially notable source of linear differential equations is the class of holonomic functions (solutions of linear equations with rational coefficients, cf Appendix B.4: Holonomic functions, p. 748), which includes GFs of Latin rectangles, regular graphs, permutations constrained by the length of their longest increasing subsequence, Young tableaux and many more structures of combinatorial theory. In an important case, that of a “regular” singularity, asymptotic forms can be systematically extracted. The singularities that may occur extend the algebraic ones (7), and the corresponding coefficients are then asymptotically composed of elements of the form (9)

ζ −n n θ (log n)ℓ ,

VII. 2. SETS AND THE EXP–LOG SCHEMA

Rational

Irred. linear system

ζ −n

—

General rational

ζ −n n ℓ

Algebraic

Irred. positive sys.

ζ −n n −3/2

—

General algebraic

ζ −n n p/q

Holonomic

Regular sing.

ζ −n n θ logℓ n

—

Irregular sing.

ζ −n e P(n

445

Perron–Frob., merom. fns, Ch. V meromorphic functions, Ch. V

1/r )

DLW Th., sing. analysis, this chapter, §VII. 6, p. 482 Puiseux, sing. analysis, this chapter, §VII. 7, p. 493

n θ logℓ n

ODE, sing. analysis, this chapter, §VII. 9.1, p. 518 ODE, saddle-point, §VIII. 7, p. 581

Figure VII.1. A telegraphic summary of a hierarchy of special functions by increasing level of generality: asymptotic elements composing coefficients and the coefficient extraction method (with ℓ, r ∈ Z≥0 , p/q ∈ Q, ζ and θ algebraic, and P a polynomial).

(θ an algebraic quantity, ℓ ∈ Z≥0 ), a type which is much more general than (8). Nonlinear differential equations are typically attached to the enumeration of trees satisfying various kinds of order constraints. A global treatment is intrinsically not possible, given the extreme diversity of singular expansions that may occur. Accordingly, we restrict attention to first-order nonlinear equations of the form d Y (z) = φ(Y (z)), dz which covers varieties of increasing trees and certain urn processes, including several models closely related to permutations. Figure VII.1 summarizes three classes of special functions encountered in this book, namely, rational, algebraic, and holonomic. When structural complexity increases, a richer set of asymptotic coefficient behaviours becomes possible. (The complex asymptotic methods employed extend much beyond the range summarized in the figure. For instance, the class of irreducible positive systems of polynomial equations are part of the general square-root singularity paradigm, also encountered with P´olya operators, as well as inverse and implicit functions in non-algebraic cases.) VII. 2. Sets and the exp–log schema We begin by examining a schema that is structurally comparable to the supercritical sequence schema of Section V. 2, p. 293, but one that requires singularity analysis for coefficient extraction. The starting point is the construction of permutations (P) as labelled sets of cyclic permutations (K): (10)

P = S ET(K)

H⇒

P(z) = exp (K (z)) , where K (z) = log

1 , 1−z

446

VII. APPLICATIONS OF SINGULARITY ANALYSIS

which gives rise to many easy explicit calculations. For instance, the probability that a random permutation consists of a unique cycle is 1/n (since it equals K n /Pn ); the number of cycles is asymptotic to log n, both on average (p. 122) and in probability (Example III.4, p. 160); the probability that a random permutation has no singleton cycle is ∼ e−1 (the derangement problem; see pp. 123 and 228). Similar properties hold true under surprisingly general conditions. We start with definitions that describe the combinatorial classes of interest. Definition VII.1. A function G(z) analytic at 0, having non-negative coefficients and finite radius of convergence ρ is said to be of (κ, λ)-logarithmic type, where κ 6= 0, if the following conditions hold: (i) the number ρ is the unique singularity of G(z) on |z| = ρ; (ii) G(z) is continuable to a 1–domain at ρ; (iii) G(z) satisfies 1 1 +λ+O , as z → ρ in 1. (11) G(z) = κ log 1 − z/ρ (log(1 − z/ρ))2 Definition VII.2. The labelled construction F = S ET(G) is said to be a labelled exp–log schema (“exponential–logarithmic schema”) if the exponential generating function G(z) of G is of logarithmic type. The unlabelled construction F = MS ET(G) is said to be an unlabelled exp–log schema if the ordinary generating function G(z) of G is of logarithmic type, with ρ < 1. In each case, the quantities (κ, λ) of (11) are referred to as the parameters of the schema. By the fact that G(z) has positive coefficients, we must have κ > 0, while the sign of λ is arbitrary. The definitions and the main properties to be derived for unlabelled multisets easily extend to the powerset construction: see Notes VII.1 and VII.5 below. Theorem VII.1 (Exp–log schema). Consider an exp–log schema with parameters (κ, λ). (i) The counting sequences satisfy κ −n 1 + O (log n)−2 , ρ [z n ]G(z) = n eλ+r0 κ−1 −n n [z ]F(z) = 1 + O (log n)−2 , n ρ Ŵ(κ) P where r0 = 0 in the labelled case and r0 = j≥2 G(ρ j )/j in the case of unlabelled multisets. (ii) The number X of G–components in a random F–object satisfies d (ψ(s) ≡ ds EFn (X ) = κ(log n − ψ(κ)) + λ + r1 + O (log n)−1 Ŵ(s)), P j where r1 = 0 in the labelled case and r1 = j≥2 G(ρ ) in the case of unlabelled multisets. The variance satisfies VFn (X ) = O(log n), and, in particular, the distribution5 of X is concentrated around its mean. 5 We shall see in Subsection IX. 7.1 (p. 667) that, in addition, the asymptotic distribution of X is

invariably Gaussian under such exp–log conditions.

VII. 2. SETS AND THE EXP–LOG SCHEMA

447

Proof. This result is from an article by Flajolet and Soria [258], with a correction to the logarithmic type condition given by Jennie Hansen [318]. We first discuss the labelled case, F = S ET(G), so that F(z) = exp G(z). (i) The estimate for [z n ]G(z) follows directly from singularity analysis with logarithmic terms (Theorem VI.4, p. 393). Regarding F(z), we find, by exponentiation, eλ 1 . (12) F(z) = 1 + O (1 − z/ρ)κ (log(1 − z/ρ))2

Like G, the function F = e G has an isolated singularity at ρ, and is continuable to the 1–domain in which the expansion (11) is valid. The basic transfer theorem then provides the estimate of [z n ]F(z). (ii) Regarding the number of components, the BGF of F with u marking the number of G–components is F(z, u) = exp(uG(z)), in accordance with the general developments of Chapter III. The function ∂ = F(z)G(z), f 1 (z) := F(z, u) ∂u u=1

is the EGF of the cumulated values of X . It satisfies near ρ eλ 1 1 f 1 (z) = κ log + λ 1 + O , (1 − z/ρ)κ 1 − z/ρ (log(1 − z/ρ))2 whose translation, by singularity analysis theory is immediate: eλ −n κ log n − κψ(κ) + λ + O (log n)−1 . [z n ] f 1 (z) ≡ EFn (X ) = ρ Ŵ(κ) This provides the mean value estimate of X as [z n ] f 1 (z)/[z n ]F(z). The variance analysis is conducted in the same way, using a second derivative. For the unlabelled case, the analysis of [z n ]G(z) can be recycled verbatim. First, given the assumptions, we must have ρ < 1 (since otherwise [z n ]G(z) could not be an integer). The classical translation of multisets (Chapter I) rewrites as F(z) = exp (G(z) + R(z)) ,

R(z) :=

∞ X G(z j ) , j j=2

G(z 2 ), . . .,

where R(z) involves terms of the form each being analytic in |z| < ρ 1/2 . Thus, R(z) is itself analytic, as a uniformly convergent sum of analytic functions, in |z| < ρ 1/2 . (This follows the usual strategy for treating P´olya operators in asymptotic theory.) Consequently, F(z) is 1–analytic. As z → ρ, we then find ∞ X 1 eλ+r0 G(ρ j ) 1 + O . (13) F(z) = , r ≡ 0 κ 2 (1 − z/ρ) j (log(1 − z/ρ)) j=2

[z n ]F(z)

The asymptotic expansion of then results from singularity analysis. The BGF F(z, u) of F, with u marking the number of G–components, is ! uG(z) u 2 G(z 2 ) F(z, u) = exp + + ··· . 1 2

448

VII. APPLICATIONS OF SINGULARITY ANALYSIS

F

κ

n = 100

n = 272

n = 739

Permutations

1

5.18737

6.18485

7.18319

Derangements

1

4.19732

5.18852

6.18454

2–regular

1 2 1 2

2.53439

3.03466

3.53440

2.97898

3.46320

3.95312

Mappings

Figure VII.2. Some exp–log structures (F ) and the mean number of G–components for n = 100, 272 ≡ ⌈100 · e⌋, 739 ≡ ⌈100 · e2 ⌋: the columns differ by about κ, as expected.

Consequently, ∂ = F(z) (G(z) + R1 (z)) , F(z, u) f 1 (z) := ∂u u=1

R1 (z) =

∞ X

G(z j ).

j=2

Again, the singularity type is that of F(z) multiplied by a logarithmic term, (14)

f 1 (z) ∼ F(z)(G(z) + r1 ), z→ρ

r1 ≡

∞ X

G(ρ j ).

j=2

The mean value estimate results. Variance analysis follows similarly.

VII.1. Unlabelled powersets. For the powerset construction F = PS ET(G), the statement of Theorem VII.1 holds with

r0 =

X

(−1) j−1

j≥2

G(ρ j ) , j

as seen by an easy adaptation of the proof technique of Theorem VII.1.

As we see below, beyond permutations, mappings, unlabelled functional graphs, polynomials over finite fields, 2–regular graphs, and generalized derangements belong to the exp–log schema; see Figure VII.2 for representative numerical data. Furthermore, singularity analysis gives precise information on the decomposition of large F objects into G components. Example VII.1. Cycles in derangements. The case of all permutations, 1 , 1−z is immediately seen to satisfy the conditions of Theorem VII.1: it corresponds to the radius of convergence ρ = 1 and parameters (κ, λ) = (1, 0). Let be a finite set of integers and consider next the class D ≡ D of permutations without any cycle of length in . This includes standard derangements (where = {1}). The specification is then D(z) = exp(K (z)) D = S ET(K) X zω 1 H⇒ G(z) = log − . G = C YCZ>0 \ (Z) 1−z ω P(z) = exp(K (z)),

K (z) = log

ω∈

VII. 2. SETS AND THE EXP–LOG SCHEMA

449

P The theorem applies, with κ = 1, λ := − ω∈ ω−1 . In particular, the mean number of cycles in a random generalized derangement of size n is log n + O(1). . . . . . . . . . . . . . . . . . . . . . . . . . Example VII.2. Connected components in 2–regular graphs. The class of (undirected) 2– regular graphs is obtained by the set construction applied to components that are themselves undirected cycles of length ≥ 3 (see p. 133 and Example VI.2, p. 395). In that case: F = S ET(G) F(z) = exp(G(z)) H⇒ 1 1 z z2 G = UC YC (Z) G(z) = log − − . ≥3 2 1−z 2 4

This is an exp–log scheme with κ = 1/2 and λ = −3/4. In particular the number of components is asymptotic to 12 log n, both in the mean and in probability. . . . . . . . . . . . . . . . . . . . . . .

Example VII.3. Connected components in mappings. The class F of mappings (functions from a finite set to itself) was introduced in Subsection II. 5.2, p. 129. The associated digraphs are described as labelled sets of connected components (K), themselves (directed) cycles of trees (T ), so that the class of all mappings has an EGF given by F(z) = exp(K (z)),

K (z) = log

1 , 1 − T (z)

T (z) = ze T (z) ,

with T the Cayley tree function. The analysis of inverse functions (Section VI. 7 and Exam−1 ple VI.8, p. 403) has shown √ √ that T (z) is singular at z = e , where it admits the singular √ expansion T (z) ∼ 1 − 2 1 − ez. Thus G(z) is logarithmic with κ = 1/2 and λ = − log 2. As a consequence, the number of connected mappings satisfies r π 1 + O(n −1/2 ) . K n ≡ n![z n ]K (z) = n n 2n

In other q words: the probability for a random mapping of size n to consist of a single component π . Also, the mean number of components in a random mapping of size n is is ∼ 2n

√ 1 log n + log 2eγ + O(n −1/2 ). 2 Similar properties hold for mappings without fixed points, which are analogous to derangements and were discussed in Chapter II, p. 130. We shall establish below, p. 480, that unlabelled functional graphs also belong to the exp–log schema. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Example VII.4. Factors of polynomials over finite fields. Factorization properties of random polynomials over finite fields are of importance in various areas of mathematics and have applications to coding theory, symbolic computation, and cryptography [51, 599, 541]. Example I.20, p. 90, offers a preliminary discussion. Let F p be the finite field with p elements and P ⊂ F p [X ] the set of monic polynomials with coefficients in the field. We view these polynomials as (unlabelled) combinatorial objects with size identified to degree. Since a polynomial is specified by the sequence of its coefficients, one has, with A the “alphabet” of coefficients, A = F p treated as a collection of atomic objects: (15)

P = S EQ(A)

H⇒

P(z) =

1 , 1 − pz

On the other hand, the unique factorization property of polynomials entails that the class I of all monic irreducible polynomials and the class P of all polynomials are related by P = MS ET(I).

450

VII. APPLICATIONS OF SINGULARITY ANALYSIS (X + 1) X 10 + X 9 + X 8 + X 6 + X 4 + X 3 + 1 X 14 + X 11 + X 10 + X 3 + 1 2 X 3 (X + 1) X 2 + X + 1 X 17 + X 16 + X 15 + X 11 + X 9 + X 6 + X 2 + X + 1 5 5 3 2 12 8 7 6 5 3 2 X (X + 1) X + X + X + X + 1 X + X + X + X + X + X + X + X + 1 X 2 + X + 1 2 X2 X2 + X + 1 X3 + X2 + 1 X8 + X7 + X6 + X4 + X2 + X + 1 X8 + X7 + X5 + X4 + 1 X 7 + X 6 + X 5 + X 3 + X 2 + X + 1 X 18 + X 17 + X 13 + X 9 + X 8 + X 7 + X 6 + X 4 + 1

Figure VII.3. The factorizations of five random polynomials of degree 25 over F2 . One out of five polynomials in this sample has no root in the base field (the asymptotic probability is 14 by Note VII.4).

As a consequence of M¨obius inversion, one then gets (Equation (94) of Chapter I, p. 91): X µ(k) 1 1 + R(z), R(z) := log . (16) I (z) = log 1−z k 1 − pz k k≥2

Regarding complex asymptotics, the function R(z) of (16) is analytic in |z| < p−1/2 . Thus I (z) is of logarithmic type with radius of convergence 1/ p and parameters X µ(k) 1 κ = 1, λ= log . k 1 − p1−k k≥2

As already noted in Chapter I, a consequence is the asymptotic estimate In ∼ p n /n, which constitutes a “Prime Number Theorem” for polynomials over finite fields: a fraction asymptotic to 1/n of the polynomials in F p [X ] are irreducible. Furthermore, since I (z) is logarithmic and P is obtained by a multiset construction, we have an unlabelled exp–log scheme, to which Theorem VII.1 applies. As a consequence: The number of factors of a random polynomial of degree n has mean and variance each asymptotic to log n; its distribution is concentrated. (See Figure VII.3 for an illustration; the mean value estimate appears in [378, Ex. 4.6.2.5].) We shall revisit this example in Chapter IX, p. 672, and establish a companion Gaussian limit law for the number of irreducible factors in a random polynomial of large degree. This and similar developments lead to a complete analysis of some of the basic algorithms known for factoring polynomials over finite fields; see [236]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

VII.2. The divisor function for polynomials. Let δ(̟ ) for ̟ ∈ P be the total number of e

e

monic polynomials (not necessarily irreducible) dividing ̟ : if ̟ = ι11 · · · ιkk , where the ι j are distinct irreducibles, then δ(̟ ) = (e1 + 1) · · · (ek + 1). One has Q [z n ] j≥1 (1 + 2z j + 3z 2 j + · · · ) [z n ]P(z)2 = n EPn (δ) = , Q n j 2 j [z ]P(z) [z ] j≥1 (1 + z + z + · · · )

so that the mean value of δ over Pn is exactly (n + 1). This evaluation is relevant to polynomial factorization over Z since it gives an upper bound on the number of irreducible factor combinations that need to be considered in order to lift a factorization from F p (X ) to Z(X ); see [379, 599].

VII.3. The cost of finding irreducible polynomials. Assume that it takes expected time t (n) to test a random polynomial of degree n for irreducibility. Then it takes expected time ∼ nt (n) to find a random irreducible polynomial of degree n: simply draw a polynomial at random and test it for irreducibility. (Testing for irreducibility can itself be achieved by developing a polynomial

VII. 2. SETS AND THE EXP–LOG SCHEMA

451

factorization algorithm which is stopped as soon as a non-trivial factor is found. See works by Panario et al. for detailed analyses of this strategy [468, 469].)

Profiles of exp–log structures. Under the exp–log conditions, it is also possible to analyse the profile of structures, that is, the number of components of size r for each fixed r . The Poisson distribution (Appendix C.4: Special distributions, p. 774) of parameter ν is the law of a discrete random variable Y such that νk . k! A variable Y is said to be negative binomial of parameter (m, α) if its probability generating function and its individual probabilities satisfy: m+k−1 k 1−α m α (1 − α)m . , P(Y = k) = E(u Y ) = k 1 − αu (The quantity P(Y = k) is the probability that the mth success in a sequence of independent trials with individual success probability α occurs at time m + k; see [206, p. 165] and Appendix C.4: Special distributions, p. 774.) Proposition VII.1 (Profiles of exp–log structures). Assume the conditions of Theorem VII.1 and let X (r ) be the number of G–components of size r in an F–object. In the labelled case, X (r ) admits a limit distribution of the Poisson type: for any fixed k, E(u Y ) = e−ν(1−u) ,

(17)

P(Y = k) = e−ν

νk , ν = gr ρ r , gr ≡ [z r ]G(z). k! admits a limit distribution of the negative-binomial type:

lim PFn (X (r ) = k) = e−ν

n→∞

In the unlabelled case, X (r ) for any fixed k, (18) Gr + k − 1 k (r ) α (1 − α)G r , lim PFn (X = k) = n→∞ k

α = ρ r , G r ≡ [z r ]G(z).

Proof. In the labelled case, the BGF of F with u marking the number X (r ) of r – components is F(z, u) = exp (u − 1)gr z r F(z).

Extracting the coefficient of u k leads to

φk (z) := [u k ]F(z, u) = exp −gr z r

(gr z r )k

F(z). k! The singularity type of φk (z) is that of F(z) since the prefactor (an exponential multiplied by a polynomial) is entire, so that singularity analysis applies directly. As a consequence, one finds (gr ρ r )k

· [z n ]F(z) , k! which provides the distribution of X (r ) under the form stated in (17). In the unlabelled case, the starting BGF equation is 1 − zr Gr F(z, u) = F(z), 1 − uz r [z n ]φk (z) ∼ exp −gr ρ r

452

VII. APPLICATIONS OF SINGULARITY ANALYSIS

and the analytic reasoning is similar to the labelled case.

Proposition VII.1 will be revisited in Example IX.23, p. 675, when we examine continuity theorems for probability generating functions. Its unlabelled version covers in particular polynomials over finite fields; see [236, 372] for related results.

VII.4. Mean profiles. The mean value of X (r ) satisfies EFn (X (r ) ) ∼ gr ρ r ,

EFn (X (r ) ) ∼ G r

ρr , 1 − ρr

in the labelled and unlabelled (multiset) case, respectively. In particular: the mean number of p roots of a random polynomial over F p that lie in the base field F p is asymptotic to p−1 . Also: the probability that a polynomial has no root in the base field is asymptotic to (1 − 1/ p) p . (For random polynomials with real coefficients, a famous result of Kac (1943) asserts that the mean number of real roots is ∼ π2 log n; see [185].)

VII.5. Profiles of powersets. In the case of unlabelled powersets F = PS ET(G) (no repetitions of elements allowed), the distribution of X (r ) satisfies Gr k α (1 − α)G r −k , lim PFn (X (r ) = k) = n→∞ k

i.e., the limit is a binomial law of parameters (G r , ρ r /(1 + ρ r )).

α=

ρr ; 1 + ρr

VII. 3. Simple varieties of trees and inverse functions A unifying theme in this chapter is the enumeration of rooted trees determined by restrictions on the collection of allowed node degrees (Sections I. 5, p. 64 and II. 5, p. 125). Some set ⊆ Z≥0 containing 0 (for leaves) and at least another number d ≥ 2 (to avoid trivialities) is fixed; in the trees considered, all outdegrees of nodes are constrained to lie in . Corresponding to the four combinations, unlabelled/labelled and plane/non-plane, there are four types of functional equations summarized by Figure VII.4. In three of the four cases, namely, unlabelled plane, labelled plane, and labelled non-plane, the generating function (OGF for unlabelled, EGF for labelled) satisfies an equation of the form (19)

y(z) = zφ(y(z)).

In accordance with earlier conventions (p. 194), we name simple variety of trees any family of trees whose GF satisfies an equation of the form (19). (The functional equation satisfied by the OGF of a degree-restricted variety of unlabelled non-plane trees furthermore involves a P´olya operator 8, which implies the presence of terms of the form y(z 2 ), y(z 3 ), . . .: such cases are discussed below in Section VII. 5.) The relation y = zφ(y) has already been examined in Section VI. 7, p. 402, from the point of view of singularity analysis. For convenience, we encapsulate into a definition the conditions of the main theorem of that section, Theorem VI.6, p. 404.

VII. 3. SIMPLE VARIETIES OF TREES AND INVERSE FUNCTIONS

Unlabelled (OGF)

plane

non-plane

V = Z × S EQ (V)

V = Z × MS ET (V)

V (z) = zφ(V (z)) P φ(u) := ω∈ u ω

V (z) = z8(V (z))) (8 a P´olya operator)

b(z) = zφ(V b(z)) V P φ(u) := ω∈ u ω

b(z) = zφ(V b(z)) V P ω φ(u) := ω∈ uω!

V = Z ⋆ S EQ (V)

Labelled (EGF)

453

V = Z ⋆ S ET (V)

Figure VII.4. Functional equations satisfied by generating functions (OGF V (z) or b(z)) of degree-restricted families of trees. EGF V

Definition VII.3. Let y(z) be a function analytic at 0. It is said to belong to the smooth inverse-function schema if there exists a function φ(u) analytic at 0, such that, in a neighbourhood of 0, one has y(z) = zφ(y(z)), and φ(u) satisfies the following conditions. (H1 ) The function φ(u) is such that (20)

φ(0) 6= 0,

[u n ]φ(u) ≥ 0,

φ(u) 6≡ φ0 + φ1 u.

(H2 ) Within the open disc of convergence of φ at 0, |z| < R, there exists a (necessarily unique) positive solution to the characteristic equation: (21)

∃τ, 0 < τ < R,

φ(τ ) − τ φ ′ (τ ) = 0.

A class Y whose generating function y(z) (either ordinary or exponential) satisfies these conditions is also said to belong to the smooth inverse-function schema. The schema is said to be aperiodic if φ(u) is an aperiodic function of u (Definition IV.5, p. 266). VII. 3.1. Asymptotic counting. As we saw on general grounds in Chapters IV and VI, inversion fails to be analytic when the first derivative of the function to be inverted vanishes. The heart of the matter is that, at the point of failure y = τ , corresponding to z = τ/φ(τ ) (the radius of convergence of y(z) at 0), the dependency y 7→ z becomes quadratic, so that its inverse z 7→ y gives rise to a square-root singularity (hence the characteristic equation). From here, the typical n −3/2 term in coefficient asymptotics results (Theorem VI.6, p. 404). In view of our needs in this chapter, we rephrase Theorem VI.6 as follows. Theorem VII.2. Let y(z) belong to the smooth inverse-function schema in the aperiodic case. Then, with τ the positive root of the characteristic equation and ρ =

454

VII. APPLICATIONS OF SINGULARITY ANALYSIS

τ/φ(τ ), one has n

[z ]y(z) =

s

φ(τ ) ρ −n 1 . 1+O √ ′′ 3 2φ (τ ) π n n

As we also know from Theorem√VI.6 (p. 404), a complete (and locally convergent) expansion of y(z) in powers of 1 − z/ρ exists, starting with s p 2φ(τ ) γ := (22) y(z) = τ − γ 1 − z/ρ + O (1 − z/ρ) , , φ ′′ (τ )

n which √ implies a complete asymptotic expansion for yn = [z ]y(z) in odd powers of 1/ n. (The statement extends to the aperiodic case, with the necessary condition that n ≡ 1 mod p, when φ has period p.) We have seen already that this framework covers binary, unary–binary, general Catalan, as well as Cayley trees (Figure VI.10, p. 406). Here is another typical application.

Example VII.5. Mobiles. A (labelled) mobile, as defined by Bergeron, Labelle, and Leroux [50, p. 240], is a (labelled) tree in which subtrees dangling from the root are taken up to cyclic shift:

1

2

3! + 3 = 9

4! + 4 × 2 + 4 × 3 + 4 × 3 × 2 = 68

(Think of Alexander Calder’s creations.) The specification and EGF equation are 1 . M = Z ⋆ (1 + C YC M) H⇒ M(z) = z 1 + log 1 − M(z) (By definition, cycles have at least one components, so that the neutral structure must be added 2 3 4 5 to allow for leaf creation.) The EGF starts as M(z) = z + 2 z2! + 9 z3! + 68 z4! + 730 z5! + · · · , whose coefficients constitute EIS A038037. The verification of the conditions of the theorem are immediate. We have φ(u) = 1 + log(1 − u)−1 , whose radius of convergence is 1. The characteristic equation reads 1 + log

τ 1 − = 0, 1−τ 1−τ

. which has a unique positive root at τ = 0.68215. (In fact, one has τ = 1 − 1/T (e−2 ), with T the Cayley tree function.) The radius of convergence is ρ ≡ 1/φ ′ (τ ) = 1 − τ . The asymptotic formula for the number of mobiles then results: 1 Mn ∼ C · An n −3/2 , n!

. where C = 0.18576,

. A = 3.14461.

(This example is adapted from [50, p. 261], with corrections.) . . . . . . . . . . . . . . . . . . . . . . . . . . .

VII. 3. SIMPLE VARIETIES OF TREES AND INVERSE FUNCTIONS

455

VII.6. Trees with node degrees that are prime numbers. Let P be the class of all unlabelled plane trees such that the (out)degrees of internal nodes belong to the set of prime numbers, {2, 3, 5, . . .}. One has P(z) = z + z 3 + z 4 + 2 z 5 + 6 z 6 + 8 z 7 + 29 z 8 + 50 z 9 + · · · , and . Pn ∼ C An n −3/2 , with A = 2.79256 84676. The asymptotic form “forgets” many details of the distribution of primes, so that it can be obtained to great accuracy. (Compare with Example V.2, p. 297 and Note VII.24, p. 480.) VII. 3.2. Basic tree parameters. Throughout this subsection, we consider a simple variety of trees V, whose generating function (OGF or EGF, as the case may be) will be denoted by y(z), satisfying the inverse relation y = zφ(y). In order to place all cases under a single umbrella, we shall write yn = [z n ]y(z), so that the number of trees of size n is either Vn = yn (unlabelled case) or Vn = n!yn (labelled case). We postulate throughout that y(z) belongs to the smooth inverse-function schema and is aperiodic. As already seen on several occasions in Chapter III (Section III. 5, p. 181), additive parameters lead to generating functions that are expressible in terms of the basic tree generating function y(z). Now that singularity analysis is available, such generating functions can be exploited systematically, with a wealth of asymptotic estimates relative to trees of large sizes coming within easy reach. The universality of the square-root singularity among varieties of trees that satisfy the smoothness assumption of Definition VII.3 then implies universal behaviour for many tree parameters, which we now list. (i) Node degrees. The degree of the root in a large random tree is O(1) on average and with high probability, and its asymptotic distribution can be generally determined (Example VII.6). A similar property holds for the degree of a random node in a random tree (Example VII.8). (ii) Level profiles can also be determined. The quantity of interest is the mean number of nodes in the kth layer from the root in a random tree. It is seen for instance that, near the root, a tree from a simple variety tends to grow linearly (Example VII.7), this in sharp contrast with other random tree models (for instance, increasing trees, Subsection VII. 9.2, p. 526), where the growth is exponential. This property is one of the numerous indications that random trees taken from simple varieties are skinny and far from having a well-balanced√shape. A related property is the fact that path length is on average O(n n) (Example VII.9), which means that the typical depth of a √ random node in a random tree is O( n). These basic properties are only the tip of an iceberg. Indeed, Meir and Moon, who launched the study of simple varieties of trees (the seminal paper [435] can serve as a good starting point) have worked out literally several dozen analyses of parameters of trees, using a strategy similar to the one presented here6. We shall have occasion, in Chapter IX, to return to probabilistic properties of simple varieties of trees satisfying the smooth inverse-function schema—we only indicate here for completeness that 6The main difference is that Meir and Moon appeal to the Darboux–P´olya method discussed in Sec-

tion VI. 11 (p. 433) instead of singularity analysis.

456

VII. APPLICATIONS OF SINGULARITY ANALYSIS

Tree

φ(w)

τ, ρ

PGF of root degree

simple variety

—

—

uφ ′ (τ u)/φ ′ (τ )

binary

(1 + w)2

1, 41

1 u + 1 u2 2 2 1 u + 2 u2 3 3 u/(2 − u)2

unary–binary general Cayley

1 + w + w2 (1 − w)−1 ew

1, 31 1 1 2, 4 1, e−1

ueu−1

(type)

(Bernoulli) (Bernoulli) (sum of two geometric) (shifted Poisson)

Figure VII.5. The distribution of root degree in simple varieties of trees of the smooth inverse-function schema.

√ height is known generally to scale as n and is associated to a limiting theta distribution (see Proposition V.4, p. 329 for the case of Catalan trees and Subsection VII. 10.2, p. 535, for general results), with similar properties holding true for width as shown by Odlyzko–Wilf and Chassaing–Marckert–Yor [112, 463]. Example VII.6. Root degrees in simple varieties. Here is an immediate application of singularity analysis, one that exemplifies the synthetic type of reasoning that goes along with the method. Take for notational simplicity a simple family V that is unlabelled, with OGF V (z) ≡ y(z). Let V [k] be the subset of V composed of all trees whose root has degree equal to k. Since a tree in V [k] is formed by appending a root to a collection of k trees, one has V [k] (z) = φk zy(z)k ,

φk := [wk ]φ(w).

For any fixed k, a singular expansion results from raising both members of (22) to the kth power; in particular, r z z (23) V [k] (z) = φk z τ k − kγ τ k−1 1 − + O 1 − . ρ ρ This is to be compared with the basic estimate (22): the ratio Vn[k] /Vn is then asymptotic to √ the ratio of the coefficients of 1 − z/ρ in the corresponding generating functions, V [k] (z) and V (z) ≡ y(z). Thus, for any fixed k, we have found that (24)

Vn[k] = ρkφk τ k−1 + O(n −1/2 ). Vn

(The error term can be strengthened to O(n −1 ) by pushing the expansion one step further.) The ratio Vn[k] /Vn is the probability that the root of a random tree of size n has degree k. Since ρ = 1/φ ′ (τ ), one can rephrase (24) as follows: In a smooth simple variety of trees, the random variable 1 representing root-degree admits a discrete limit distribution given by (25)

lim PVn (1 = k) =

n→∞

kφk τ k−1 . φ ′ (τ )

(By general principles expounded in Chapter IX, convergence is uniform.) Accordingly, the probability generating function (PGF) of the limit law admits the simple expression EVn u 1 = uφ ′ (τ u)/φ ′ (τ ).

VII. 3. SIMPLE VARIETIES OF TREES AND INVERSE FUNCTIONS

457

The distribution is thus characterized by the fact that its PGF is a scaled version of the derivative of the basic tree constructor φ(w). Figure VII.5 summarizes this property together with its specialization to our four pilot examples. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Additive functionals. Singularity analysis applies to many additive parameters of trees. Consider three tree parameters, ξ, η, σ satisfying the basic relation, deg(t)

ξ(t) = η(t) +

(26)

X

σ (t j ),

j=1

which can be taken to define ξ(t) in terms of the simpler parameter η(t) (a “toll”, cf Subsection VI. 10.3, p. 427) and the sum of values of σ over the root subtrees of t (with deg(t) the degree of the root and t j the jth root-subtree of t). In the Pcase of a recursive parameter, ξ ≡ σ , unwinding the recursion shows that ξ(t) := st η(s), where the sum is extended to all subtrees s of t. As we are interested in average-case analysis, we introduce the cumulative GFs, X X X (27) 4(z) = ξ(t)z |t| , H (z) = η(t)z |t| , 6(z) = σ (t)z |t| , t

t

t

assuming again an unlabelled variety of trees for simplicity. We first state a simple algebraic result which formalizes several of the calculations of Section III. 5, p. 181, dedicated to recursive tree parameters.

Lemma VII.1 (Iteration lemma for trees). For tree parameters from a simple variety with GF y(z) that satisfy the additive relation (26), the cumulative generating functions (27), are related by 4(z) = H (z) + zφ ′ (y(z))6(z).

(28)

In particular, if ξ is defined recursively in terms of η, that is, σ ≡ ξ , one has 4(z) =

(29)

zy ′ (z) H (z) = H (z). 1 − zφ ′ (y(z)) y(z)

Proof. We have e(z), 4(z) = H (z) + 4

where

e(z) := 4

X t∈V

z |t|

deg(t)

X j=1

σ (t j ) .

e(z) according to the values r of root degree, we find Spitting the expression of 4 X e(z) = 4 φr z 1+|t1 |+···+|tr | (σ (t1 ) + σ (t2 ) + · · · + σ (tr )) r ≥0

X

φr 6(z)y(z)r −1 + y(z)6(z)y(z)r −2 + · · · y(z)r −1 6(z)

=

z

=

z6(z) ·

r ≥0

X r φr y(z)r −1 , r ≥0

which yields the linear relation expressing 4 in (28).

458

VII. APPLICATIONS OF SINGULARITY ANALYSIS

In the recursive case, the function 4 is determined by a linear equation, namely 4(z) = H (z) + zφ ′ (y(z))4(z), which, once solved, provides the first form of (29). Differentiation of the fundamental relation y = zφ(y) yields the identity y y y ′ (1 − zφ ′ (y)) = φ(y) = , i.e., 1 − zφ ′ (y) = ′ , z zy

from which the second form results.

VII.7. A symbolic derivation. For a recursive parameter, we can view 4(z) as the GF of trees with one subtree marked, to which is attached a weight of η. Then (29) can be interpreted as follows: point to an arbitrary node at a tree in V (the GF is zy ′ (z)), remove the tree attached to this node (a factor of y(z)−1 ), and replace it by the same tree but now weighted by η (the GF is H (z)). VII.8. Labelled varieties. Formulae (28) and (29) hold verbatim for labelled trees (either of the plane or non-plane type), provided we interpret y(z), 4(z), H (z) as EGFs: 4(z) := P |t| t∈V ξ(t)z /|t|!, and so on. Example VII.7. Mean level profile in simple varieties. The question we address here is that of determining the mean number of nodes at level k (i.e., at distance k from the root) in a random tree of some large size n. (An explicit expression for the joint distribution of nodes at all levels has been developed in Subsection III. 6.2, p. 193, but this multivariate representation is somewhat hard to interpret asymptotically.) Let ξk (t) be the number of nodes at level k in tree t. Define the generating function of cumulated values, X X k (z) := ξk (t)z |t| . t∈V

Clearly, X 0 (z) ≡ y(z) since each tree has a unique root. Then, since the parameter ξk is the sum over subtrees of parameter ξk−1 , we are in a situation exactly covered by (28), with η(t) ≡ 0. The recurrence X k (z) = zφ ′ (y(z))4k−1 (z), is then immediately solved, to the effect that k (30) X k (z) = zφ ′ (y(z)) y(z).

Making use of the (analytic) expansion of φ ′ at τ , namely, φ ′ (y) ∼ φ ′ (τ ) + φ ′′ (τ )(y − τ ) and of ρφ ′ (τ ) = 1, one obtains, for any fixed k: r r r z z z τ −γ 1− ∼ τ − γ (τρφ ′′ (τ )k + 1) 1 − . X k (z) ∼ 1 − kγρφ ′′ (τ ) 1 − ρ ρ ρ Thus comparing the singular part of X k (z) to that of y(z), we find: For fixed k, the mean number of nodes at level k in a tree is of the asymptotic form EVn [ξk ] ∼ Ak + 1,

A := τρφ ′′ (τ ).

This result was first given by Meir and Moon [435]. The striking fact is that, although the number of nodes at level k can at least double at each level, growth is only linear on average. In figurative terms, the immediate vicinity of the root starts like a “cone”, and trees of simple varieties tend to be rather skinny near their base. When used in conjunction with saddle-point bounds (p. 246), the exact GF expression of (30) additionally provides a probabilistic upper bound on the height of trees of the form O(n 1/2+δ ) for any δ > 0. Indeed restrict z to the interval (0, ρ) and assume that k = n 1/2+δ . Let χ be the height parameter. First, we have (31)

PVn (χ ≥ k) ≡ EVn ([[ξk ≥ 1]]) ≤ EVn (ξk ).

VII. 3. SIMPLE VARIETIES OF TREES AND INVERSE FUNCTIONS

459

Figure VII.6. Three random 2–3 trees ( = {0, 2, 3}) of size n = 500 have√height, respectively, 48, 57, 47, in agreement with the fact that height is typically O( n).

Next by saddle-point bounds, for any legal positive x (that is, 0 < x < Rconv (φ)), k k (32) EVn (ξk ) ≤ xφ ′ (y(x)) y(x)x −n ≤ τ xφ ′ (y(x)) x −n .

δ Fix now x = ρ − nn . Local expansions then show that k (33) log xφ ′ (y(x)) x −n ≤ −K n 3δ/2 + O n δ ,

for some positive constant K . Thus, by (31) and (33): In a smooth simple variety of trees, the probability of height exceeding n 1/2+δ is exponentially small, being of the rough form exp(−n 3δ/2 ). Accordingly, the mean height is O(n 1/2+δ ) for√any δ > 0. The moments of height were characterized in [246]: the mean is asymptotic to λ n and the limit distribution is of the Theta type encountered in Example V.8, p. 326, in the particular case of general Catalan trees, where explicit expressions are available. (Further local limit and large deviation estimates appear in [230]; we shall return to the topic of tree height in Subsection VII. 10.1, p. 532.) Figure VII.6 displays three random trees of size n = 500. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

VII.9. The variance of level profiles. The BGF of trees with u marking nodes at level k has an explicit expression, in accordance with the developments of Chapter III. For instance for k = 3, this is zφ(zφ(zφ(uy(z)))). Double differentiation followed by singularity analysis shows that 1 1 VVn [ξk ] ∼ A2 k 2 − A(3 − 4A)k + τ A − 1, 2 2 another result of Meir and Moon [435]. The precise √ analysis of the mean and variance in the interesting regime where k is proportional to n is also given in [435], but it requires either the saddle-point method (Chapter VIII) or the adapted singularity analysis techniques of Theorem IX.16, p. 709. Example VII.8. Mean degree profile. Let ξ(t) ≡ ξk (t) be the number of nodes of degree k in random tree of some variety V. The analysis extends that of the root degree seen earlier. The parameter ξ is an additive functional induced by the basic parameter η(t) ≡ ηk (t) defined by

460

VII. APPLICATIONS OF SINGULARITY ANALYSIS

ηk (t) := [[deg(t) = k]]. By the analysis of root degree, we have for the GF of cumulated values associated to η H (z) = φk zy(z)k , φk := [wk ]φ(w), so that, by the fundamental formula (29),

X (z) = φk zy(z)k

zy ′ (z) = z 2 φk y(z)k−1 y ′ (z). y(z)

The singular expansion of zy ′ (z) can be obtained from that of y(z) by differentiation (Theorem VI.8, p. 419), 1 1 zy ′ (z) = γ √ + O(1), 2 1 − z/ρ

the corresponding coefficient satisfying [z n ](zy ′ ) = nyn . This gives immediately the singularity type of X , which is of the form of an inverse square root. Thus, X (z) ∼ ρφk τ k−1 (zy ′ (z)) implying (ρ = τ/φ(τ ))

Xn φk τ k ∼ . nyn φ(τ )

Consequently, one has: Proposition VII.2. In a smooth simple variety of trees, the mean number of nodes of degree k is asymptotic to λk n, where λk := φk τ k /φ(τ ). Equivalently, the probability distribution of the degree 1⋆ of a random node in a random tree of size n satisfies lim Pn (1⋆ ) = λk ≡

n→∞

φk τ k , φ(τ )

with PGF :

X k

λk u k =

φ(uτ ) . φ(τ )

For the usual tree varieties this gives: Tree

φ(w)

τ, ρ

probability distribution

(type)

binary

(1 + w)2

1, 41 1, 31 1, 1 2 4 1, e−1

PGF: 41 + 12 u + 14 u 2 PGF: 31 + 13 u + 13 u 2

(Bernoulli)

unary–binary general Cayley

1 + w + w2 (1 − w)−1 ew

PGF: 1/(2 − u) PGF: eu−1

(Bernoulli) (Geometric) (Poisson)

For instance, asymptotically, a general Catalan tree has on average n/2 leaves, n/4 nodes of degre 1 n/8 of degree 2, and so on; a Cayley tree has ∼ ne−1 /k! nodes of degree k; for binary (Catalan) trees, the four possible types of nodes each appear with asymptotic frequency 1/4. (These data agree with the fact that a random tree under Vn is distributed like a branching process tree determined by the PGF φ(uτ )/φ(τ ); see Subsection III. 6.2, p. 193.) . . . . . . . . .

VII.10. Variances. The variance of the number of k–ary nodes is ∼ νn, so that the distribution of the number of nodes of this type is concentrated, for each fixed k. The starting point is the BGF defined implicitly by Y (z, u) = z φ(Y (z, u)) + φk (u − 1)Y (z, u)k , upon taking a double derivative with respect to u, setting u = 1, and finally performing singularity analysis on the resulting GF.

VII. 3. SIMPLE VARIETIES OF TREES AND INVERSE FUNCTIONS

461

VII.11. The mother of a random node. The discrepancy in distributions between the root degree and the degree of a random node deserves an explanation. Pick up a node distinct from the root at random in a tree and look at the degree of its mother. The PGF of the law is in the limit uφ ′ (uτ )/φ ′ (τ ). Thus the degree of the root is asymptotically the same as that of the mother of any non-root node. More generally, let X have distribution pk := P(X = k). Construct a random variable Y such that the probability qk := P(Y = k) is proportional both to k and pk . Then for the associated PGFs, the relation q(u) = p′ (u)/ p′ (1) holds. The law of Y is said to be the sizebiased version of the law of X . Here, a mother is picked up with an importance proportional to its degree. In this perspective, Eve appears to be just like a random mother. Example VII.9. Path length. Path length of a tree is the sum of the distances of all nodes to the root. It is defined recursively by ξ(t) = |t| − 1 +

deg(t) X

ξ(t j )

j=1

(Example III.15, p. 184 and Subsection VI. 10.3, p. 427). Within the framework of additive functional of trees (28), we have η(t) = |t| − 1 corresponding to the GF of cumulated values H (z) = zy ′ (z) − y(z), and the fundamental relation (29) gives X (z) = (zy ′ (z) − y(z))

z 2 y ′ (z)2 zy ′ (z) = − zy ′ (z). y(z) y(z)

The type of y ′ (z) at its singularity is Z −1/2 , where Z := (1 − z/ρ). The formula for X (z) involves the square of y ′ , so that the singularity of X (z) is of type Z −1 , resembling a simple pole. This means that the cumulated value X n = [z n ]X (z) grows like ρ −n , so that the mean value of ξ over Vn has growth n 3/2 . Working out the constants, we find X (z) + zy ′ (z) ∼

γ2 1 + O(Z −1/2 ). 4τ Z

As a consequence: Proposition VII.3. In a random tree of size n from a smooth simple variety, the expectation of path length satisfies s p φ(τ ) (34) EVn (ξ ) = λ π n 3 + O(n), . λ := 2τ 2 φ ′′ (τ ) For our classical varieties, the main terms of (34) are then: Binary √ ∼ π n3

unary–binary √ ∼ 12 3π n 3

general √ ∼ 21 π n 3

Cayley q ∼ 12 π n 3 .

Observe that the quantity n1 EVn (ξ ) represents the expected depth of a random node in a random √ tree (the model is then [1 . . n]×Vn ), which is thus ∼ λ n. (This result is consistent with height 1/2 of a tree being with high probability of order O(n ).) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

VII.12. Variance of path length. Path length can be analysed starting from the bivariate generating function given by a functional equation of the difference type (see Chapter III, p. 185), which allows for the computation of higher moments. The standard deviation is found to be asymptotic to 32 n 3/2 for some computable constant 32 > 0, so that the distribution is spread. Louchard [416] and Tak´acs [566] have additionally worked out the asymptotic form of all moments, leading to a characterization of the limit law of path length that can be described in terms of the Airy function: see Subsection VII. 10.1, p. 532.

462

VII. APPLICATIONS OF SINGULARITY ANALYSIS

# components # cyclic nodes

∼ 12 log n √ ∼ π n/2

Tail length (λ)

∼ ne−k /k!

Component size

Cycle length (µ)

∼ ne−1

# terminal nodes # nodes of in-degree k

Tree size

∼

∼

√

√

π n/8 π n/8

∼ n/3

∼ 2n/3

Figure VII.7. Expectations of the main additive parameters of random mappings of size n.

VII.13. Generalizations P of path length. Define the subtree size index of order α ∈ R≥0 to be ξ(t) ≡ ξα (t) := st |s|α , where the sum is extended to all the subtrees s of t. This corresponds to a recursively defined parameter with η(t) = |t|α . The results of Section VI. 10 relative to Hadamard products and polylogarithms make it possible to analyse the singularities of H (z) and X (z). It is found that there are three different regimes α > 12 EVn (ξ ) ∼ K α n α

α = 21 EVn (ξ ) ∼ K 1/2 n log n

α < 12 EVn (ξ ) ∼ K α n

where each K α is a computable constant. (This extends the results of Subsection VI. 10.3, p. 427 to all simple varieties of trees that are smooth.)

VII. 3.3. Mappings. The basic construction of mappings (Chapter II, p. 129), F = exp(K ) F = S ET(K) 1 K = C YC(T ) H⇒ (35) K = log 1 − T T = Z ⋆ S ET(T ) T = ze T ,

builds maps from Cayley trees, which constitute a smooth simple variety. The construction lends itself to a number of multivariate extensions. For instance, we already know from Example VII.3, p. 449, that the number of components is asymptotic to 21 log n, both on average and in probability. Take next the parameter χ equal to the number of cyclic points, which gives rise to the BGF 1 F(z, u) = exp log = (1 − uT )−1 . 1 − uT The mean number of a cyclic points, for a random mapping of size n, is accordingly T n! ∂ n! F(z, u) (36) µn ≡ EFn [χ ] = n [z n ] . = n [z n ] n ∂u n (1 − T )2 u=1

Singularity analysis is immediate, since T (1 − T )2

∼

z→e−1

1 1 2 1 − ez

−→

[z n ]

T 1 n e . ∼ 2 n→∞ 2 (1 − T )

Thus: √ The mean number of cyclic points in a random mapping of size n is asymptotic to π n/2. Many parameters can be similarly analysed in a systematic manner, thanks to generating function, as shown in the survey [247]: see Figure VII.7 for a summary

VII. 3. SIMPLE VARIETIES OF TREES AND INVERSE FUNCTIONS

463

Figure VII.8. Two views of a random mapping of size n = 100. The random mapping has three connected components, with cycles of respective size 2, 4, 4; it is made of fairly skinny trees, has a giant component of size 75, and its diameter equals 14.

of results whose proofs we leave as exercises to the reader. The left-most table describes global parameters of mappings; the right-most table is relative to properties of random point in random n-mapping: λ is the distance to its cycle of a random point, µ the length of the cycle to which the point leads, tree size and component size are, respectively, the size of the largest tree containing the point and the size of its (weakly) connected component. In particular, a random mapping of size n has relatively few components, some of which are expected to be of a large size. The estimates of Figure VII.7 are in fair agreement with what is observed on the single sample of size n = 100 of Figure VII.8: this particular mapping has 3 components (the average is about 2.97), 10 cyclic points (the average, as calculated in (36), is about 12.20), but a fairly large diameter—the maximum value of λ + µ, taken over all nodes—equal to 14, and a giant component of size 75. The proportion of nodes of degree 0, 1, 2, 3, 4 turns out to be, respectively, 39%, 33%, 21%, 7%, 1%, to be compared against the asymptotic values given by a Poisson law of rate 1 (analogous to the degree profile of Cayley trees found in Example VII.8); namely 36.7%, 36.7%, 18.3%, 6.1%, 1.5%.

VII.14. Extremal statistics on mappings. Let λmax , µmax , and ρ max be the maximum values of λ, µ, and ρ, taken over all the possible starting points, where ρ = λ + µ. Then, the expectations satisfy [247] √ √ √ EFn (λmax ) ∼ κ1 n, EFn (µmax ) ∼ κ2 n, EFn (ρ max ) ∼ κ3 n, √ . . . where κ1 = 2π log 2 = 1.73746, κ2 = 0.78248 and κ3 = 2.4149. (For the estimate relative max to ρ , see also [12].) The largest tree and the largest components have expectations asymptotic, respectively, to . . δ1 n and δ2 n, where δ1 = 0.48 and δ2 = 0.7582.

464

VII. APPLICATIONS OF SINGULARITY ANALYSIS

The properties outlined above for the class of all mappings also prove to be universal for a wide variety of mappings defined by degree restrictions of various sorts: we outline the basis of the corresponding theory in Example VII.10, then show some surprising applications in Example VII.11. Example VII.10. Simple varieties of mappings. Let be a subset of the integers containing 0 and at least another integer greater than 1. Consider mappings φ ∈ F such that the number of preimages of any point is constrained to lie in . Such special mappings may serve to model the behaviour of special classes of functions under iteration, and are accordingly of interest in various areas of computational number theory and cryptography. For instance, the quadratic functions φ(x) = x 2 + a over F p have the property that each element y has either zero, one, or two preimages (depending on whether y − a is a quadratic non-residue, 0, or a quadratic residue). The basic construction of mappings needs to be amended. Start with the family of trees T that are the simple variety corresponding to : X uω (37) T = zφ(T ), φ(w) := . ω! ω∈

At any vertex on a cycle, one must graft r trees with the constraint that r + 1 ∈ (since one edge is coming from the cycle itself). Such legal tuples with a root appended are represented by U = zφ ′ (T ),

(38)

since φ is an exponential generating function and shift (r 7→ (r + 1)) corresponds to differentiation. Then connected components and components are formed in the usual way by 1 1 , F = exp(K ) = . 1−U 1−U The three relations (37), (38), (39) fully determine the EGF of –restricted mappings. The function φ is a subseries of the exponential function; hence, it is entire and it satisfies automatically the smoothness conditions of Theorem VII.2, p. 453. With τ the characteristic value, the function T (z) then has a square-root singularity at ρ = τ/φ(τ ). The same holds for U , which admits the singular expansion (with γ1 a constant simply related to γ of equation (22)) r z (40) U (z) ∼ 1 − γ1 1 − , ρ (39)

K = log

since U = zφ ′ (T ). Thus, eventually:

F(z) ∼ q

κ , 1 − ρz

κ :=

1 . γ1

There results the universality of an n −1/2 counting law in such constrained mappings: Proposition VII.4. Consider mappings with node degrees in a set ⊆ Z≥0 , such that the corresponding tree family belongs to the smooth implicit function schema and is aperiodic. The number of mappings of size n satisfies s 1 φ ′ (τ )2 κ κ= Fn ∼ √ ρ −n , . n! 2φ(τ )φ ′′ (τ ) πn This statement nicely extends what is known to hold for unrestricted mappings. The analysis of additive functionals can then proceed on lines very similar to the case of standard mappings, to the effect that the estimates of the same form as in Figure VII.7 hold, albeit with

VII. 3. SIMPLE VARIETIES OF TREES AND INVERSE FUNCTIONS

465

different multiplicative factors. The programme just sketched has been carried out in a thorough manner by Arney and Bender [18], whose paper provides a detailed treatment. . . . . . . Example VII.11. Applications of random mapping statistics. There are interesting consequences of the foregoing asymptotic theory of random mappings in several areas of computational mathematics, as we now briefly explain. Random number generators. Many (pseudo) random number generators operate by iterating a given function ϕ over a finite domaine E; usually, E is a large integer interval [0 . . N − 1]. Such a scheme produces a pseudo-random sequence u 0 , u 1 , u 2 , . . ., where u 0 is the “seed” and u n+1 = ϕ(u n ). Particular strategies are known for the choice of ϕ, which ensure that the “period” (the maximum of ρ = λ + µ, where λ is the distance to cycle and µ is the cycle’s length) is of the order of N : this is for instance granted by linear congruential generators and feedback register algorithms; see Knuth’s authoritative discussion in [379, Ch. 3]. By contrast, a randomly chosen √ function ϕ has expected O( N ) cycle time (Figure VII.7, p. 462), so that it is highly likely to give rise to a poor generator. As the popular adage says: “A random random number generator is bad!”. Accordingly, one can make use of the results of Figure VII.7 and Example VII.10 in order to compare statistical properties of a proposed random number generator to properties of a random function, and discard the former if there is a manifest closeness. For instance, take ϕ to be ϕ(x) := x 2 + 1 mod (106 + 3),

6 + 3) is expected to cycle where the modulus is a prime number. A random mapping of size (10√ on average after about 1250 steps (the expectation of ρ = λ + µ is ∼ π N /2 by Figure VII.7). From five starting values u 0 , we observe the following periods

31 314 3141 31415 314159 687 985 813 557 932 √ whose magnitude looks suspiciously like N . Such a random number generator is thus to be discarded. For similar reasons, von Neumann’s well-known “middle-square” procedure (start from an ℓ-digit number, then repeatedly square and extract the middle digits) makes for a rather poor random number generator [379, p. 5]. (Related applications to cryptography are presented by Quisquater and Delescaille in [501].)

(41)

u0 : ρ ≡λ+µ :

3 1569

Floyd’s cycle detection. There is a spectacular algorithm due to Floyd [379, Ex. 3.1.6], for cycle detection, which is well worth knowing when one needs to experiment with large mappings. Given an initial seed x0 and a mapping ϕ, Floyd’s algorithm determines, up to a small factor, the value of ρ(x0 ) = λ(x0 ) + µ(x0 ), using only two registers. The principle is as follows. Start a tortoise and a hare on u 0 at time 0; then, let the tortoise move at speed 1 along the rho-shaped path and let the hare move at twice the speed. After λ(x0 ) steps, the tortoise joins the cycle, from which time on, the hare, which is already on the cycle, will catch the tortoise after at most µ(x0 ) steps, since their speed differential on the cycle is one. Pictorially:

λ

µ

466

VII. APPLICATIONS OF SINGULARITY ANALYSIS

In more dignified terms, setting X 0 = u0,

X n+1 = ϕ(X n ),

and

Y0 = u 0 ,

Yn+1 = ϕ(ϕ(X n )),

we have the property that the first value ν such that X ν = Yν ≡ X 2ν must satisfy the inequalities (42)

λ ≤ ν ≤ λ + µ ≤ 2ν.

The corresponding algorithm is then extremely short: Algorithm: Floyd’s Cycle Detector: tortoise := x0 ; hare := x0 ; ν := 0; repeat tortoise :=ϕ(tortoise); hare := ϕ(ϕ(hare)); ν := ν + 1; until tortoise = hare {ν is an estimate of λ + µ in the sense of (42)}. Pollard’s rho method for integer factoring. Pollard [487] had the insight to exploit Floyd’s algorithm in order to develop an efficient integer factoring method. Assume heuristically that a quadratic function x 7→ x 2 + a mod p, with p a prime number, has statistical properties similar to those of a random function (we have verified a particular case by (41) above). It must then √ tend to cycle after about p steps. Let N be a (large) number to be factored, and assume for simplicity that N = pq, with p and q both prime (but unknown!). Choose a random a and a random initial value x0 , fix ϕ(x) = x 2 + a

(mod N ),

and run the hare-and-tortoise algorithm. By the Chinese Remainder Theorem, the value of a number x mod N is determined by the pair (x mod p, x mod q); the tortoise T and the hare H can then be seen as running two simultaneous races, one modulo p, the other modulo q. Say √ that p < q. After about p steps, one is likely to have H≡T

(mod p),

while, most probably, hare and tortoise will be non-congruent mod q. In other words, the greatest common divisor of the difference (H − T ) and N will provide p; hence it factors N . The resulting algorithm is also extremely short: Algorithm: Pollard’s Integer Factoring: choose a, x0 randomly in [0 . . N − 1]; T := x0 ; H := x0 ; repeat T := (T 2 + a) mod N ; H := (H 2 + a)2 + a mod N ; D := gcd(H − T, N ); until D 6= 1 {if D 6= 0, a non-trivial divisor has been found}. The agreement with what the theory of random mappings predicts is excellent: one indeed obtains an algorithm that factors large numbers N in O(N 1/4 ) operations with high probability (see for instance the data in [538, p. 470]). Although Pollard’s algorithm is, for very large N , subsumed by other factoring methods, it is still the best for moderate values of N or for numbers with small divisors, where it proves far superior to trial divisions. Equally importantly, similar ideas serve in many areas of computational number theory; for instance the determination of discrete logarithms. (Proving rigorously what one observes in simulations is another story: it often requires advanced methods of number theory [23, 442].) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

VII. 4. TREE-LIKE STRUCTURES AND IMPLICIT FUNCTIONS

467

VII.15. Probabilities of first-order sentences. A beautiful theorem of Lynch [426], much in line with the global aims of analytic combinatorics, gives a class of properties of random mappings for which asymptotic probabilities are systematically computable. In mathematical logic, a first-order sentence is built out of variables, equality, boolean connectives (∨, ∧, ¬, etc), and quantifiers (∀, ∃). In addition, there is a function symbol ϕ, representing a generic mapping. Theorem. Given a property P expressed by a first-order sentence, let µn (P) be the probability that P is satisfied by a random mapping ϕ of size n. Then the quantity µ∞ (P) = limn→∞ µn (P) exists and its value is given by an expression consisting of integer constants and the operators +, −, ×, ÷, and e x . For instance: P : µ∞ (P) :

ϕ is perm.

ϕ without fixed pt.

∀x∃yϕ(y) = x

∀x¬ϕ(x) = x

0

e−1

ϕ has #leaves ≥ 2

∃x, y [x 6= y ∧ ∀z[ϕ(z) 6= x ∧ ϕ(z) 6= y]] 1

One can express in this language a property like P12 : “all cycles of length 1 are attached to −1+e−1

. The proof of the theotrees of height at most 2”, for which the limit probability is e−1+e rem is based on Ehrenfeucht games supplemented by ingenious inclusion–exclusion arguments. (Many cases, like P12 , can be directly treated by singularity analysis.) Compton [125, 126, 127] has produced lucid surveys of this area, known as finite model theory.

VII. 4. Tree-like structures and implicit functions The aim of this section is to demonstrate the universality of the square-root singularity type for classes of recursively defined structures, which considerably extend the case of (smooth) simple varieties of trees. The starting point is the investigation of recursive classes Y, with associated GF y(z), that correspond to a specification: (43)

Y = G[Z, Y]

H⇒

y(z) = G(z, y(z)).

In the labelled case, y(z) is an EGF and G may be an arbitrary composition of basic constructors, which is reflected by a bivariate function G(z, w); in the unlabelled case, y(z) is an OGF and G may be an arbitrary composition of unions, products, and sequences. (P´olya operators corresponding to unlabelled sets and cycles are discussed in Section VII. 5, p. 475.) This situation covers structures that we have already seen, like Schr¨oder’s bracketing systems (Chapter I, p. 69) and hierarchies (Chapter II, p. 128), as well as new ones to be examined here; namely, paths with diagonal steps and trees with variable node sizes or edge lengths. VII. 4.1. The smooth implicit-function schema. The investigation of (43) necessitates certain analytic conditions to be satisfied by the bivariate function G, which we first encapsulate into the definition of a schema. P Definition VII.4. Let y(z) be a function analytic at 0, y(z) = n≥0 yn z n , with y0 = 0 and yn ≥ 0. The function is said to belong to the smooth implicit-function schema if there exists a bivariate G(z, w) such that y(z) = G(z, y(z)), where G(z, w) satisfies the following conditions.

468

VII. APPLICATIONS OF SINGULARITY ANALYSIS

P (I1 ): G(z, w) = m,n≥0 gm,n z m w n is analytic in a domain |z| < R and |w| < S, for some R, S > 0. (I2 ): The coefficients of G satisfy (44)

gm,n ≥ 0, g0,0 = 0, g0,1 6= 1, gm,n > 0 for some m and for some n ≥ 2.

(I3 ): There exist two numbers r, s, such that 0 < r < R and 0 < s < S, satisfying the system of equations, (45)

G(r, s) = s,

G w (r, s) = 1,

with r < R,

s < S,

which is called the characteristic system. A class Y with a generating y(z) satisfying y(z) = G(z, y(z)) is also said to belong to the smooth implicit-function schema. Postulating that G(z, w) is analytic and with non-negative coefficients is a minimal assumption in the context of analytic combinatorics. The problem is assumed to be normalized, so that y(0) = 0 and G(0, 0) = 0, the condition g0,1 6= 1 being imposed to avoid that the implicit equation be of the reducible form y = y + · · · (first line of (44)). The second condition of (44) means that in G(z, y), the dependency on y is nonlinear (otherwise, the analysis reduces to rational and meromorphic asymptotic methods of Chapter V). The major analytic condition is (I3 ), which postulates the existence of positive solutions r, s to the characteristic system within the domain of analyticity of G. The main result7 due to Meir and Moon [439] expresses universality of the squareroot singularity together with its usual consequences regarding asymptotic counting. Theorem VII.3 (Smooth implicit-function schema). Let y(z) belong to the smooth implicit-function schema defined by G(z, w), with (r, s) the positive solution of the characteristic system. Then, y(z) converges at z = r , where it has a square-root singularity, s p 2r G z (r, s) y(z) = s − γ 1 − z/r + O(1 − z/r ), , γ := z→r G ww (r, s) the expansion being valid in a 1–domain. If, in addition, y(z) is aperiodic8, then r is the unique dominant singularity of y and the coefficients satisfy γ [z n ]y(z) = √ r −n 1 + O(n −1 ) . n→∞ 2 π n 3 7 This theorem has an interesting history. An overly general version of it was first stated by Bender in 1974 (Theorem 5 of [36]). Canfield [102] pointed out ten years later that Bender’s conditions were not quite sufficient to grant square-root singularity. A corrected statement was given by Meir and Moon in [439] with a further (minor) erratum in [438]. We follow here the form given in Theorem 10.13 of Odlyzko’s survey [461] with the correction of another minor misprint (regarding g0,1 which should read g0,1 6= 1). A statement concerning a restricted class of functions (either polynomial or entire) already appears in Hille’s book [334, vol. I, p. 274]. 8In the usual sense of Definition IV.5, p. 266. Equivalently, there exist three indices i < j < k such that yi y j yk 6= 0 and gcd( j − i, k − i) = 1.

VII. 4. TREE-LIKE STRUCTURES AND IMPLICIT FUNCTIONS

469

Observe that the statement implies the existence of exactly one root of the characteristic system within the part of the positive quadrant where G is analytic, since, obviously, yn cannot admit two asymptotic expressions with different parameters. A complete expansion exists in powers of (1 − z/r )1/2 (for y(z)) and in powers of 1/n (for yn ), while periodic cases can be treated by a simple extension of the technical apparatus to be developed. The proof of this theorem first necessitates two lemmas of independent interest: (i) Lemma VII.2 is logically equivalent to an analytic version of the classical Implicit Function Theorem found in Appendix B.5: Implicit Function Theorem, p. 753. (ii) Lemma VII.3 supplements this by describing what happens at a point where the implicit function theorem “fails”. These two statements extend the analytic and singular inversion lemmas of Subsection IV. 7.1, p. 275. Lemma VII.2 (Analytic Implicit Functions). Let F(z, w) be z bivariate function analytic at (z, w) = (z 0 , w0 ). Assume that F(z 0 , w0 ) = 0 and Fw (z 0 , w0 ) 6= 0. Then, there exists a unique function y(z) analytic in a neighbourhood of z 0 such that y(z 0 ) = w0 and F(z, y(z)) = 0.

Proof. This is a restatement of the Analytic Implicit Function Theorem of Appendix B.5: Implicit Function Theorem, p. 753, upon effecting a translation z 7→ z + z 0 , w 7→ w + w0 . Lemma VII.3 (Singular Implicit Functions). Let F(z, w) be a bivariate function analytic at (z, w) = (z 0 , w0 ). Assume the conditions: F(z 0 , w0 ) = 0, Fz (z 0 , w0 ) 6= 0, Fw (z 0 , w0 ) = 0, and Fww (z 0 , w0 ) 6= 0. Choose an arbitrary ray of angle θ emanating from z 0 . Then there exists a neighbourhood of z 0 such that at every point z of with z 6= z 0 and z not on the ray, the equation F(z, y) = 0 admits two analytic solutions y1 (z) and y2 (z) that satisfy, as z → z 0 : s p 2z 0 Fz (z 0 , w0 ) y1 (z) = y0 − γ 1 − z/z 0 + O (1 − z/z 0 )) , γ := , Fww (z 0 , w0 ) √ √ to − . and similarly for y2 whose expansion is obtained by changing Proof. Locally, near (r, s), the function F(z, w) behaves like

1 F + (w − s)Fw + (z − r )Fz + (w − s)2 Fww , 2 (plus smaller order terms), where F and its derivatives are evaluated at the point (r, s). Since F = Fw = 0, cancelling (46) suggests for the solutions of F(z, w) = 0 near z = r the form √ w − s = ±γ r − z + O(z − r ), (46)

which is consistent with the statement. This informal argument can be justified by the following steps (details omitted): (a) establish the existence of a formal solution in powers of ±(1 − z/r )1/2 ; (b) prove, by the method of majorant series, that the formal solutions also converge locally and provide a solution to the equation. Alternatively, by the Weierstrass Preparation Theorem (Appendix B.5: Implicit Function Theorem, p. 753) the two solutions y1 (z), y2 (z) that assume the value s

470

VII. APPLICATIONS OF SINGULARITY ANALYSIS

(w)

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0 0

0.2

0.4

0.6

0.8

1.0

(z)

Figure VII.9. The connection problem for the equation w = 41 z + w2 (with explicit √ forms w = (1 ± 1 − z)/2): the combinatorial solution y(z) near z = 0 and the two analytic solutions y1 (z), y2 (z) near z = 1.

at z = r are solutions of a quadratic equation

(Y − s)2 + b(z)(Y − s) + c(z) = 0,

where b and c are analytic at z = r , with b(r ) = c(r ) = 0. The solutions are then obtained by the usual formula for solving a quadratic equation, p 1 −b(z) ± b(z)2 − 4c(z) , Y −s = 2 which provides for y1 (z) an expression as the square-root of an analytic function and yields the statement. It is now possible to return to the proof of our main statement. Proof. [Theorem VII.3] Given the two lemmas, the general idea of the proof of Theorem VII.3 can be easily grasped. Set F(z, w) = w − G(z, w). There exists a unique analytic function y(z) satisfying y = G(z, y) near z = 0, by the analytic lemma. On the other hand, by the singular lemma, near the point (z, w) = (r, s), there exist two solutions y1 , y2 , both of which have a square root singularity. Given the positive character of the coefficients of G, it is not hard to see that, of y1 , y2 , the function y1 (z) is increasing as z approaches z 0 from the left (assuming the principal determination of the square root in the definition of γ ). A simple picture of the situation regarding the solutions to the equation y = G(z, y) is exemplified by Figure VII.9. The problem is then to show that a smooth analytic curve (the thin-line curve in Figure VII.9) does connect the positive-coefficient solution at 0 to the increasingbranch solution at r . Precisely, one needs to check that y1 (z) (defined near r ) is the analytic continuation of y(z) (defined near 0) as z increases along the positive real axis. This is indeed a delicate connection problem whose technical proof is discussed

VII. 4. TREE-LIKE STRUCTURES AND IMPLICIT FUNCTIONS

471

in Note VII.16. Once this fact is granted and it has been verified that r is the unique dominant singularity of y(z) (Note VII.17), the statement of Theorem VII.3 follows directly by singularity analysis.

VII.16. The connection problem for implicit functions. A proof that y(z) and y1 (z) are well connected is given by Meir and Moon in the study [439], from which our description is adapted. Let ρ be the radius of convergence of y(z) at 0 and τ = y(ρ). The point ρ is a singularity of y(z) by Pringsheim’s Theorem. The goal is to establish that ρ = r and τ = s. Regarding the curve C = (z, y(z)) 0 ≤ z ≤ ρ , this means that three cases are to be excluded: (a) C stays entirely in the interior of the rectangle R := (z, y) 0 ≤ z ≤ r, 0 ≤ y ≤ s .

(b) C intersects the upper side of the rectangle R at some point of abscissa r0 < r where y(r0 ) = s. (c) C intersects the right-most side of the rectangle R at the point (r, y(r )) with y(r ) < s. Graphically, the three cases are depicted in Figure VII.10.

(b) (a)

(c)

Figure VII.10. The three cases (a), (b), and (c), to be excluded (solid lines).

In the discussion, we make use of the fact that G(z, w), which has non-negative coefficients is an increasing function in each of its argument. Also, the form (47)

y′ =

G z (z, y) , 1 − G w (z, y)

shows differentiability (hence analyticity) of the solution y as soon as G w (z, y) 6= 1. Case (a) is excluded. Assume that 0 < ρ < r and 0 < τ < s. Then, we have G w (r, s) = 1, and by monotonicity properties of G w , the inequality G w (ρ, τ ) < 1 holds. But then y(z) must be analytic at z = ρ, which contradicts the fact that ρ is a singularity. Case (b) is excluded. Assume that 0 < r0 < r and y(r0 ) = s. Then there are two distinct points on the implicit curve y = G(z, y) at the same altitude, namely (r0 , s) and (r, s), implying the equalities y(r0 ) = G(r0 , y(r0 )) = s = G(r, s), which contradicts the monotonicity properties of G. Case (c) is excluded. Assume that y(r ) < s. Let a < r be a point chosen close enough to r . Then above a, there are three branches of the curve y = G(z, y), namely y(a), y1 (a), y2 (a), where the existence of y1 , y2 results from Lemma VII.3. This means that the function y 7→ G(a, y) has a graph that intersects the main diagonal at three points, a contradiction with the fact that G(a, y) is a convex function of y.

472

VII. APPLICATIONS OF SINGULARITY ANALYSIS

VII.17. Unicity of the dominant singularity. From the previous note, we know that y(r ) = s, with r the radius of convergence of y. The aperiodicity of y implies that |y(ζ )| < y(r ) for all |ζ | such that |ζ | = r and |ζ | 6= r (see the Daffodil Lemma IV.1, p. 266). One then has for any such ζ the property: |G w (ζ, y(ζ ))| < G(r, s) = 1, by monotonicity of G w . But then by (47) above, this implies that y(ζ ) is analytic at ζ . The solutions to the characteristic system (45) can be regarded as the intersection points of two curves, namely, G(r, s) − s = 0,

G w (r, s) = 1.

Here are plots in the case of two functions G: the first one has non-negative coefficients whereas the second one (corresponding to a counterexample of Canfield [102]) involves negative coefficients. Positivity of coefficients implies convexity properties that avoid pathological situations. G(z, y) =

1 − 1 − y − y3 1−z−y (positive)

z 24 − 9y + y 2 (not positive)

G(z, y) =

0.4

4

(s) 0.2

(s) 2

0

0.1

0

0.2 (r)

10 (r)

20

VII. 4.2. Combinatorial applications. Many combinatorial classes, which admit a recursive specification of the form Y = G(Z, Y), as in (43), p. 467, can be subjected to Theorem VII.3. The resulting structures are, to varying degrees, avatars of tree structures. In what follows, we describe a few instances in which the squareroot universality holds. (i) Hierarchies are trees enumerated by the number of their leaves (Examples VII.12 and VII.13). (ii) Trees with variable node sizes generalize simple families of trees; they occur in particular as mathematical models of secondary structures in biology (Example VII.14). (iii) Lattice paths with variable edge lengths are attached to some of the most classical objects of combinatorial theory (Note VII.19). Example VII.12. Labelled hierarchies. The class L of labelled hierarchies, as defined in Note II.19, p. 128, satisfies L = Z + S ET≥2 (L)

H⇒

L = z + eL − 1 − L .

VII. 4. TREE-LIKE STRUCTURES AND IMPLICIT FUNCTIONS

473

Indo-European

Celtic

Irish

German

Germanic

WG

English

Italic

NG

Greek

French

Danish

Armenian

BaSl

Italian

Slavic

Baltic

Polish

Russian

InIr

Persian

Urdu

Hindi

Lithuanian

Figure VII.11. A hierarchy placed on some of the modern Indo-European languages.

These occur in statistical classification theory: given a collection of n distinguished items, L n is the number of ways of superimposing a non-trivial classification (cf Figure VII.11). Such abstract classifications usually have no planar structure, hence our modelling by a labelled set construction. In the notations of Definition VII.4, p. 467, the basic function is G(z, w) = z +ew −1−w, which is analytic in |z| < ∞, |w| < ∞. The characteristic system is r + es − 1 − s = s,

es − 1 = 1,

which has a unique positive solution, s = log 2, r = 2 log 2 − 1, obtained by solving the second equation for s, then propagating the solution to get r . Thus, hierarchies belong to the smooth implicit-function schema, and, by Theorem VII.3, the EGF L(z) has a square-root singularity. One then finds mechanically 1 1 Ln ∼ √ (2 log 2 − 1)−n+1/2 . n! 2 π n3 (The unlabelled counterpart is the object of Note VII.23, p. 479.) . . . . . . . . . . . . . . . . . . . . . . . .

VII.18. The degree profile of hierarchies. Combining BGF techniques and singularity analysis, it is found that a random hierarchy of some large size n has on average about 0.57n nodes of degree 2, 0.18n nodes of degree 3, 0.04n nodes of degree 4, and less than 0.01n nodes of degree 5 or higher. Example VII.13. Trees enumerated by leaves. For a (non-empty) set ⊂ Z≥0 that does not contain 0,1, it makes sense to consider the class of labelled trees, C = Z + S EQ (C)

or

C = Z + S ET (C).

(A similar discussion can be conducted for unlabelled plane trees, with OGFs replacing EGFs.) These are rooted trees (plane or non-plane, respectively), with size determined by the number of leaves and with degrees constrained to lie in . The EGF is then of the form C(z) = z + η(C(z)).

This variety of trees includes the labelled hierarchies, which correspond to η(w) = ew − 1 − w. Assume for simplicity η to be entire (possibly a polynomial). The basic function is G(z, w) = z + η(w), and the characteristic system is s = r + η(s), η′ (s) = 1. Since η′ (0) = 0 and η′ (+∞) = +∞, this system always has a solution: s = η[−1] (1),

r = s − η(s).

474

VII. APPLICATIONS OF SINGULARITY ANALYSIS

A fragment of RNA is, in first approximation, a treelike structure with edges corresponding to base pairs and “loops” corresponding to leaves. There are constraints on the sizes of leaves (taken here between 4 and 7) and length of edges (here between 1 and 4 base pairs). We model such an RNA fragment as a planted tree P attached to a binary tree (Y) with equations: P = AY, Y = AY 2 + B, A = z2 + z4 + z6 + z8, B = z4 + z5 + z6 + z7. Figure VII.12. A simplified combinatorial model of RNA structures analogous to those considered by Waterman et al.

Thus Theorem VII.3 applies, giving (48)

[z n ]C(z) ∼

γ r −n , √ 2 π n3

γ =

r

1 ′′ r η (s), 2

and a complete expansion can be obtained. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Example VII.14. Trees with variable edge lengths and node sizes. Consider unlabelled plane b of ordered pairs (ω, σ ), trees in which nodes can be of different sizes: what is given is a set where a value (ω, σ ) means that a node of degree ω and size σ is allowed. Simple varieties in their basic form correspond to σ ≡ 1; trees enumerated by leaves (including hierarchies) correspond to σ ∈ {0, 1} with σ = 1 iff ω = 0. Figure VII.12 suggests the way such trees can model the self-bonding of single-stranded nucleic acids like RNA, according to Waterman et al. [336, 453, 534, 558]. Clearly an extremely large number of variations are possible. b is The fundamental equation in the case of a finite X Y (z) = P(z, Y (z)), P(z, w) := z σ wω , b (ω,σ )∈

with P a polynomial. In the aperiodic case, there is invariably a formula of the form Yn ∼ κ · An n 3/2 , corresponding to the universal square-root singularity. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

VII.19. Schr¨oder numbers. Consider the class Y of unary–binary trees where unary nodes have size 2, while leaves and binary nodes have the usual size 1. The GF satisfies Y = z + z 2 Y + zY 2 , so that p 1 − z − 1 − 6z + z 2 2 Y (z) = z D(z ), D(z) = . 2z We have D(z) = 1 + 2 z + 6 z 2 + 22 z 3 + 90 z 4 + 394 z 5 + · · · , which is EIS A006318 (“Large Schr¨oder numbers”). By the bijective correspondence between trees and lattice paths, Y2n+1 is in correspondence with excursions of length n made of steps (1, 1), (2, 0), (1, −1). Upon tilting by 45◦ , this is equivalent to paths connecting the lower left corner to the upper right corner of an (n × n) square that are made of horizontal, vertical, and diagonal steps, and never go under

´ VII. 5. UNLABELLED NON-PLANE TREES AND POLYA OPERATORS

475

the main diagonal. The series S = 2z (1 + D) enumerates Schr¨oder’s generalized parenthesis systems (Chapter I, p. 69): S := z + S 2 /(1 − S), and the asymptotic formula √ −n+1/2 1 1 3−2 2 Y2n−1 = Sn = Dn−1 ∼ √ 2 4 π n3 follows straightforwardly.

VII. 5. Unlabelled non-plane trees and P´olya operators Essentially all the results obtained earlier for simple varieties of trees can be extended to the case of non-plane unlabelled trees. P´olya operators are central, and their treatment is typical of the asymptotic theory of unlabelled objects obeying symmetries (i.e., involving the unlabelled MS ET, PS ET, C YC constructions), as we have seen repeatedly in this book. Binary and general trees. We start the discussion by considering the enumeration of two classes of non-plane trees following P´olya [488, 491] and Otter [466], whose articles are important historic sources for the asymptotic theory of non-plane tree enumeration—a brief account also appears in [319]. (These authors used the more traditional method of Darboux instead of singularity analysis, but this distinction is immaterial here, as calculations develop under completely parallel lines under both theories.) The two classes under consideration are those of general and binary non-plane unlabelled trees. In both cases, there is a fairly direct reduction to the enumeration of Cayley trees and of binary trees, which renders explicit several steps of the calculation. The trick is, as usual, to treat values of f (z 2 ), f (z 3 ), . . . , arising from P´olya operators, as “known” analytic quantities. Proposition VII.5 (Special unlabelled non-plane trees). Consider the two classes of unlabelled non-plane trees H = Z × MS ET(H),

W = Z × MS ET{0,2} (W),

respectively, of the general and binary type. Then, with constants γ H , A H and γW , A W given by Notes VII.21 and VII.22, one has γW γH AnH , W2n−1 ∼ √ AnW . (49) Hn ∼ √ 3 2 πn 2 π n3 Proof. (i) General case. The OGF of non-plane unlabelled trees is the analytic solution to the functional equation ! H (z 2 ) H (z) + + ··· . (50) H (z) = z exp 1 2 Let T be the solution to (51)

T (z) = ze T (z) ,

that is to say, the Cayley function. The function H (z) has a radius of convergence ρ strictly less than 1 as its coefficients dominate those of T (z), the radius of convergence . of the latter being exactly e−1 = 0.367. The radius ρ cannot be 0 since the number of trees is bounded from above by the number of plane trees whose OGF has radius 1/4. Thus, one has 1/4 ≤ ρ ≤ e−1 .

476

VII. APPLICATIONS OF SINGULARITY ANALYSIS

Rewriting the defining equation of H (z) as H (z) = ζ e H (z)

with

ζ := z exp

H (z 3 ) H (z 2 ) + + ··· 2 3

!

,

we observe that ζ = ζ (z) is analytic for |z| < ρ 1/2 ; that is, ζ is analytic in a disc that properly contains the disc of convergence of H (z). We may thus rewrite H (z) as H (z) = T (ζ (z)).

Since ζ (z) is analytic at z = ρ, a singular expansion of H (z) near z = ρ results from composing the singular expansion of T at e−1 with the analytic expansion of ζ at ρ. In this way, we get: p z z 1/2 , γ = 2eρζ ′ (ρ). +O 1− (52) H (z) = 1 − γ 1 − ρ ρ Thus, γ ρ −n . [z n ]H (z) ∼ √ 2 π n3 (ii) Binary case. Consider the functional equation 1 1 (53) f (z) = z + f (z)2 + f (z 2 ). 2 2 This enumerates non-plane binary trees with size defined as the number of external nodes, so that W (z) = 1z f (z 2 ). Thus, it suffices to analyse [z n ] f (z), which dispenses us from dealing with periodicity phenomena arising from the parity of n. The OGF f (z) has a radius of convergence ρ that is at least 1/4 (since there are fewer non-plane trees than plane ones). It is also at most 1/2, which is seen from a comparison of f with the solution to the equation g = z + 21 g 2 . We may then proceed as before: treat the term 12 f (z 2 ) as a function analytic in |z| < ρ 1/2 , as though it were known, then solve. To this effect, set 1 ζ (z) := z + f (z 2 ), 2 which exists in |z| < ρ 1/2 . Then, the equation (53) becomes a plain quadratic equation, f = ζ + 21 f 2 , with solution p f (z) = 1 − 1 − 2ζ (z).

The singularity ρ is the smallest positive solution of ζ (ρ) = 1/2. The singular √ expansion of f is obtained by combining the analytic expansion of ζ at ρ with 1 − 2ζ . The usual square-root singularity results: p p γ := 2ρζ ′ (ρ). f (z) ∼ 1 − γ 1 − z/ρ,

This induces the ρ −n n −3/2 form for the coefficients [z n ] f (z) ≡ [z 2n−1 ]W (z). The argument used in the proof of the proposition may seem partly non-constructive. However, numerically, the values of ρ and γ can be determined to great accuracy. See the notes below as well as Finch’s section on “Otter’s tree enumeration constants” [211, Sec. 5.6].

´ VII. 5. UNLABELLED NON-PLANE TREES AND POLYA OPERATORS

477

VII.20. Complete asymptotic expansions for Hn , W2n−1√ . These can be determined since the OGFs admit complete asymptotic expansions in powers of

1 − z/ρ.

VII.21. Numerical evaluation of constants I. Here is an unoptimized procedure controlled by a parameter m ≥ 0 for evaluating the constants γ H , ρ H of (49) relative to general unlabelled non-plane trees. Procedure Get value of ρ(m : integer); 1. Set up a procedure to compute and memorize the Hn on demand; (this can be based on recurrence relations implied by H ′ (z); see [456]) Pm [m] 2. Define f (z) := j=1 Hn z n ; P m 1 [m] k 3. Define ζ [m] (z) := z exp (z ) ; k=2 k f

4. Solve numerically ζ [m] (x) = e−1 for x ∈ (0, 1) to max(m, 10) digits of accuracy; 5. Return x as an approximation to ρ. For instance, a conservative estimate of the accuracy attained for m = 0, 10, . . . , 50 (in a few billion machine instructions) is: m=0 3 · 10−2

m = 10 10−6

m = 20 10−11

m = 30 10−16

m = 40 10−21

m = 50 10−26

Accuracy appears to be a little better than 10−m/2 . This yields to 25D: . . ρ = 0.3383218568992076951961126, A H ≡ ρ −1 = 2.955765285651994974714818, . γ H = 1.559490020374640885542206.

The formula of Proposition VII.5 estimates H100 with a relative error of 10−3 .

VII.22. Numerical evaluation of constants II. The procedure of the previous note adapts easily to binary trees, giving: . . ρ = 0.4026975036714412909690453, A W ≡ ρ −1 = 2.483253536172636858562289, . γW = 1.130033716398972007144137.

The formula of Proposition VII.5 estimates [z 100 ] f (z) with a relative error of 7 · 10−3 .

The results relative to general and binary trees are thus obtained by a modification of the method used for simple varieties of trees, upon treating the P´olya operator part as an analytic variant of the corresponding equations of simple varieties of trees. Alkanes, alcohols, and degree restrictions. The previous two examples suggest that a general theory is possible for varieties of unlabelled non-plane trees, T = Z MS ET (T ), determined by some ⊂ Z≥0 . First, we examine the case of special regular trees defined by = {0, 3}, which, when viewed as alkanes and alcohols, are of relevance to combinatorial chemistry (Example VII.15). Indeed, the problem of enumerating isomers of such chemical compounds has been at the origin of P´olya’s foundational works [488, 491]. Then, we extend the method to the general situation of trees with degrees constrained to an arbitrary finite set (Proposition VII.5). Example VII.15. Non-plane trees and alkanes. In chemistry, carbon atoms (C) are known to have valency 4 while hydrogen (H ) has valency 1. Alkanes, also known as paraffins (Figure VII.13), are acyclic molecules formed of carbon and hydrogen atoms according to this rule and without multiple bonds; they are thus of the type Cn H2n+2 . In combinatorial terms, we are talking of unrooted trees with (total) node degrees in {1, 4}. The rooted version of these trees are determined by the fact that a root is chosen and (out)degrees of nodes lie in the set = {0, 3}; such rooted ternary trees then correspond to alcohols (with the OH group marking one of the carbon atoms).

478

VII. APPLICATIONS OF SINGULARITY ANALYSIS

H | | H--C--H | | H Methane

H H | | | | H--C--C--H | | | | H H

H H H | | | | | | H--C--C--C--H | | | | | | H H H

H OH H | | | | | | H--C--C--C--H | | | | | | H H H

Ethane

Propane

Propanol

Figure VII.13. A few examples of alkanes (C H4 , C2 H6 , C3 H8 ) and an alcohol. Alcohols (A) are the simplest to enumerate, since they correspond to rooted trees. The OGF starts as (EIS A000598) A(z) = 1 + z + z 2 + z 3 + 2 z 4 + 4 z 5 + 8 z 6 + 17 z 7 + 39 z 8 + 89 z 9 + · · · , with size being taken here as the number of internal nodes. The specification is A = {ǫ} + Z MS ET3 (A). + (Equivalently A := A \ {ǫ} satisfies A+ = Z MS ET0,1,2,3 (A+ ).) This implies that A(z) satisfies the functional equation:

1 1 1 A(z 3 ) + A(z)A(z 2 ) + A(z)3 . 3 2 6 In order to apply Theorem VII.3, introduce the function 1 1 1 A(z 3 ) + A(z 2 )w + w3 , (54) G(z, w) = 1 + z 3 2 6 A(z) = 1 + z

which exists in |z| < |ρ|1/2 and |w| < ∞, with ρ the (yet unknown) radius of convergence of A. Like before, the P´olya terms A(z 2 ), A(z 3 ) are treated as known functions. By methods similar to those earlier in the analysis of binary and general trees, we find that the characteristic system admits a solution, . . r = 0.3551817423143773928, s = 2.1174207009536310225,

so that ρ = r and y(ρ) = s. Thus the growth of the number of alcohols is of the form . κρ −n n −3/2 , with ρ −1 = 2.81546. Let B(z) be the OGF of alkanes (EIS A000602), which are unrooted trees: B(z) = 1 + z + z 2 + z 3 + 2 z 4 + 3 z 5 + 5 z 6 + 9 z 7 + 18 z 8 35 z 9 + 75 z 10 + · · · .

For instance, B6 = 5 because there are five isomers of hexane, C6 H14 , for which chemists had to develop a nomenclature system, interestingly enough based on a diameter of the tree: Hexane

3-Methylpentane

2,3-Dimethylbutane

2,2-Dimethylbutane

2-Methylpentane

´ VII. 5. UNLABELLED NON-PLANE TREES AND POLYA OPERATORS

479

The number of structurally different alkanes can then be found by an adaptation of the dissimilarity formula (Equation (57) below and Note VII.26). This problem has served as a powerful motivation for the enumeration of graphical trees and its fascinating history goes back to Cayley. (See Rains and Sloane’s article [502] and [491]). The asymptotic formula of (unrooted) alkanes is of the global form ρ −n n −5/2 , which represents roughly a proportion 1/n of the number of (rooted) alcohols: see below. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

The pattern of analysis should by now be clear, and we state: Theorem VII.4 (Non-plane unlabelled trees). Let ∋ 0 be a finite subset of Z≥0 and consider the variety V of (rooted) unlabelled non-plane trees with outdegrees of nodes in . Assume aperiodicity (gcd() = 1) and the condition that contains at least one element larger than 1. Then the number of trees of size n in V satisfies an asymptotic formula: Vn ∼ C · An n −3/2 .

Proof. The argument given for alcohols is transposed verbatim. Only the existence of a root of the characteristic system needs to be established. The radius of convergence of V (z) is a priori ≤ 1. The fact that ρ is strictly less than 1 is established by means of an exponential lower bound; namely, Vn > B n , for some B > 1 and infinitely many values of n. To obtain this “exponential diversity” of the set of trees, first choose an n 0 such that Vn 0 > 1, then build a perfect d–ary tree (for some d ∈ , d 6= 0, 1) of height h, and finally graft freely subtrees of size n 0 at n/(4n 0 ) of the leaves of the perfect tree. Choosing d such that d h > n/(4n 0 ) yields the lower bound. That the radius of convergence is non-zero results from the upper bound provided by corresponding plane trees whose growth is at most exponential. Thus, one has 0 < ρ < 1. By the translation of multisets of bounded cardinality, the function G is polynomial in finitely many of the quantities {V (z), V (z 2 ), . . .}. Thus the function G(z, w) constructed as in the case of alcohols, in Equation (54), converges in |z| < ρ 1/2 , |w| < ∞. As z → ρ −1 , we must have τ := V (ρ) finite, since otherwise, there would be a contradiction in orders of growth in the nonlinear equation V (z) = · · ·+· · · V (z)d · · · as z → ρ. Thus (ρ, τ ) satisfies τ = G(ρ, τ ). For the derivative, one must have G w (ρ, τ ) = 1 since: (i) a smaller value would mean that V is analytic at ρ (by the Implicit Function Theorem); (ii) a larger value would mean that a singularity has been encountered earlier (by the usual argument on failure of the Implicit Function Theorem). Thus, Theorem VII.3 on positive implicit functions is applicable. A large number of variations are clearly possible as evidenced by the suggestive title of an article [320] published by Harary, Robinson, and Schwenk in 1975: “Twenty-step algorithm for determining the asymptotic number of trees of various species”.

VII.23. Unlabelled hierarchies. The class H of unlabelled hierarchies is specified by H =

Z + MS ET≥2 (H); see Note I.45, p. 72. One has . en ∼ √γ ρ −n , ρ = 0.29224. H 3 2 πn (Compare with the labelled case of Example VII.12, p. 472.) What is the asymptotic proportion of internal nodes of degree r , for a fixed r > 0?

480

VII. APPLICATIONS OF SINGULARITY ANALYSIS

VII.24. Trees with prime degrees and the BBY theory. Bell, Burris, and Yeats [33] develop a general theory meant to account for the fact that, in their words, “almost any family of trees defined by a recursive equation that is nonlinear [. . . ] lead[s] to an asymptotic law of the P´olya form t (n) ∼ Cρ −n n −3/2 ”. Their most general result [33, Th. 75] implies for instance that the number of unlabelled non-plane trees whose node degrees are restricted to be prime numbers admits such a P´olya form (see also Note VII.6, p. 455).

Unlabelled functional graphs (mapping patterns). Unlabelled functional graphs (named “functions” in [319, pp. 69–70]) are denoted here by F; they correspond to unlabelled digraphs with loops allowed, in which each vertex has outdegree equal to 1. They can be specified as multisets of components (L) that are cycles of non-plane unlabelled trees (H), F = MS ET(L);

L = C YC(H);

H = Z × MS ET(H),

a specification that entirely parallels that of mappings in Equation (35), p. 462. Indeed, an unlabelled functional graph can be used to represent the “shape” of a mapping, as obtained when labels are discarded. That is, functional graphs result when mappings are identified up to a possible permutation of their underlying domain. This explains the alternative term of “mapping pattern” [436] sometimes employed for such graphs. The counting sequence starts as 1, 1, 3, 7, 19, 47, 130, 343, 951 (EIS A001372). The OGF H (z) has a square-root singularity by virtue of (52) above, with additionally H (ρ) = 1. The translation of the unlabelled cycle construction, X ϕ( j) 1 log , L(z) = j 1 − H (z j ) j≥1

√ implies that L(z) is logarithmic, and F(z) has a singularity of type 1/ Z where Z := 1 − z/ρ. Thus, unlabelled functional graphs constitute an exp–log structure in the sense of Section VII. 2, p. 445, with κ = 1/2. The number of unlabelled functional graphs thus grows like Cρ −n n −1/2 and the mean number of components in a random functional graph is ∼ 21 log n, as for labelled mappings; see [436] for more on this topic.

VII.25. An alternative form of F(z). Arithmetical simplifications associated with the Euler totient function (A PPENDIX A, p. 721) yield: F(z) =

∞ Y

k=1

1 − H (z k )

−1

.

A similar form applies generally to multisets of unlabelled cycles (Note I.57, p. 85).

Unrooted trees. All the trees considered so far have been rooted and this version is the one most useful in applications. An unrooted tree9 is by definition a connected acyclic (undirected) graph. In that case, the tree is clearly non-plane and no special root node is distinguished. The counting of the class U of unrooted labelled trees is easy: there are plainly Un = n n−2 of these, since each node is distinguished by its label, which entails that 9Unrooted trees are also called sometimes free trees.

´ VII. 5. UNLABELLED NON-PLANE TREES AND POLYA OPERATORS

481

nUn = Tn , with Tn = n n−1 by Cayley’s formula. Also, the EGF U (z) satisfies Z z dy 1 (55) U (z) = T (y) = T (z) − T (z)2 , y 2 0 as already seen when we discussed labelled graphs in Subsection II. 5.3, p. 132. For unrooted unlabelled trees, symmetries are present and a tree can be rooted in a number of ways that depends on its shape. For instance, a star graph leads to a number of different rooted trees that equals 2 (choose either the centre or one of the peripheral nodes), while a line graph gives rise to ⌈n/2⌉ structurally different rooted trees. With H the class of rooted unlabelled trees and I the class of unrooted trees, we have at this stage only a general inequality of the form In ≤ Hn ≤ n In .

A table of values of the ratio Hn /In suggests that the answer is close to the upper bound: (56)

n Hn /In

10 6.78

20 15.58

30 23.89

40 32.15

50 40.39

60 48.62

The solution is provided by a famous exact formula due to Otter (Note VII.26): 1 (57) I (z) = H (z) − H (z)2 − H (z 2 ) , 2 which gives in particular (EIS A000055) I (z) = z + z 2 + z 3 + 2 z 4 + 3 z 5 + 6 z 6 + 11 z 7 + 23 z 8 + · · · . Given (57), it is child’s play to determine the singular expansion of I knowing that of H . The radius of convergence of I is the same as that of H , since the term H (z 2 ) only introduces exponentially small coefficients. Thus, it suffices to analyse H − 21 H 2 : 1 z 1 H (z) − H (z)2 ∼ − δ2 Z + δ3 Z 3/2 + O Z 2 , . Z = 1− 2 2 ρ What is noticeable is the cancellation in coefficients for the term Z 1/2 (since 1 − x − 1 1 2 2 3/2 is the actual singularity type of I . Clearly, 2 (1 − x) = 2 + O(x )), so that Z the constant δ3 is computable from the first four terms in the singular expansion of H at ρ. Then singularity analysis yields: The number of unrooted trees of size n satisfies the formula 3δ3 ρ −n , In ∼ (0.5349496061 . . .) (2.9955765856 . . .)n n −5/2 . (58) In ∼ √ 5 4 πn The numerical values are from [211] and the result is Otter’s original [466]: an unrooted tree of size n gives rise to about different 0.8n rooted trees on average. (The formula (58) corresponds to an error slightly under 10−2 for n = 100.)

VII.26. Dissimilarity theorem for trees. Here is how combinatorics justifies (57), following [50, §4.1]. Let I • (and I •–• ) be the class of unrooted trees with one vertex (respectively, one edge) distinguished. We have I • ∼ = S ET2 (H). The combinatorial = H (rooted trees) and I •–• ∼ isomorphism claimed is (59) I • + I •–• ∼ = I + (I × I) . Proof. A diameter of an unrooted tree is a simple path of maximal length. If the length of any diameter is even, call “centre” its mid-point; otherwise, call “bicentre” its mid-edge. (For

482

VII. APPLICATIONS OF SINGULARITY ANALYSIS

each tree, there is either one centre or one bicentre.) The left-hand side of (59) corresponds to trees that are pointed either at a vertex (I • ) or an edge (I •–• ). The term I on the right-hand side corresponds to cases where the pointing happens to coincide with the canonical centre or bicentre. If there is not coincidence, then, an ordered pair of trees results from a suitable surgery of the pointed tree. [Hint: cut in some canonical way near the pointed vertex or edge.]

VII. 6. Irreducible context-free structures In this section, we discuss an important variety of context-free classes, one that gives rise to the universal law of square-root singularities, itself attached to counting sequences that are of the general asymptotic form An n −3/2 . First, we enunciate an abstract structural result (Theorem VII.5, p. 483) that connects “irreducibility” of context-free systems to the square-root singularity phenomenon. Before engaging into a proof, we first illustrate its scope by describing applications to non-crossing configurations in the plane (these are richer than triangulations introduced in Chapter I) and to random boolean expressions. Finally, we prove an important complex analytic result, the Drmota–Lalley–Woods Theorem (Theorem VII.6, p. 489), which provides the underlying analytic engine needed to establish Theorem VII.5 and justify the asymptotic properties of irreducible context-free specifications. General algebraic functions are to be treated next, in Section VII. 7, p. 493. VII. 6.1. Context-free specifications and the irreducibility schema. We start from the notion of a context-free class already introduced in Subsection I. 5.4, p. 79, which we recall: a class is context-free if it is determined as the first component of a system of combinatorial equations Y1 = F1 (Z, Y1 , . . . , Yr ) .. .. .. (60) . . . Yr = Fr (Z, Y1 , . . . , Yr ), where each F j is a construction that only involves the combinatorial constructions of disjoint union and cartesian product. (This repeats Equation (83) of Chapter I, p. 79.) As seen in Subsection I. 5.4, binary and general trees, triangulations, as well as Dyck and Łukasiewicz languages are typical instances of context-free classes. As a consequence of the symbolic rules of Chapter I, the OGF of a context-free class C is the first component (C(z) ≡ y1 (z)) of the solution of a polynomial system of equations of the form y1 (z) = 81 (z, y1 (z), . . . , yr (z)) .. .. .. (61) . . . yr (z) = 8r (z, y1 (z), . . . , yr (z)),

where the 8 j are polynomials. By elimination (Cf Appendix B.1: Algebraic elimination, p. 739), it is always possible to find a bivariate polynomial P(z, y) such that (62)

P(z, C(z)) = 0,

and C(z) is an algebraic function. (Algebraic functions are discussed in all generality in the next section.)

VII. 6. IRREDUCIBLE CONTEXT-FREE STRUCTURES

483

The case of linear systems has been dealt with in Chapter V, when examining the transfer matrix method. Accordingly, we only need to consider here nonlinear systems (of equations or specifications) defined by the condition that at least one 8 j in (61) is a polynomial of degree 2 or more in the y j , corresponding to the fact that at least one of the constructions F j in (60) involves at least a product Yk Yℓ . Definition VII.5. A context-free specification (60) is said to belong to the irreducible context-free schema if it is nonlinear and its dependency graph (p. 33) is strongly connected. It is said to be aperiodic if all the y j (z) are aperiodic10. Theorem VII.5 (Irreducible context-free schema). A class C that belongs to the irreducible context-free schema has a generating function that has a square-root singularity at its radius of convergence ρ: r z z C(z) = τ − γ 1 − + O 1 − , ρ ρ for computable algebraic numbers ρ, τ, γ . If, in addition, C(z) is aperiodic, then the dominant singularity is unique and the counting sequence satisfies γ ρ −n . (63) Cn ∼ √ 2 π n3 This theorem is none other than a transcription, at the combinatorial level, of a remarkable analytic statement, Theorem VII.6, due to Drmota, Lalley, and Woods, which is proved below (p. 489), is slightly stronger, and is of independent interest. Computability issues. There are two complementary approaches to the calculation of the quantities that appear in (63), one based on the original system (61), the other based on the single equation (62) that results from elimination. We offer at this stage a brief pragmatic discussion of computational aspects, referring the reader to Subsection VII. 6.3, p. 488, and Section VII. 7, p. 493, for context and justifications. (a) System: Considering the proof of Theorem VII.6 below, one should solve, in positive real numbers, a polynomial system of m + 1 equations in the m + 1 unknowns ρ, τ1 , . . . , τm ; namely, τ1 = 81 (ρ, τ1 , . . . , τm ) .. .. .. . . . (64) τm = 8m (ρ, τ1 , . . . , τm ) 0 = J (ρ, τ1 , . . . , τm ),

which one can call the characteristic system. There J is the Jacobian determinant: ∂ (65) J (z, y1 , . . . , ym ) := det δi, j − 8i (z, y1 , . . . , ym ) , ∂yj

10An aperiodic function is such that the span of the coefficient sequence is equal to 1 (Definition IV.5, p. 266). For an irreducible system, it can be checked that all the y j are aperiodic if and only if at least one of the y j is aperiodic.

484

VII. APPLICATIONS OF SINGULARITY ANALYSIS

with δi, j ≡ [[i = j]] being the usual Kronecker symbol. The quantity ρ represents the common radius of convergence of all the y j (z) and τ j = y j (ρ). (In case several possibilities present themselves for ρ, as in Note VII.28, then one can use either a priori combinatorial bounds to filter out the spurious ones11 or make use of the reduction to a single equation as in point (b) below.) The constant γ ≡ γ1 in Theorem VII.5 is then a component of the solution to a linear system of equations (with coefficients in the field generated by ρ, τ j ) and is obtained by the method of undetermined coefficients, since each y j is of the form p (66) y j (z) ∼ τ j − γ j 1 − z/ρ, z → ρ.

(b) Equation: The general techniques are going to be described in Section, §VII. 7, p. 493. They give rise to the following algorithm: (i) determine the exceptional set, identify the proper branch of the algebraic curve and the dominant positive singularity; (ii) determine the coefficients in the singular (Puiseux) expansion, knowing a priori that the singularity is of the square-root type. In all events, symbolic algebra systems prove invaluable in performing the required algebraic eliminations and isolating the combinatorially relevant roots (see, in particular, Pivoteau et al. [485] for a general symbolic–numeric approach). Example VII.16 serves to illustrate some of these computations.

VII.27. Catalan and the Jacobian determinant. For the Catalan GF, defined by y = 1 + zy 2 ,

the characteristic system (64) instantiates to

τ − 1 − ρτ 2 = 0,

1 − 2ρτ = 0,

giving back as expected: ρ = 14 , τ = 2.

VII.28. Burris’ Caveat. As noted by Stanley Burris (private communication), even some very simple context-free specifications may be such that there exist several positive solutions to the characteristic system (64). Consider y1 = z(1 + y2 + y 2 ) 1 (B) : y2 = z(1 + y1 + y 2 ), 2 which is clearly associated to a redundant way of counting unary–binary trees (via a deterministic 2-colouring). The characteristic system is n o τ1 = ρ(1 + τ2 + τ12 ), τ2 = ρ(1 + τ1 + τ22 ), (1 − 2ρτ1 )(1 − 2ρτ2 ) − ρ 2 = 0 . The positive solutions are 1 ρ = , τ1 = τ2 = 1 3

∪

ρ=

1 √ (2 2 − 1), 7

τ1 = τ2 =

√ 2+1 .

Only the first solution is combinatorially significant. (A somewhat similar situation, though it relates to a non-irreducible context-free specification, arises with supertrees of Example VII.20, p. 503: see Figure VII.19, p. 504.)

11This is once more a connection problem, in the sense of p. 470.

VII. 6. IRREDUCIBLE CONTEXT-FREE STRUCTURES

485

VII. 6.2. Combinatorial applications. Lattice animals (Example I.18, p. 80), random walks on free groups [395], directed walks in the plane (see references [27, 392, 395] and p. 506 below), coloured trees [616], and boolean expression trees (reference [115] and Examples VII.17) are only some of the many combinatorial structures belonging to the irreducible context-free schema. Stanley presents in his book [554, Ch. 6] several examples of algebraic GFs, and an inspiring survey is provided by Bousquet-M´elou in [84]. We limit ourselves here to a brief discussion of non-crossing configurations and random boolean expressions. Example VII.16. Non-crossing configurations. Context-free descriptions can model naturally very diverse sorts of objects including particular topological-geometric configurations—we examine here non-crossing planar configurations. The problems considered have their origin in combinatorial musings of the Rev. T.P. Kirkman in 1857 and were revisited in 1974 by Domb and Barett [169] for the purpose of investigating certain perturbative expansions of statistical physics. Our presentation follows closely the synthesis offered by Flajolet and Noy in [245]. Consider, for each value of n, graphs built on vertices that are all the nth complex roots of unity, numbered 0, . . . , n − 1. A non-crossing graph is a graph such that no two of its edges cross. One can also define connected non-crossing graphs, non-crossing forests (acyclic graphs), and non-crossing trees (acyclic connected graphs); see Figure VII.14. Note that the various graphs considered can always be considered as rooted in some canonical way (e.g., at the vertex of smallest index) . Trees. A non-crossing tree is rooted at 0. To the root vertex is attached an ordered collection of vertices, each of which has an end-node ν that is the common root of two non-crossing trees, one on the left of the edge (0, ν) the other on the right of (0, ν). Let T denote the class of trees and U denote the class of trees whose root has been severed. With • ≡ Z denoting a generic node, we have T = • × U,

U = S EQ(U × • × U),

which corresponds graphically to the “butterfly decomposition”:

U=

T= U

U

U

U

U

The reduction to a pure context-free form is obtained by noticing that U = S EQ(V) is equivalent to U = 1 + UV: a specification and the associated polynomial system are then (67) {T = ZU, U = 1 + UV, V = ZUU }

H⇒

{T = zU, U = 1 + U V, V = zU 2 }.

This system relating U and V is irreducible (then, T is immediately obtained from U ), and aperiodicity is obvious from the first few values of the coefficients. The Jacobian (65) of the {U, V }-system (obtained by z → ρ, U → υ, V → β), is 1−β υ = 1 − β − 2ρυ 2 . 2ρυ 1 Thus, the characteristic system (64) giving the singularity of U, V is

{υ = 1 + υβ, β = ρυ 2 , 1 − β − 2ρυ 2 = 0},

486

VII. APPLICATIONS OF SINGULARITY ANALYSIS

(tree)

(forest)

(graph)

(connected graph)

Configuration / OGF

coefficients (exact / asymptotic)

Trees (EIS A001764)

z + z 2 + 3z 3 + 12z 4 + 55z 5 + · · · 3n − 3 1 2n − √ 1 n−1 3 27 ∼ √ ( )n 27 π n 3 4

T 3 − zT + z 2 = 0

Forests (EIS A054727) F 3 + (z 2 − z − 3)F 2 + (z + 3)F − 1 = 0

Connected graphs (EIS A007297) C 3 + C 2 − 3zC + 2z 2 = 0

Graphs (EIS A054726) G 2 + (2z 2 − 3z − 2)G + 3z + 1 = 0

1 + z + 2z 2 + 7z 3 + 33z 4 + 181z 5 · · · n X n 3n − 2 j − 1 1 j=1

2n − j

j −1

n− j

0.07465 ∼ √ (8.22469)n π n3 z + z 2 + 4z 3 + 23z 4 + 156z 5 + · · · 2n−3 X 3n − 3 j − 1 1 n−1 n+ j j −n+1 √ √ j=n−1 2 6 − 3 2 √ n ∼ 6 3 √ 18 π n 3 1 + z + 2z 2 + 8z 3 + 48z 4 + 352z 5 + · · · n−1 n 2n − 2 − j n−1− j 1X 2 (−1) j n−1− j j n j=0 p √ √ n 140 − 99 2 q ∼ 6+4 2 4 π n3

Figure VII.14. (Top) Non-crossing graphs: a tree, a forest, a connected graph, and a general graph. (Bottom) The enumeration of non-crossing configurations by algebraic functions.

VII. 6. IRREDUCIBLE CONTEXT-FREE STRUCTURES

487

4 , υ = 3 , β = 1 . The complete asymptotic formula is whose positive solution is ρ = 27 2 3 displayed in Figure VII.14. (In a simple case like this, we have more: T satisfies T 3 −zT +z 2 = 3n−3 1 0, which, by Lagrange inversion, gives Tn = 2n−1 n−1 .)

Forests. A (non-crossing) forest is a non-crossing graph that is acyclic. In the present context, it is not possible to express forests simply as sequences of trees, because of the geometry of the problem. Starting conventionally from the root vertex 0 and following all connected edges defines a “backbone” tree. To the left of every vertex of the tree, a forest may be placed. There results the decomposition (expressed directly in terms of OGFs) F = 1 + T [z 7→ z F],

(68)

where T is the OGF of trees and F is the OGF of forests. In (68), the term T [z 7→ z F] denotes a functional composition. A context-free specification in standard form results mechanically from (67) upon replacing z by z F: (69)

{ F = 1 + T,

T = z FU,

U = 1 + U V,

V = z FU 2 }.

This system is irreducible and aperiodic, so that the asymptotic shape of Fn is a priori of the form γ ωn n −3/2 according to Theorem VII.5. The characteristic system is found to have three . solutions, of which only one has all its components positive, corresponding to ρ = 0.12158, a 3 2 root of the cubic equation 5ρ − 8ρ − 32ρ + 4 = 0. (The values of constants are otherwise worked out in Example VII.19, p. 502, by means of the equational approach.) Graphs. Similar constructions (see [245]) give the OGFs of connected and general graphs, with the results tabulated in Figure VII.14. In summary: Proposition VII.6. The number of non-crossing trees, forests, connected graphs, and graphs each satisfy an asymptotic formula of the form C An . π n3 The common shape of the asymptotic estimates is worthy of note, as is the fact that binomial expressions are available in each particular case (Note VII.34, p. 495, introduces a general framework that “explains” the existence of such binomial expressions). . . . . . . . . . . . . . . . . . . √

Example VII.17. Random boolean expressions. We reconsider boolean expressions in the form of and–or trees introduced in Example I.15, p. 69, in connection with Hipparchus of Rhodes and Schr¨oder, and in Example I.17, p. 77. Such an expression is described by a binary tree whose internal nodes can be tagged with “∨” (or-function) or “∧” (and-function); external nodes are formal variables and their negations (“literals”). We fix the number of variables to some number m. The class E of all such boolean expressions satisfies a symbolic equation of the form m ∧ ∨ X E = ւ ց + ւ ց + x j + ¬x j . E E E E j=1 Size is taken to be the number of internal (binary) nodes; that is, the number of boolean connectives. Each boolean expression given in the form of such an and–or tree represents a certain m boolean function of m variables, among the 22 functions. The corresponding OGF and coefficients are √ 2m 1 − 1 − 16mz 1 2n n n n+1 ∼ √ E(z) = , E n ≡ [z ]E(z) = 2 (2m) (16m)n , 4z n+1 n π n3 the radius of convergence of E(z) being ρ = 1/(16m).

488

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Our purpose is to establish the following result due to Lefmann and Savick´y [405], our line of proof following [115]. Proposition VII.7. Let f be a boolean function of m variables (m fixed). Then the probability that a random and–or formula of size n computes f converges, as n tends to infinity, to a constant value ̟ ( f ) 6= 0.

Proof. Consider, for each f , the subclass Y f ⊂ E of expressions that compute f . We thus m have 22 such classes. It is then immediate to write combinatorial equations describing the Y f , by considering all the ways in which a function f can arise. Indeed, if f is not a literal, then ∨ ∨ X X ւ ց ւ ց Yf = + Yg Yh Yg Yh , (g∨h)= f (g∧h)= f while, if f = x j (say), then Yf = xj +

∨ ∨ X ւ ց ւ ց + Yg Yh . Yg Yh (g∧h)= f (g∨h)= f X

m

Thus, at generating function level, we have a system of 22 polynomial equations. This system is irreducible: given two functions f and g represented by 8 and Ŵ (say), we can always construct an expression for f involving the expression Ŵ by building a tree of the form (8 ∧ (True ∨ Ŵ)) = ((8 ∧ ((x1 ∨ ¬x1 ) ∨ Ŵ)). Thus any Y f depends on any other Yg . Similar arguments, based on the fact that True = (True ∧ True) = (True ∧ True ∧ True) = · · · ,

with “True” itself representable as (x1 ∨ ¬x1 ) = ((x1 ∧ x1 ) ∨ ¬x1 ) = · · · , guarantee aperiodicity. Thus Theorem VII.5 applies: the Y f all have the same radius of P convergence, and that radius must be equal to that of E(z) (namely ρ = 1/(16m)), since E = f Y f . Thereby the proposition is established.

It is an interesting and largely open problem to characterize the relation between the limit probability ̟ ( f ) of a function f and its structural complexity. At least, the cases m = 1, 2, 3 can be solved exactly and numerically: it appears that functions of low complexity tend to occur much more frequently, as shown by the data of [115]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

VII. 6.3. The analysis of irreducible polynomial systems. The analytic engine behind Theorem VII.5 is a fundamental result, the “Drmota–Lalley–Woods” (DLW) Theorem, due to independent research by several authors: Drmota [172] developed a version of the theorem in the course of studies relative to limit laws in various families of trees defined by context-free grammars; Woods [616], motivated by questions of boolean complexity and finite model theory, gave a form expressed in terms of colouring rules for trees; finally, Lalley [395] came across a similarly general result when quantifying return probabilities for random walks on groups. Drmota and Lalley show how to pull out limit Gaussian laws for simple parameters (by a perturbative analysis; see Chapter IX); Woods shows how to deduce estimates of coefficients even in some periodic or non-irreducible cases. In the treatment that follows we start from a polynomial system of equations, y j = 8 j (z, y1 , . . . , ym ) , j = 1, . . . , m,

VII. 6. IRREDUCIBLE CONTEXT-FREE STRUCTURES

489

in accordance with the notations adopted at the beginning of the section. We only consider nonlinear systems defined by the fact that at least one polynomial 8 j is nonlinear in some of the indeterminates y1 , . . . , ym . (Linear systems have been discussed extensively in Chapter V.) For applications to combinatorics, we define four possible attributes of a polynomial system. The first one is a natural positivity condition. (i) Algebraic positivity (or a-positivity). A polynomial system is said to be apositive if all the component polynomials 8 j have non-negative coefficients. Next, we want to restrict consideration to systems that determine a unique solution vector (y1 , . . . , ym ) ∈ (C[[z]])m . Define the z-valuation val(Ey ) of a vector yE ∈ C[[z]]m as the minimum over all j’s of the individual valuations12 val(y j ). The distance between two vectors is defined as usual by d(E u , vE) = 2− val(Eu −Ev ) . Then: (ii) Algebraic properness (or a-properness). A polynomial system is said to be a-proper if it satisfies a Lipschitz condition d(8(Ey ), 8(Ey ′ )) < K d(Ey , yE ′ )

for some K < 1.

In that case, the transformation 8 is a contraction on the complete metric space of formal power series and, by the general fixed point theorem, the equation yE = 8(Ey ) admits a unique solution. This solution may be obtained by the iterative scheme, yE(0) = (0, . . . , 0)t ,

yE(h+1) = 8(y (h) ),

yE = lim yE(h) . h→∞

in accordance with our discussion of the semantics of recursion, on p. 31. The key notion is irreducibility. To a polynomial system, yE = 8(Ey ), associate its dependency graph defined in the usual way as a graph whose vertices are the numbers 1, . . . , m and the edges ending at a vertex j are k → j, if y j figures in a monomial of 8k . (iii) Algebraic irreducibility (or a-irreducibility). A polynomial system is said to be a-irreducible if its dependency graph is strongly connected. (This notion matches that of Definition VII.5, p. 483.) Finally, one needs the usual technical notion of aperiodicity: (iv) Algebraic aperiodicity (or a-aperiodicity). A proper polynomial system is said to be aperiodic if each of its component solutions y j is aperiodic in the sense of Definition IV.5, p. 266. We can now state: Theorem VII.6 (Irreducible positive polynomial systems, DLW Theorem). Consider a nonlinear polynomial system yE = 8(Ey ) that is a-positive, a-proper, and a-irreducible. Then, all component solutions y j have the same radius of convergence ρ < ∞, and there exist functions h j analytic at the origin such that, in a neighbourhood of ρ: p 1 − z/ρ . (70) yj = h j 12Let f = P∞ f z n with f 6= 0 and f = · · · = f β 0 β−1 = 0; the valuation of f is by definition n=β n

val( f ) = β; see Appendix A.5: Formal power series, p. 730.

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In addition, all other dominant singularities are of the form ρω with ω a root of unity. If furthermore the system is a-aperiodic, all y j have ρ as unique dominant singularity. In that case, the coefficients admit a complete asymptotic expansion, X (71) [z n ]y j (z) ∼ ρ −n dk n −3/2−k , k≥0

for computable dk .

Proof. The proof consists in gathering by stages consequences of the assumptions. It is essentially based on a close examination of “failure” of the multivariate implicit function theorem and the way this situation leads to square-root singularities. (a) As a preliminary observation, we note that each component solution y j is an algebraic function that has a non-zero radius of convergence. This can be checked directly by the method of majorant series (Note IV.20, p. 250), or as a consequence of the multivariate version of the implicit function theorem (Appendix B.5: Implicit Function Theorem, p. 753). (b) Properness together with the positivity of the system implies that each y j (z) has non-negative coefficients in its expansion at 0, since it is a formal limit of approximants that have non-negative coefficients. In particular, by positivity, ρ j is a singularity of y j (by virtue of Pringsheim’s theorem). From the known nature of singularities of algebraic functions (e.g., the Newton–Puiseux Theorem, p. 498 below), there must exist some order R ≥ 0 such that each Rth derivative ∂zR y j (z) becomes infinite as z → ρ − j . We establish now that ρ1 = · · · = ρm . In effect, differentiation of the equations composing the system implies that a derivative of arbitrary order r , ∂zr y j (z), is a linear form in other derivatives ∂zr y j (z) of the same order (and a polynomial form in lower order derivatives); also the linear combination and the polynomial form have nonnegative coefficients. Assume a contrario that the radii were not all equal, say ρ1 = · · · = ρs , with the other radii ρs+1 , . . . being strictly greater. Consider the system differentiated a sufficiently large number of times, R. Then, as z → ρ1 , we must have ∂zR y j tending to infinity for j ≤ s. On the other hand, the quantities ys+1 , etc., being analytic, their Rth derivatives that are analytic as well must tend to finite limits. In other words, because of the irreducibility assumption (and again positivity), infinity has to propagate and we have reached a contradiction. Thus: all the y j have the same radius of convergence. We let ρ denote this common value. (c1 ) The key step consists in establishing the existence of a square-root singularity at the common singularity ρ. Consider first the scalar case, that is (72)

y − φ(z, y) = 0,

where φ is assumed to be a nonlinear polynomial in y and have non-negative coefficients. This case belongs to the smooth implicit function schema, whose argument we briefly revisit under our present perspective. Let y(z) be the unique branch of the algebraic function that is analytic at 0. Comparison of the asymptotic orders in y inside the equality y = φ(z, y) shows that (by

VII. 6. IRREDUCIBLE CONTEXT-FREE STRUCTURES

491

nonlinearity) we cannot have y → ∞ when z tends to a finite limit. Let now ρ be the radius of convergence of y(z). Since y(z) is necessarily finite at its singularity ρ, we set τ = y(ρ) and note that, by continuity, τ − φ(ρ, τ ) = 0. By the implicit function theorem, a solution (z 0 , y0 ) of (72) can be continued analytically as (z, y0 (z)) in the vicinity of z 0 as long as the derivative with respect to y (the simplest form of a Jacobian), J (z 0 , y0 ) := 1 − φ y′ (z 0 , y0 ), remains non-zero. The quantity ρ being a singularity, we must thus have J (ρ, τ ) = 0. ′′ is non-zero at (ρ, τ ) (by nonlinearity On the other hand, the second derivative −φ yy and positivity). Then, the local expansion of the defining equation (72) at (ρ, τ ) binds (z, y) locally by 1 ′′ (ρ, τ ) + · · · = 0, −(z − ρ)φz′ (ρ, τ ) − (y − τ )2 φ yy 2 implying the singular expansion y − τ = −γ (1 − z/ρ)1/2 + · · · . This establishes the first part of the assertion in the scalar case. (c2 ) In the multivariate case, we graft Lalley’s ingenious argument [395] that is based on a linearized version of the system to which Perron–Frobenius theory is applicable. First, irreducibility implies that any component solution y j depends positively and nonlinearly on itself (by possibly iterating 8), so that a contradiction in asymptotic regimes would result, if we suppose that any y j tends to infinity. Each y j (z) remains finite at the positive dominant singularity ρ. Now, the multivariate version of the implicit function theorem (Theorem B.6, p. 755) grants us locally the analytic continuation of any solution y1 , y2 , . . . , ym at z 0 provided there is no vanishing of the Jacobian determinant ∂ J (z 0 , y1 , . . . , ym ) := det δi, j − 8i (z 0 , y1 , . . . , ym ) . ∂yj i, j=1 . . m Thus, we must have (73)

J (ρ, τ1 , . . . , τm ) = 0

where

τ j := y j (ρ).

The next argument uses Perron–Frobenius theory (Subsection V. 5.2 and Note V.34, p. 345) and linear algebra. Consider the Jacobian matrix ∂ K (z, y1 , . . . , ym ) := 8i (z, y1 , . . . , ym ) , ∂yj i, j=1 . . m which represents the “linear part” of 8. For z, y1 , . . . , ym all non-negative, the matrix K has positive entries (by positivity of 8) so that it is amenable to Perron–Frobenius theory. In particular it has a positive eigenvalue λ(z, y1 , . . . , ym ) that dominates all the other in modulus. The quantity λ(z) := λ(z, y1 (z), . . . , ym (z))

492

VII. APPLICATIONS OF SINGULARITY ANALYSIS

is increasing, as it is an increasing function of the matrix entries that themselves increase with z for z ≥ 0. We propose to prove that λ(ρ) = 1, In effect, λ(ρ) < 1 is excluded since otherwise (I − K ) would be invertible at z = ρ and this would imply J 6= 0, thereby contradicting the singular character of the y j (z) at ρ. Assume a contrario λ(ρ) > 1 in order to exclude the other case. Then, by the monotonicity and continuity of λ(z), there would exist ρ < ρ such that λ(ρ) = 1. Let v be a left eigenvector of K (ρ, y1 (ρ), . . . , ym (ρ)) corresponding to the eigenvalue λ(ρ). Perron–Frobenius theory guarantees that such a vector v has all its coefficients that are positive. Then, upon multiplying on the left by v the column vectors corresponding to y and 8(y) (which are equal), one gets an identity; this derived identity, upon expanding near ρ, gives X Bi, j (yi (z) − yi (ρ))(y j (z) − y j (ρ)) + · · · , (74) A(z − ρ) = − i, j

where · · · hides lower order terms and the coefficients A, Bi, j are non-negative with A > 0. There is a contradiction in the orders of growth if each yi is assumed to be analytic at ρ, since the left-hand side of (74) is of exact order (z − ρ) while the righthand side is at least as small as (z − ρ)2 . Thus, we must have λ(ρ) = 1 and λ(x) < 1 for x ∈ (0, ρ). A calculation similar to (74) but with ρ replaced by ρ shows finally that, if yi (z) − yi (ρ) ∼ γi (ρ − z)α , then consistency of asymptotic expansions implies 2α = 1, that is α = 12 . We have thus proved: All the component solutions y j (z) have a square-root singularity at ρ. (The existence of a complete expansion in powers of (ρ − z)1/2 results from a refinement of this argument.) The proof of the general case (70) is thus complete. (d) In the aperiodic case, we first observe that each y j (z) cannot assume an infinite value on its circle of convergence |z| = ρ, since this would contradict the boundedness of |y j (z)| in the open disc |z| < ρ (where y j (ρ) serves as an upper bound). Consequently, by singularity analysis, the Taylor coefficients of any y j (z) are O(n −1−η ) for some η > 1 and the series representing y j at the origin converges on |z| = ρ.

For the rest of the argument, we observe that, if yE = 8(z, yE), then yE = 8hmi (z, yE) where the superscript denotes iteration of the transformation 8 in the variables yE = (y1 , . . . , ym ). By irreducibility, 8hmi is such that each of its component polynomials involves all the variables. Assume a contrario the existence of a singularity ρ ∗ of some y j (z) on |z| = ρ. The triangle inequality yields |y j (ρ ∗ )| ≤ y j (ρ), and the stronger form |y j (ρ ∗ )| < y j (ρ) results from the Daffodil Lemma (p. 267). Then, the modified Jacobian matrix K hmi of 8hmi taken at the y j (ρ ∗ ) has entries dominated strictly by the entries of K hmi taken at the y j (ρ). Therefore, the dominant eigenvalue of K hmi (z, yE j (ρ ∗ )) must be strictly less than 1. This would imply that I − K hmi (z, yE j (ρ ∗ )) is invertible so that

VII. 7. THE GENERAL ANALYSIS OF ALGEBRAIC FUNCTIONS

493

the y j (z) would be analytic at ρ ∗ . A contradiction has been reached: ρ is the sole dominant singularity of each y j and this concludes the argument. Many extensions of the DLW Theorem are possible, as indicated by the notes and references below—the underlying arguments are powerful, versatile, and highly general. Consequences regarding limit distributions, as obtained by Drmota and Lalley, are further explored in Chapter IX (p. 681).

VII.29. Analytic systems. Drmota [172] has shown that the conclusions of the DLW Theorem regarding universality of the square-root singularity hold more generally for 8 j that are analytic functions of Cm+1 to C, provided there exists a positive solution of the characteristic system within the domain of analyticity of the 8 j (see the original article [172] and the note [99] for a discussion of precise conditions). This extension then unifies the DLW theorem and Theorem VII.3 relative to the smooth implicit function schema. VII.30. P´olya systems. Woods [616] has shown that several systems built from P´olya operators of the form MS ETk can also be treated by an extension of the DLW Theorem, which then unifies this theorem and Theorem VII.4. VII.31. Infinite systems. Lalley [398] has extended the conclusions of the DLW Theorem to certain infinite systems of generating function equations. This makes it possible to quantify the return probabilities of certain random walks on infinite free products of finite groups.

The square-root singularity property ceases to be universal when the assumptions of Theorems VII.5 and VII.6, in essence, positivity or irreducibility, fail to be satisfied. For instance, supertrees that are specified by a positive but reducible system have a singularity of the fourth-root type (Example VII.10, p. 412 to be revisited in Example VII.20, p. 503). We discuss next, in Section VII. 7, general methods that apply to any algebraic function and are based on the minimal polynomial equation (rather than a system) satisfied by the function. Note that the results there do not always subsume the present ones, since structure is not preserved when a system is reduced, by elimination, to a single equation. It would at least be desirable to determine directly, from a positive (but reducible) system, the type of singular behaviour of the solution, but the systematic research involved in such a programme is yet to be carried out. VII. 7. The general analysis of algebraic functions Algebraic series and algebraic functions are simply defined as solutions of a polynomial equation or system. Their singularities are strongly constrained to be branch points, with the local expansion at a singularity being a fractional power series known as a Newton–Puiseux expansion (Subsection VII. 7.1). Singularity analysis then turns out to be systematically applicable to algebraic functions, to the effect that their coefficients are asymptotically composed of elements of the form p ∈ Q \ {−1, −2, . . .}, (75) C · ωn n p/q , q see Subsection VII. 7.2. This last form includes as a special case the exponent p/q = −3/2, that was encountered repeatedly, when dealing with inverse functions, implicit functions, and irreducible systems. In this section, we develop the basic structural results that lead to the asymptotic forms (75). However, designing effective methods (i.e., decision procedures) to compute the characteristic constants in (75) is not obvious in the algebraic case. Several algorithms will be described in order to locate and

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analyse singularities (e.g., Newton’s polygon method). In particular, the multivalued character of algebraic functions creates a need to solve what are known as connection problems. Basics. We adopt as the starting point of the present discussion the following definition of an algebraic function or series (see also Note VII.32 for a variant). Definition VII.6. A function f (z) analytic in a neighbourhood V of a point z 0 is said to be algebraic if there exists a (non-zero) polynomial P(z, y) ∈ C[z, y], such that P(z, f (z)) = 0,

(76)

z ∈ V.

A power series f ∈ C[[z]] is said to be an algebraic power series if it coincides with the expansion of an algebraic function at 0. The degree of an algebraic series or function f is by definition the minimal value of deg y P(z, y) over all polynomials that are cancelled by f (so that rational series are algebraic of degree 1). One can always assume P to be irreducible over C (that is P = Q R implies that one of Q or R is a scalar) and of minimal degree. An algebraic function may also be defined by starting with a polynomial system of the form P1 (z, y1 , . . . , ym ) = 0 .. .. .. (77) . . . Pm (z, y1 , . . . , ym ) = 0,

where each P j is a polynomial. A solution of the system (77) is by definition an m– tuple ( f 1 , . . . , f m ) that cancels each P j ; that is, P j (z, f 1 , . . . , f m ) = 0. Any of the f j is called a component solution. A basic but non-trivial result of elimination theory is that any component solution of a non-degenerate polynomial system is an algebraic series (Appendix B.1: Algebraic elimination, p. 739). In other words, one can eliminate the auxiliary variables y2 , . . . , ym and construct a single bivariate polynomial Q such that Q(z, y1 ) = 0. We stress the point that, in the definitions by an equation (76) or a system (77), no positivity of any sort nor irreducibility is assumed. The analysis which is now presented applies to any algebraic function, whether or not it comes from combinatorics.

VII.32. Algebraic definition of algebraic series. It is also customary to define f to be an algebraic series if it satisfies P(z, f ) = 0 in the sense of formal power series, without a priori consideration of convergence issues. Then the technique of majorant series may be used to prove that the coefficients of f grow at most exponentially. Thus, the alternative definition is indeed equivalent to Definition VII.6.

VII.33. “Alg is in Diag of Rat”. Every algebraic function F(z) over C(z) is the diagonal of a rational function G(x, y) = A(x, y)/B(x, y) ∈ C(x, y). Precisely: X X F(z) = G n,n z n , where G(x, y) = G m,n x m y n . n≥0

m,n≥0

This is implied by a theorem of Denef and Lipshitz [154], which is related to the holonomic framework (Appendix B.4: Holonomic functions, p. 748).

VII. 7. THE GENERAL ANALYSIS OF ALGEBRAIC FUNCTIONS

−1

0

495

+1

Figure VII.15. The real section of the lemniscate of Bernoulli defined by P(z, y) = (z 2 + y 2 )2 − (z 2 − y 2 ) = 0: the origin is a double point where two analytic branches meet; there are also two real branch points at z = ±1.

VII.34. Multinomial sums and algebraic coefficients. Let F(z) be an algebraic function. Then Fn = [z n ]F(z) is a (finite) linear combination of “multinomial forms” defined as X n0 + h n Sn (C; h; c1 , . . . , cr ) := c 1 · · · crnr , n 1 , . . . , nr 1 C

where the summation is over all values of n 0 , n 1 , . . . , nr satisfying a collection of linear inequalities C involving n. [Hint: a consequence of Denef–Lipshitz.] Consequently: coefficients of any algebraic function over Q(z) invariably admit combinatorial (i.e., binomial) expressions”. (Eisenstein’s lemma, p. 505, can be used to establish algebraicity over Q(z).) An alternative proof can be based on Note IV.39, p. 270, and Equation (31), p. 753.

VII. 7.1. Singularities of general algebraic functions. Let P(z, y) be an irreducible polynomial of C[z, y], P(z, y) = p0 (z)y d + p1 (z)y d−1 + · · · + pd (z).

The solutions of the polynomial equation P(z, y) = 0 define a locus of points (z, y) in C × C that is known as a complex algebraic curve. Let d be the y-degree of P. Then, for each z there are at most d possible values of y. In fact, there exist d values of y “almost always”, that is except for a finite number of cases. — If z 0 is such that p0 (z 0 ) = 0, then there is a reduction in the degree in y and hence a reduction in the number of finite y-solutions for the particular value of z = z 0 . One can conveniently regard the points that disappear as “points at infinity” (formally, one then operates in the projective plane). — If z 0 is such that P(z 0 , y) has a multiple root, then some of the values of y will coalesce. Define the exceptional set of P as the set (R is the resultant of Appendix B.1: Algebraic elimination, p. 739): (78) 4[P] := {z R(z) = 0}, R(z) := R(P(z, y), ∂ y P(z, y), y).

The quantity R(z) is also known as the discriminant of P(z, y), with y as the main variable and z a parameter. If z 6∈ 4[P], then we have a guarantee that there exist d distinct solutions to P(z, y) = 0, since p0 (z) 6= 0 and ∂ y P(z, y) 6= 0. Then, by the Implicit Function Theorem, each of the solutions y j lifts into a locally analytic function y j (z). A branch of the algebraic curve P(z, y) = 0 is the choice of such a y j (z) together with a simply connected region of the complex plane throughout which this particular y j (z) is analytic.

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VII. APPLICATIONS OF SINGULARITY ANALYSIS

Singularities of an algebraic function can thus only occur if z lies in the exceptional set 4[P]. At a point z 0 such that p0 (z 0 ) = 0, some of the branches escape to infinity, thereby ceasing to be analytic. At a point z 0 where the resultant polynomial R(z) vanishes but p0 (z) 6= 0, then two or more branches collide. This can be either a multiple point (two or more branches happen to assume the same value, but each one exists as an analytic function around z 0 ) or a branch point (some of the branches actually cease to be analytic). An example of an exceptional point that is not a branch point is provided by the classical lemniscate of Bernoulli: at the origin, two branches meet while each one is analytic there (see Figure VII.15). A partial knowledge of the topology of a complex algebraic curve may be obtained by first looking at its restriction to the reals. Consider for instance the polynomial equation P(z, y) = 0, where P(z, y) = y − 1 − zy 2 ,

which defines the OGF of the Catalan numbers. A rendering of the real part of the curve is given in Figure VII.16. The complex aspect of the curve, as given by ℑ(y) as a function of z, is also displayed there. In accordance with earlier observations, there are normally two sheets (branches) above each point. The exceptional set is given by the roots of the discriminant, R = z(1 − 4z), 1 that is, z = 0, 4 . For z = 0, one of the branches escapes at infinity, while for z = 1/4, the two branches meet and there is a branch point: see Figure VII.16. In summary the exceptional set provides a set of possible candidates for the singularities of an algebraic function. Lemma VII.4 (Location of algebraic singularities). Let y(z), analytic at the origin, satisfy a polynomial equation P(z, y) = 0. Then, y(z) can be analytically continued along any simple path emanating from the origin that does not cross any point of the exceptional set defined in (78). Proof. At any z 0 that is not exceptional and for a y0 satisfying P(z 0 , y0 ) = 0, the fact that the discriminant is non-zero implies that P(z 0 , y) has only a simple root at y0 , and we have Py (z 0 , y0 ) 6= 0. By the Implicit Function Theorem, the algebraic function y(z) is analytic in a neighbourhood of z 0 . Nature of singularities. We start the discussion with an exceptional point that is placed at the origin (by a translation z 7→ z + z 0 ) and assume that the equation P(0, y) = 0 has k equal roots y1 , . . . , yk where y = 0 is this common value (by a translation y 7→ y + y0 or an inversion y 7→ 1/y, if points at infinity are considered). Consider a punctured disc |z| < r that does not include any other exceptional point relative to P. In the argument that follows, we let y1 , (z), . . . , yk (z) be analytic determinations of the root that tend to 0 as z → 0. Start at some arbitrary value interior to the real interval (0, r ), where the quantity y1 (z) is locally an analytic function of z. By the implicit function theorem, y1 (z) can be continued analytically along a circuit that starts from z and returns to z while simply encircling the origin (and staying within the punctured disc). Then, by permanence of (1) analytic relations, y1 (z) will be taken into another root, say, y1 (z). By repeating the

VII. 7. THE GENERAL ANALYSIS OF ALGEBRAIC FUNCTIONS

497

10 8 6 y 4 2

–1

–0.8

–0.6

z

–0.4

0

–0.2

0.2

–2 –4 –6 –8

4

–10 2 3 y0

2 1 y0

–2

–1 –2 –3 –0.1

–1

0.15 –0.05

Re(z) 0

0.2 Im(z) 0

1

–1

–0.2 0 –0.8 –0.6 –0.4 Im(z)

0.2

0.4

0.6

0.8

0.25 0.05

1

Re(z)

0.3 0.1

0.35

Figure VII.16. The real section of the Catalan curve (top). The complex Catalan curve with a plot of ℑ(y) as a function of z = (ℜ(z), ℑ(z)) (bottom left); a blow-up of ℑ(y) near the branch point at z = 1/4 (bottom right).

process, we see that, after a certain number of times κ with 1 ≤ κ ≤ k, we will have (0) (κ) obtained a collection of roots y1 (z) = y1 (z), . . . , y1 (z) = y1 (z) that form a set of κ distinct values. Such roots are said to form a cycle. In this case, y1 (t κ ) is an analytic function of t except possibly at 0 where it is continuous and has value 0. Thus, by general principles (regarding removable singularities, see Morera’s Theorem, p. 743), it is in fact analytic at 0. This in turn implies the existence of a convergent expansion near 0: (79)

κ

y1 (t ) =

∞ X

cn t n .

n=1

(The parameter t is known as the local uniformizing parameter, as it reduces a multivalued function to a single-valued one.) This translates back into the world of z: each determination of z 1/κ yields one of the branches of the multivalued analytic function as (80)

y1 (z) =

∞ X n=1

cn z n/κ .

498

VII. APPLICATIONS OF SINGULARITY ANALYSIS

Alternatively, with ω = e2iπ/κ a root of unity, the κ determinations are obtained as ( j) y1 (z)

=

∞ X

cn ωn z n/κ ,

n=1

each being valid in a sector of opening < 2π . (The case κ = 1 corresponds to an analytic branch.) If κ = k, then the cycle accounts for all the roots which tend to 0. Otherwise, we repeat the process with another root and, in this fashion, eventually exhaust all roots. Thus, all the k roots that have value 0 at z = 0 are grouped into cycles of size κ1 , . . . , κℓ . Finally, values of y at infinity are brought to zero by means of the change of variables y = 1/u, then leading to negative exponents in the expansion of y. Theorem VII.7 (Newton–Puiseux expansions at a singularity). Let f (z) be a branch of an algebraic function P(z, f (z)) = 0. In a circular neighbourhood of a singularity ζ slit along a ray emanating from ζ , f (z) admits a fractional series expansion (Puiseux expansion) that is locally convergent and of the form X f (z) = ck (z − ζ )k/κ , k≥k0

for a fixed determination of (z − ζ )1/κ , where k0 ∈ Z and κ is an integer ≥ 1, called the “branching type”13. Newton (1643–1727) discovered the algebraic form of Theorem VII.7 and published it in his famous treatise De Methodis Serierum et Fluxionum (completed in 1671). This method was subsequently developed by Victor Puiseux (1820–1883) so that the name of Puiseux series is customarily attached to fractional series expansions. The argument given above is taken from the neat presentation offered by Hille in [334, Ch. 12, vol. II]. It is known as a “monodromy argument”, meaning that it consists in following the course of values of an analytic function along paths in the complex plane till it returns to its original value. Newton polygon. Newton also described a constructive approach to the determination of branching types near a point (z 0 , y0 ), that, by means of the previous discussion, can always be taken to be (0, 0). In order to introduce the discussion, let us examine the Catalan generating function near z 0 = 1/4. Elementary algebra gives the explicit form of the two branches √ √ 1 1 1 − 1 − 4z , y2 (z) = 1 + 1 − 4z , y1 (z) = 2z 2z

whose forms are consistent with what Theorem VII.7 predicts. If however one starts directly with the equation, P(z, y) ≡ y − 1 − zy 2 = 0

13 From the general discussion, if k < 0, then κ = 1 is possible (case f (ζ ) = ∞, with a polar 0 singularity); if k0 ≥ 0, then a singularity only exists if κ ≥ 2 (case of a branch point with | f (ζ )| < ∞).

VII. 7. THE GENERAL ANALYSIS OF ALGEBRAIC FUNCTIONS

499

then, the translation z = 1/4 − Z (the minus sign is a mere notational convenience), y = 2 + Y yields

1 Q(Z , Y ) ≡ − Y 2 + 4Z + 4Z Y + Z Y 2 . 4 Look for solutions of the form Y = cZ α (1 + o(1)) with c 6= 0, whose existence is a priori granted by Theorem VII.7 (Newton–Puiseux). Each of the monomials in (81) gives rise to a term of a well-determined asymptotic order, respectively, Z 2α , Z 1 , Z α+1 , Z 2α+1 . If the equation is to be identically satisfied, then the main asymptotic order of Q(Z , Y ) should be 0. Since c 6= 0, this can only happen if two or more of the exponents in the sequence (2α, 1, α + 1, 2α + 1) coincide and the coefficients of the corresponding monomial in P(Z , Y ) is zero, a condition that is an algebraic constraint on the constant c. Furthermore, exponents of all the remaining monomials have to be larger since by assumption they represent terms of lower asymptotic order. Examination of all the possible combinations of exponents leads one to discover that the only possible combination arises from the cancellation of the first two terms of Q, namely − 41 Y 2 + 4Z , which corresponds to the set of constraints (81)

1 − c2 + 4 = 0, 4 with the supplementary conditions α + 1 > 1 and 2α + 1 > 1 being satisfied by this choice α = 1/2. We have thus discovered that Q(Z , Y ) = 0 is consistent asymptotically with Y ∼ 4Z 1/2 , Y ∼ −4Z 1/2 . 2α = 1,

The process can be iterated upon subtracting dominant terms. It invariably gives rise to complete formal asymptotic expansions that satisfy Q(Z , Y ) = 0 (in the Catalan example, these are series in ±Z 1/2 ). Furthermore, elementary majorizations establish that such formal asymptotic solutions represent indeed convergent series. Thus, local expansions of branches have indeed been determined. An algorithmic refinement (also due to Newton) is known as the method of Newton polygons. Consider a general polynomial X Q(Z , Y ) = Zaj Y bj , j∈J

and associate to it the finite set of points (a j , b j ) in N × N, which is called the Newton diagram. It is easily verified that the only asymptotic solutions of the form Y ∝ Z τ correspond to values of τ that are inverse slopes (i.e., 1x/1y) of lines connecting two or more points of the Newton diagram (this expresses the cancellation condition between two monomials of Q) and such that all other points of the diagram are on this line or to the right of it (as the other monomials must be of smaller order). In other words: Newton’s polygon method. Any possible exponent τ such that Y ∼ cZ τ is a solution to a polynomial equation corresponds to one of the inverse slopes of the left-most convex envelope of the Newton diagram. For each viable τ , a polynomial equation constrains the possible values of the corresponding

500

VII. APPLICATIONS OF SINGULARITY ANALYSIS

0.4 y 0.2

–0.4

–0.2

0

–0.2

1 0 0111 1 0 1 0 1 0 0001 0 1 0 1 0 1 0 1 0 0 0001 0 0 1 4 1 0111 0 1 1 0 1 1 0 1 0 1 0 1 000 111 1 0 0111 1 0 1 1 0 1 0 000 0 1 0 1 0 1 0 1 0 1 0 1 000 111 0 1 0 1 3 1 0111 0 1 0 1 0 1 000 0 1 0 1 1 0 11 00 000000000 111111111 0 1 0 1 0 1 0 1 0 1 0 1 000000000 111111111 0 1 0 1 0 1 0 1 00 11 00 11 0 1 0 1 000000000 111111111 2 1 0 1 0 1 0 1 0 1 00 11 00 11 0 0 1 000000000 111111111 0 1 1 0 11 1 0 1 0 1 0 0 1 000000000 111111111 00 0 1 0 1 0 1 0 1 0 0 1 1 1 0000000 1111111 000000000 111111111 001 11 0 1 0 1 0 0 0 1 0 1 0000000 1111111 0 1 0 1111111 0 1 0 1 1 1 1 1 0 0 1 0000000 0 1 1 0 1 1 0 0 0 1 0 0 1 0000000 1111111 0 0 1 0 1 0 1 05 1 1 0 1 0 1 0 1 2 3 4 6

5

0.2 z

0.4

–0.4

Figure VII.17. The real algebraic curve defined by the equation P = (y − z 2 )(y 2 − z)(y 2 − z 3 ) − z 3 y 3 near (0, 0) (left) and the corresponding Newton diagram (right).

coefficient c. Complete expansions are obtained by repeating the process, which means deflating Y from its main term by way of the substitution Y 7→ Y − cZ τ .

Figure VII.17 illustrates what goes on in the case of the curve P = 0 where P(z, y)

= (y − z 2 )(y 2 − z)(y 2 − z 3 ) − z 3 y 3 = y5 − y3 z − y4 z2 + y2 z3 − 2 z3 y3 + z4 y + z5 y2 − z6,

considered near the origin. As the factored part suggests, the curve is expected to resemble (locally) the union of two orthogonal parabolas and of a curve y = ±z 3/2 having a cusp, i.e., the union of √ y = z 2 , y = ± z, y = ±z 3/2 , respectively. It is visible on the Newton diagram that the possible exponents y ∝ z τ at the origin are the inverse slopes of the segments composing the envelope, that is, 3 1 τ = 2, τ = , τ = . 2 2 For computational purposes, once determined the branching type κ, the value of k0 that dictates where the expansion starts, and the first coefficient, the full expansion can be recovered by deflating the function from its first term and repeating the Newton diagram construction. In fact, after a few initial stages of iteration, the method of indeterminate coefficients can always be eventually applied [Bruno Salvy, private communication, August 2000]. Computer algebra systems usually have this routine included as one of the standard packages; see [531]. VII. 7.2. Asymptotic form of coefficients. The Newton–Puiseux theorem describes precisely the local singular structure of an algebraic function. The expansions are valid around a singularity and, in particular, they hold in indented discs of the type required in order to apply the formal translation mechanisms of singularity analysis.

VII. 7. THE GENERAL ANALYSIS OF ALGEBRAIC FUNCTIONS

501

P Theorem VII.8 (Algebraic asymptotics). Let f (z) = n f n z n be the branch of an algebraic function that is analytic at 0. Assume that f (z) has a unique dominant singularity at z = α1 on its circle of convergence. Then, in the non-polar case, the coefficient f n satisfies the asymptotic expansion, X (82) f n ∼ α1−n dk n −1−k/κ , k≥k0

where k0 ∈ Z and κ is an integer ≥ 2. In the polar case, κ = 1 and k0 < 0, the estimate (82) is to be interpreted as a terminating (exponential–polynomial) form. If f (z) has several dominant singularities |α1 | = |α2 | = · · · = |αr |, then there exists an asymptotic decomposition (where ǫ is some small fixed number, ǫ > 0) fn =

(83) where each

φ ( j) (n)

r X j=1

φ ( j) (n) + O((|α1 | + ǫ))−n ,

admits a complete asymptotic expansion, X ( j) φ ( j) (n) ∼ α −n dk n −1−k/κ j , j ( j)

k≥k0

( j)

with either k0 in Z and κ j an integer ≥ 2 or κ j = 1 and k0 < 0. Proof. An early version of this theorem appeared as [220, Th. D, p. 293]. The expansions granted by Theorem VII.7 are of the exact type required by singularity analysis (Theorem VI.4, p. 393). For multiple singularities, Theorem VI.5 (p. 398) based on composite contours is to be used: in that case each φ ( j) (n) is the contribution obtained by transfer of the corresponding local singular element. In the case of multiple singularities, partial cancellations may occur in some of the dominant terms of (83): consider for instance the case of 1 q = 1 + 0.60z + 0.04z 2 − 0.36z 3 − 0.408z 4 − · · · , 6 2 1 − 5z + z where the function has two complex conjugate singularities with an argument not commensurate to π , and refer to the corresponding discussion of rational coefficients asymptotics (Subsection IV. 6.1, p. 263). Fortunately, such delicate arithmetic situations tend not to arise in combinatorial situations. Example VII.18. Branches of unary–binary trees. The generating function of unary–binary trees (Motzkin numbers, pp. 68 and 396) is f (z) defined by P(z, f (z)) = 0 where P(z, y) = y − z − zy − zy 2 ,

so that

p √ 1 − z − (1 + z)(1 − 3z) 1 − 2z − 3z 2 = . 2z 2z There exist only two branches: f and its conjugate f that form a 2–cycle at z = 1/3. The singularities of all branches are at 0, −1, 1/3 as is apparent from the explicit form of f or from f (z) =

1−z−

502

VII. APPLICATIONS OF SINGULARITY ANALYSIS

1.2 1.1 1 0.9 y 0.8 0.7 0.6

–0.4

–0.2

0.5 0

0.2 z

Figure VII.18. The real algebraic curve corresponding to non-crossing forests.

the defining equation. The branch representing f (z) at the origin is analytic there (by a general argument or by the combinatorial origin of the problem). Thus, the dominant singularity of f (z) is at 1/3 and it is unique in its modulus class. The “easy” case of Theorem VII.8 then applies once f (z) has been expanded near 1/3. As a rule, the organization of computations is simpler if one makes use of the local uniformizing parameter with a choice of sign in accordance to the direction along which the singularity is approached. In this case, we set z = 1/3 − δ 2 and find 1/2 9 63 27 2997 5 1 f (z) = 1 − 3 δ + δ 2 − δ 3 + δ 4 − δ + ··· , δ = −z . 2 8 2 128 3 This translates immediately into 8085 15 505 3n+1/2 − + · · · , 1− + f n ≡ [z n ] f (z) ∼ √ 16n 512n 2 8192n 3 2 π n3 which agrees with the direct derivation of Example VI.3, p. 396. . . . . . . . . . . . . . . . . . . . . . . . .

VII.35. Meta-asymptotics. Estimate the growth of the coefficients in the asymptotic expansions of Catalan and Motzkin (unary–binary trees) numbers. Example VII.19. Branches of non-crossing forests. Consider the polynomial equation P(z, y) = 0, where P(z, y) = y 3 + (z 2 − z − 3)y 2 + (z + 3)y − 1, (see Figure VII.18 for the real branches) and the combinatorial GF satisfying P(z, F) = 0 determined by the initial conditions, F(z) = 1 + 2z + 7z 2 + 33z 3 + 181z 4 + 1083z 5 + · · · . (EIS A054727). F(z) is the OGF of non-crossing forests defined in Example VII.16, p. 485. The exceptional set is mechanically computed: its elements are roots of the discriminant R = −z 3 (5z 3 − 8z 2 − 32z + 4). Newton’s algorithm shows that two of the branches at 0, say y0 and y2 , form a cycle of length 2 √ √ with y0 = 1− z+O(z), y2 = 1+ z+O(z) while it is the “middle branch” y1 = 1+z+O(z 2 ) that corresponds to the combinatorial GF F(z).

VII. 7. THE GENERAL ANALYSIS OF ALGEBRAIC FUNCTIONS

503

The non-zero exceptional points are the roots of the cubic factor of R; namely . = {−1.93028, 0.12158, 3.40869}. . Let ξ = 0.1258 be the root in (0, 1). By Pringsheim’s theorem and the fact that the OGF of an infinite combinatorial class must have a positive dominant singularity in [0, 1], the only possibility for the dominant singularity of y1 (z) is ξ . For z near ξ , the three branches of the cubic give rise to one branch that is analytic with value approximately 0.67816 and a cycle of two conjugate branches with value near 1.21429 at z = ξ . The expansion of the two conjugate branches is of the singular type, p α ± β 1 − z/ξ ,

where

q 43 18 35 1 . . 228 − 981ξ − 5290ξ 2 = 0.14931. + ξ − ξ 2 = 1.21429, β = 37 37 74 37 The determination with a minus sign must be adopted for representing the combinatorial GF when z → ξ − since otherwise one would get negative asymptotic estimates for the non-negative coefficients. Alternatively, one may examine the way the three real branches along (0, ξ ) match with one another at 0 and at ξ − , then conclude accordingly. Collecting partial results, we finally get by singularity analysis the estimate β 1 1 . Fn = √ ωn 1 + O( ) , ω = = 8.22469 3 n ξ 2 πn with the cubic algebraic number ξ and the sextic β as above. . . . . . . . . . . . . . . . . . . . . . . . . . . . . α=

The example above illustrates several important points in the analysis of coefficients of algebraic functions when there are no simple explicit radical forms. First, a given combinatorial problem determines a unique branch of an algebraic curve at the origin. Next, the dominant singularity has to be identified by “connecting” the combinatorial branch with the branches at every possible singularity of the curve. Finally, computations tend to take place over algebraic numbers and not simply rational numbers. So far, examples have illustrated the common situation where the function’s exponent at its dominant singularity is 1/2. Our last example shows a case where the exponent assumes a different value, namely 1/4. Example VII.20. Branches of supertrees. Consider the quartic equation y 4 − 2 y 3 + (1 + 2 z) y 2 − 2 yz + 4 z 3 = 0 and let K be the branch analytic at 0 determined by the initial conditions: K (z) = 2 z 2 + 2 z 3 + 8 z 4 + 18 z 5 + +64 z 6 + 188 z 7 + · · · . The OGF K corresponds to bicoloured supertrees of Example VI.10, p. 412; a partial graph is represented in Figure VII.19. The discriminant is found to be R = 16 z 4 16 z 2 + 4 z − 1 (−1 + 4 z)3 , √ with roots at 1/4 and (−1 ± 5)/8. The dominant singularity of the branch of combinatorial interest turns out to be at z = 41 where K (1/4) = 1/2. The translation z = 1/4+Z , y = 1/2+Y

504

VII. APPLICATIONS OF SINGULARITY ANALYSIS 2

1.5

1

k

0.5

–0.6

–0.4

–0.2

0.2

z –0.5

–1

Figure VII.19. The real algebraic curve associated with the generating function of supertrees of type K .

then transforms the basic equation into 4 Y 4 + 8 Z Y 2 + 16 Z 3 + 12 Z 2 + Z = 0. According to Newton’s polygon method, the main cancellation arises from 4Y 4 + Z = 0: this corresponds to a segment of inverse slope 1/4 in the Newton diagram and accordingly to a cycle formed with four conjugate branches, i.e., a fourth-root singularity. Thus, one has 1/4 3/4 1 1 1 4n 1 K (z) ∼ 1/2 − √ , −z −z −√ + · · · , [z n ]K (z) ∼ n→∞ 8Ŵ( 3 )n 5/4 2 4 2 4 z→ 41 4 which is consistent with values found earlier (p. 412). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Computable coefficient asymptotics. The previous discussion contains the germ of a complete algorithm for deriving an asymptotic expansion of coefficients of any algebraic function. We sketch in Note VII.36 the main principles, while leaving some of the details to the reader. Observe that the problem is a connection problem: the “shapes” of the various sheets around each point (including the exceptional points) are known, but it remains to connect them together and see which ones are encountered first when starting with a given branch at the origin.

VII.36. Algebraic Coefficient Asymptotics (ACA). Here is an outline of the algorithm. Algorithm ACA: Input: A polynomial P(z, y) with d = deg y P(z, y); a series Y (z) such that P(z, Y ) = 0 and assumed to be specified by sufficiently many initial terms so as to be distinguished from all other branches. Output: The asymptotic expansion of [z n ]Y (z) whose existence is granted by Theorem VII.8. The algorithm consists of three main steps: Preparation (I), Dominant singularities (II), and Translation (III). I. Preparation: Define the discriminant R(z) = R(P, Py′ , y).

VII. 7. THE GENERAL ANALYSIS OF ALGEBRAIC FUNCTIONS

505

(P1 ) Compute the exceptional set 4 = {z R(z) = 0} and the points of infinity 40 = {z p0 (z) = 0}, where p0 (z) is the leading coefficient of P(z, y) considered as a function of y. (P2 ) Determine the Puiseux expansions of all the d branches at each of the points of 4 ∪ {0} (by Newton diagrams and/or indeterminate coefficients). This includes the expansion of analytic branches as well. Let {yα, j (z)}dj=1 be the collection of all such expansions at some α ∈ 4 ∪ {0}. (P3 ) Identify the branch at 0 that corresponds to Y (z). II. Dominant singularities: (Controlled approximate matching of branches). Let 41 , 42 , . . . be a partition of the elements of 4 ∪ {0} sorted according to the increasing values of their modulus: it is assumed that the numbering is such that if α ∈ 4i and β ∈ 4 j , then |α| < |β| is equivalent to i < j. Geometrically, the elements of 4 have been grouped in concentric circles. First, a preparation step is needed. (D1 ) Determine a non-zero lower bound δ on the radius of convergence of any local Puiseux expansion of any branch at any point of 4. Such a bound can be constructed from the minimal distance between elements of 4 and from the degree d of the equation. The sets 4 j are to be examined in sequence until it is detected that one of them contains a singularity. At step j, let σ1 , σ2 , . . . , σs be an arbitrary listing of the elements of 4 j . The problem is to determine whether any σk is a singularity and, in that event, to find the right branch to which it is associated. This part of the algorithm proceeds by controlled numerical approximations of branches and constructive bounds on the minimum separation distance between distinct branches. (D2 ) For each candidate singularity σk , with k ≥ 2, set ζk = σk (1 − δ/2). By assumption, each ζk is in the domain of convergence of Y (z) and of any yσk , j . (D3 ) Compute a non-zero lower bound ηk on the minimum distance between two roots of P(ζk , y) = 0. This separation bound can be obtained from resultant computations. (D4 ) Estimate Y (ζk ) and each yσk , j (ζk ) to an accuracy better than ηk /4. If two elements, Y (z) and yσk , j (z) are (numerically) found to be at a distance less than ηk for z = ζk , then they are matched: σk is a singularity and the corresponding yσk , j is the corresponding singular element. Otherwise, σk is declared to be a regular point for Y (z) and discarded as candidate singularity. The main loop on j is repeated until a singularity has been detected, when j = j0 , say. The radius of convergence ρ is then equal to the common modulus of elements of 4 j0 ; the corresponding singular elements are retained. III. Coefficient expansion: Collect the singular elements at all the points σ determined to be a dominant singularity at Phase II. Translate termwise using the singularity analysis rule, Ŵ(− p/κ + n) (σ − z) p/κ 7→ σ p/κ−n , Ŵ(− p/κ)Ŵ(n + 1) and reorganize into descending powers of n, if needed.

This algorithm vindicates the following assertion (see also Chabaud’s thesis [110]). Proposition VII.8 (Decidability of algebraic connections.). The dominant singularities of a branch of an algebraic function can be determined in a finite number of operations by the algorithm ACA of Note VII.36.

VII.37. Eisenstein’s lemma. Let y(z) be an algebraic function with rational coefficients (for instance a combinatorial generating function) satisfying 8(z, y(z)) = 0, where the coefficient of the polynomial 8 are in C; then there exists a polynomial 9 with integer coefficients such that 9(z, y(z)) = 0. (Hint [65]. Consider the case where the coefficients of 8 are Q–linear combinations of 1 and an irrational α, and write 8(z, y) = 81 (z, y) + α8α (z, y), where 81 , 8α ∈ Q[z, y]; extracting [z n ]8(z, y(z)) would produce a Q–linear relation between 1

506

VII. APPLICATIONS OF SINGULARITY ANALYSIS

and α, unless one of 81 , 8α is trivial, which must then be the case.) Thus, one can get 9(z, y) in Q[z, y], and by clearing denominators, in Z[z, y]. As a consequence, for algebraic y(z) with rational coefficients, there exists an integer B such that for all n, one has B n [z n ]y(z) ∈ Z. Since P P there are infinitely many primes, the functions e z , log(1 + z), z n /n 2 , z n /(n!)3 , and so on, are transcendental (i.e., not algebraic).

VII.38. Powers of binomial coefficients. Define Sr (z) :=

2n r n n≥0 n z , with r ∈ Z>0 . For

P

even r = 2ν the function S2ν (z) is transcendental (not algebraic) since its singular expansion involves a logarithmic term. For odd r = 2ν + 1 and r ≥ 3, the function S2ν+1 (z) is also transcendental as a consequence of the arithmetic transcendence of the number π ; see [220]. These functions intervene in P´olya’s drunkard problem (p. 425). In contrast with the “hard” theory of arithmetic transcendence, it is usually “easy” to establish transcendence of functions, by exhibiting a local expansion that contradicts the Newton–Puiseux Theorem (p. 498).

VII. 8. Combinatorial applications of algebraic functions In this section, we introduce objects whose construction leads to algebraic functions, in a way that extends the basic symbolic method. This includes: walks with a finite number of allowed jumps (Subsection VII. 8.1) and planar maps (Subsection VII. 8.2). In such cases, bivariate functional equations reflect the combinatorial decompositions of objects. The common form of these functional equations is (84)

8(z, u, F(z, u), h 1 (z), . . . , h r (z)) = 0,

where 8 is a known polynomial and the unknown functions are F and h 1 , . . . , h r . Specific methods are needed in order to attain solutions to such functional equations that would seem at first glance to be grossly underdetermined. Walks and excursions lead to a linear version of (84) that is treated by the so-called kernel method. Maps lead to nonlinear versions that are solved by means of Tutte’s quadratic method. In both cases, the strategy consists in binding z and u by forcing them to lie on an algebraic curve (suitably chosen in order to eliminate the dependency on F(z, u)), and then pulling out consequences of such a specialization. Asymptotic estimates can then be developed from such algebraic solutions, thanks to the general methods expounded in the previous section. VII. 8.1. Walks and the kernel method. Start with a set that is a finite subset of Z and is called the set of jumps. A walk (relative to ) is a sequence w = (w0 , w1 , . . . , wn ) such that w0 = 0 and wi+1 − wi ∈ , for all i, 0 ≤ i < n. A non-negative walk (also known as a “meander”) satisfies wi ≥ 0 and an excursion is a non-negative walk such that, additionally, wn = 0. A bridge is a walk such that wn = 0. The quantity n is called the length of the walk or the excursion. For instance, Dyck paths and Motzkin paths analysed in Section V. 4, p. 318, are excursions that correspond to = {−1, +1} and = {−1, 0, +1}, respectively. (Walks and excursions are also somewhat related to paths in graphs in the sense of Section V. 5, p. 336.) We let −c denote the smallest (negative) value of a jump, and d denote the largest (positive) jump. A fundamental rˆole is played in this discussion by the characteristic

VII. 8. APPLICATIONS OF ALGEBRAIC FUNCTIONS

507

polynomial14 of the walk, S(y) :=

X

ω∈

yω =

d X

Sj y j,

j=−c

which is a Laurent polynomial; that is, it involves negative powers of the variable y. . Walks. Observe first the rational character of the BGF of walks, with z marking length and u marking final altitude: (85)

W (z, u) =

1 . 1 − zS(u)

Since walks may terminate at a negative altitude, this is a Laurent series in u. Bridges. The GF of bridges is formally [u 0 ]W (z, u), since bridges correspond to walks that end at altitude 0. Thus one has Z 1 1 du (86) B(z) = , 2iπ γ 1 − zS(u) u upon integrating along a circle γ that separates the small and large branches, as discussed below. The integral can then be evaluated by residues: details are found in [27]; the net result is Equation (97), p. 511. Excursions and meanders. We propose next to determine the number Fn of excursions of length n and type , via the corresponding OGF F(z) =

∞ X

Fn z n .

n=0

In fact, we shall determine the more general BGF X F(z, u) := Fn,k u k z n , n,k

where Fn,k is the number of non-negative walks (meanders) of length n and final altitude k (i.e., the value of wn in the definition of a walk is constrained to equal k). In particular, one has F(z) = F(z, 0). The main result of this subsection can be stated informally as follows (see Propositions VII.9, p. 510 and VII.10, p. 513 for precise versions): For each finite set ∈ Z, the generating function of excursions is an algebraic function that is explicitly computable from . The number of excursions of length n satisfies asymptotically a universal law of the form C An n −3/2 . 14 If is a set, then the coefficients of S lie in {0, 1}. The treatment presented here applies in all generality to cases where the coefficients are arbitrary positive real numbers. This accounts for probabilistic situations as well as multisets of jump values.

508

VII. APPLICATIONS OF SINGULARITY ANALYSIS

There are many ways to view this result. The problem is usually treated within probability theory by means of Wiener–Hopf factorizations [515], and Lalley [396] offers an insightful analytic treatment from this angle. On another level, Labelle and Yeh [392] show that an unambiguous context-free specification of excursions can be systematically constructed, a fact that is sufficient to ensure the algebraicity of the GF F(z). (Their approach is implicitly based on the construction of a pushdown automaton itself equivalent, by general principles, to a context-free grammar.) The Labelle–Yeh construction reduces the problem to a large, but somewhat “blind”, combinatorial preprocessing. Accordingly, for analysts, it has the disadvantage of not extracting a simpler analytic (but non-combinatorial) structure inherent in the problem: the shape of the end result can indeed be predicted by the Drmota–Lalley–Woods Theorem, but the nature of the constants involved is not clearly accessible in this way. The kernel method. The method described below is often known as the kernel method. It takes some of its inspiration from exercises in the 1968 edition of Knuth’s book [377] (Ex. 2.2.1.4 and 2.2.1.11), where a new approach was proposed to the enumeration of Catalan and Schr¨oder objects. The technique has since been extended and systematized by several authors; see for instance [26, 27, 86, 202, 203] for relevant combinatorial works. Our presentation below follows that of Lalley [396] and of Banderier and Flajolet [27]. The polynomial f n (u) = [z n ]F(z, u) is the generating function of non-negative walks of length n, with u recording final altitude. A simple recurrence relates f n+1 (u) to f n (u), namely, f n+1 (u) = S(u) · f n (u) − rn (u),

(87)

where rn (u) is a Laurent polynomial consisting of the sum of all the monomials of S(u) f n (u) that involve negative powers15 of u: (88)

rn (u) :=

−1 X

j=−c

u j ([u j ] S(u) f n (u)) = {u 1. Second, it may be the case that a parameter is accessible via a collection of univariate GFs rather than a BGF (see typically our discussion of extremal parameters in Section III. 8, p. 214). We briefly indicate in this section ways to deal with such situations. VII. 10.1. Moment pumping. Our reader should have no difficulty in recognizing as familiar at least the first two steps of the following procedure, nicknamed “moment pumping” in [249], which serve to extract moments from bivariate generating functions. Procedure: Moment Pumping Input: A bivariate generating function F(z, u) determined by a functional equation. Output: The limit law corresponding to the array of coefficients [z n u k ]F(z, u); that is, the asymptotic probability distribution of a parameter χ on a class Fn . Step 1. Elucidate the singular structure of F(z, 1) corresponding to the counting problem [z n ]F(z, 1). (Tools of Chapters IV–VII are well-suited for this task, the functional equation satisfied by F(z, 1) being usually simpler than that of F(z, u).) Step 2. Work out the singular structure (main terms) of each of the partial derivatives ∂r F(z, u) µr (z) := r ∂u u=1

VII. 10. SINGULARITY ANALYSIS AND PROBABILITY DISTRIBUTIONS

533

for r = 1, 2, . . ., and use meromorphic methods or singularity analysis to conclude as to [z n ]µr (z). If, as it is most often the case, the combinatorial parameter marked by u is of polynomial growth in the size n, then the radius of convergence of each µr is a priori the same as that of F(z, 1). Furthermore, in many cases, the singular structure of the µr (z) is of the same broad type as that of µ0 (z) ≡ F(z, 1). Step 3. From the moments, as given by Step 2, attempt to reconstruct the limit distribution using the Moment Convergence Theorem (Theorem C.2, p. 778).

In order for the procedure to succeed22, we typically need the standard deviation of χ to be of the same order as the mean, which necessitates that the distribution is spread in the sense of Chapter III, p. 161. (Otherwise, there are larger and larger cancellations in moments of the centred and scaled variant of χ , so that the analysis requires an unbounded number of terms in the singular expansions of the GFs µr (z); see also Pittel’s study [484] for an insightful discussion of related problems.) Example VII.26. The area under Dyck excursions. We now examine the coefficients in the BGF, which is a solution of the functional equation 1 (143) F(z, q) = , i.e., F(z, q) = 1 + z F(z, q)F(qz, q). 1 − z F(qz, q)

It is such that [z n q k ]F(z, q) represents the number of Dyck excursions of length 2n and area k− n (p. 330). Thus we are aiming at characterizing the distribution of area in Dyck paths. We set , which is, up to normalization, the GF of the r th factorial moments. µr (z) := ∂qr F(z, q) q=1 1 1 − √1 − 4z , as anticipated. Clearly, µ0 satisfies the relation µ0 = 1 + zµ20 , and µ0 = 2z Application of the moment pumping procedure leads to a collection of equations, µ1 µ2

= =

2zµ0 µ1 + z 2 µ0 µ′0 2zµ0 µ2 + 2zµ21 + 2z 2 µ1 µ′0 + 2z 2 µ0 µ′1 + z 3 µ0 µ′′0 ,

and so on. Precisely, the shape of the equation giving µr , for r ≥ 1, is j r X X j k k r z ∂z µ j−k , µr − j (144) µr = z k j j=0

k=0

as results, upon setting q = 1, from Leibniz’s product rule and a computation of the derivatives j ∂q F(qz, q). In particular, each µr can be expressed from the previous µ and their derivatives, since the equation relative √ to µr is of the linear form µr = 2zµ0 µr + · · · , so that µr (z) is a rational form in z and δ := 1 − 4z. An examination of the initial values of the µ then suggests that, in terms of dominant singular asymptotics, as z → 14 , there holds Kr (145) µr (z) = + O (1 − 4z)−(3r −2)/2 , r ≥ 1, (3r −1)/2 (1 − 4z) a property that is readily verified by induction. (In such situations, the closure of functions of singularity analysis class under differentiation, p. 419, proves handy.) In particular, by singularity analysis, the mean and standard deviation of χ on Fn are each of order n 3/2 . Now, equipped with (145), we can trace back the main singular contributions in (144), noting that the “weight”, as measured by the exponent of (1 − 4z)−1 , of the term in (144) 22The important Gaussian case, which is mostly excluded by moment pumping, tends to yield agreeably to the perturbation methods of Chapter IX, so that the univariate methods discussed here and those of Chapter IX are indeed complementary.

534

VII. APPLICATIONS OF SINGULARITY ANALYSIS

corresponding to generic indices j, k is (3r − k − 2)/2. Then, by identifying the corresponding coefficients, we come up with the recurrence valid for r ≥ 2 (146)

3r =

r −1 1X r r (3r − 1) 3r −1 3r − j 3 j + 4 4 j j=1

(the linear term arises from j = r, k = 1) and from (145) and (146), the shape of factorial moments, hence that of the usual power moments, results by plain singularity analysis: √ π3r (147) En χ r ∼ Mr n 3r/2 , Mr := . Ŵ((3r − 1)/2)

It can then be verified [568] that the moment Mr uniquely characterize a probability distribution (Appendix C.5: Convergence in law, p. 776). Proposition VII.15. The distribution of area χ in Dyck excursions, scaled by n −3/2 , converges to a limit, known as the Airy23 distribution of the area type, which is determined by its moments Mr , as specified by (146) and (147). In other terms, there exists a distribution function H (x) supported by R>0 such that limn→∞ Pn (χ < xn 3/2 ) = H (x).

Due to the exact correspondence between Dyck excursions and trees, the same limit distribution occurs for path length in general Catalan trees. Proposition VII.15 is originally due to Louchard [415, 416], who developed connections with Brownian motion—the limit distribution is indeed up to normalization that of Brownian excursion area. (The approach presented here also has the merit of providing finite n corrections.) Our moment pumping approach largely follows the lines of Tak´acs’ treatment [568]. The recurrence relation (144) can furthermore be solved by generating functions, to the effect that the 3r entertain intimate relations with the Airy function: for surveys, see [244, 352]. Curiously, the Wright constants arising in the enumeration of labelled graphs of fixed excess (the Pk (1) of p. 134) appear to be closely related to the moments Mr : this fact can be explained combinatorially by means of breadth-first search of graphs, as noted by Spencer [548]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

VII.53. Path length in simple varieties of trees. Under the usual conditions on φ, the limit distribution is an Airy distribution of the area type, as shown by Tak´acs [566]. VII.54. A parking problem II. This continues Example II.19, p. 146. Consider m cars and condition by the fact that everybody eventually finds a parking space and the last space remains empty. Define total displacement as the sum of the distances (over all cars) between the initially intended parking location and the first available space. The analysis reduces to the differencedifferential equation [249, 380], which generalizes (65), p. 146, F(z, q) − q F(qz, q) ∂ F(z, q) = F(z, q) · . ∂z 1−q

Moment pumping is applicable [249]: the limit distribution is once more an Airy (of area type). This problem arises in the analysis of the linear probing hashing algorithm [380, §6.4] and is of relevance as a discrete version of important coalescence models. It is also shown in [249] based on [285] that the number of inversions in a Cayley tree is asymptotically Airy. 23 The Airy function Ai(z) is of hypergeometric type and is closely related to Bessel functions of order ±1/3. It is defined as the solution of y ′′ − zy = 0 satisfying Ai(0) = 3−2/3 / Ŵ(2/3) and Ai′ (0) = −3−1/3 / Ŵ(1/3); see [3, 604] for basic properties. The 3r intervene in the expansion of log Ai(z) at infinity [244, 352]. After Louchard and Tak´acs, the distribution function H (x) can be expressed in terms of confluent hypergeometric functions and zeros of the Airy function.

VII. 10. SINGULARITY ANALYSIS AND PROBABILITY DISTRIBUTIONS

535

VII.55. The Wiener index and other functionals of trees. The Wiener index, a structural index of interest to chemists, is defined as the sum of the distances between all pairs of nodes in a tree. For simple families, as shown by Janson [348], it admits a limit distribution. (Similar properties hold for many additive functionals of combinatorial tree families [210]. As regards moment pumping, the methods are also related to those of Subsection VI. 10.3, p. 427, dedicated to tree recurrences.)

VII.56. Difference equations, polyominoes, and limit laws. Many of the q–difference equations that are defined by a polynomial relation between F(z, q), F(qz, q), . . . (and even systems) may be analysed, as shown by Richard [509, 510]. This covers several models of polyominoes, including the staircase, the horizontally-vertically convex, and the column convex ones. Area (for fixed perimeter) is asymptotically Airy distributed. It is from these and similar results, supplemented by extensive computations based on transfer-matrix methods, that Guttmann and the Melbourne school have been led to conjecturing that the limit area of self-avoiding polygons (closed walks) in the plane is Airy (see our comments on p. 365). VII.57. Path length in increasing trees. For binary increasing trees, the analysis of path length reduces to that of the functional equation, Z z F(z, q) = 1 + F(qt, q)2 dt. 0

There exists a limit law, as first shown by Hennequin [328] using moment pumping, with alternative approaches due to R´egnier [505] and R¨osler [517]. This law is important in computer science, since it describes the number of comparisons used by the Quicksort algorithm and involved in the construction of a binary search tree. The mean is 2n log n + O(n), the variance is ∼ (7 − 4ζ (2))n 2 , and the moment of order r of the limit law is a polynomial form in zeta values ζ (2), . . . , ζ (r ). See [209] for recent news and references.

VII. 10.2. Families of generating functions. There is no logical obstacle to applying singularity analysis to a whole family of functions. In a way, this is similar to what was done in Chapter V when analysing longest runs in words (p. 308) and the height of general Catalan trees (p. 326), in the simpler case of meromorphic coefficient asymptotics. One then needs to develop suitable singular expansions together with companion error terms, a task that may be technically demanding when GFs are given by nonlinear functional relations or recurrences. We illustrate below the situation by an aperc¸u of the analysis of height in simple varieties of trees. Example VII.27. Height in simple varieties of trees. The recurrence (148)

y0 (z) = 0,

yh+1 (z) = 1 + zyh (z)2

is such that yh (z) is the OGF of binary trees of height less than h, with size measured by the number of binary nodes (Example III.28, p. 216). Each yh (z) is a polynomial, with deg(yh ) = 2h−1 − 1. Some technical difficulties are to be expected since the yh have no singularity at a finite distance, whereas their formal limit y(z) is the OGF of Catalan number, √ 1 y(z) = 1 − 1 − 4z , 2z which has a square-root singularity at z = 1/4. As a matter of fact, the sequence wh = zyh satisfies the recurrence wh+1 = z + wh2 , which was made famous by Mandelbrot’s studies and gives rise to amazing graphics [473]; see Figure VII.23 for a poor man’s version. When |z| ≤ r < 1/4, simple majorant series considerations show that the convergence yh (z) → y(z) is uniformly geometric. When z ≥ s > 1/4, it can be checked that the yh (z) grow doubly exponentially. What happens in-between, in a 1–domain, needs to be quantified. We do so following Flajolet, Gao, Odlyzko, and Richmond [230, 246].

536

VII. APPLICATIONS OF SINGULARITY ANALYSIS

The grey level relative to a point z = x +i y in the diagram indicates the number of iterations necessary for the GFs yh (z) either to diverge to infinity (the outer, darker region) or to the finite limit y(z) (the inner region, corresponding to the Mandelbrot set, with the darker area around 0 corresponding to faster convergence). The cardioid-shaped region defined by |1 − ε(z)| ≤ 1 is a guaranteed region of convergence, beyond the circle |z| = 1/4. The determination of height reduces to finding what goes on near the cusp z = 1/4 of the cardioid. Figure VII.23. The GFs of binary trees of bounded height: speed of convergence.

Starting from the basic recurrence (148), we have y − yh+1 = z(y 2 − yh2 ) = z(y − yh )(2y − (y − yh )), which rewrites as (149)

eh+1 = (2zy)eh (1 − eh ),

where

eh (z) =

1 y(z) − yh (z) 2zy(z)

is proportional to the OGF of trees having height at least h. (The function x 7→ λx(1 − x), which is at the basis of the recurrence (149), is also known as the logistic map; its iterates, for real parameter values λ, give rise to a rich diversity of patterns.) First, let us examine what happens right at the singularity 1/4 and consider eh ≡ eh ( 14 ). The induced recurrence is e0 = 12 , whose solution decreases monotonically to 0 (argument: otherwise, there would need to be a fixed point in (0, 1)). This form resembles the familiar recurrence associated with the solution by iteration of a fixed-point equation ℓ = f (ℓ), but here it corresponds to an “indifferent” fixed-point, f ′ (ℓ) = 1, which precludes the usual geometric convergence. A classical trick of iteration theory, found in de Bruijn’s book [143, §8.4], neatly solves the problem. Consider instead the quantities f h := 1/eh , which satisfy the induced recurrence eh+1 = eh (1 − eh ),

(150)

with

1 1 + 2 ··· , with fh fh This suggests that f h ∼ h. Indeed, by a terminating form of (151),

(151)

f h+1 =

fh

1 − f h−1

(152) f h+1 = f h + 1 +

≡ fh + 1 +

f h−2 1 + , f h 1 − f −1 h

i.e.,

f h+1 = h + 2 +

h X

j=0

f 0 = 2.

f j−1 +

h X

f j−2

−1 j=0 1 − f j

,

one can derive properties of the sequence ( f h ) by “bootstrapping”: the fact that f h > h implies that the first sum in (152) is O(log h), while the second one is O(1); then, another round serves

VII. 10. SINGULARITY ANALYSIS AND PROBABILITY DISTRIBUTIONS

to refine the estimates, so that, for some C: f h = h + log h + C + O

log h h

537

,

and the behaviour of eh = 1/ f h is now well quantified. √ The analysis for z 6= 1/4 proceeds along similar lines. We set ε ≡ ε(z) := 1 − 4z and again abbreviate eh (z) as eh . Upon considering eh fh = (1 − ε)h and taking inverses, we obtain f h+1 = f h + (1 − ε)h +

(153)

Proceeding as before leads to the general approximation

f h eh2 1 − eh

.

√ ε(z)(1 − ε(z))h , ε(z) := 1 − 4z, 1 − (1 − ε(z))h proved to be valid for any fixed z ∈ (0, 1/4), as h → ∞. This approximation is compatible both with eh (1/4) ∼ 1/ h (derived earlier) and with the geometric convergence of yh (z) to y(z) valid for 0 < z < 1/4. With some additional work, it can be proved that (154) remains valid as z → 41 in a 1–domain and as h → ∞; see Figure VII.23. Obtaining the detailed conditions on (z, h), together with a uniform error term for (154), is the crux of the analysis in [247]. From this point on, we content ourselves with brief indications on subsequent developments. Given (154), one deduces24 that the GF of cumulated height satisfies X X ε(1 − ε)h 1 = 4 log + O(1), H (z) := 2y(z) eh (z) ∼ 4 h ε 1 − (1 − ε) (154)

eh (z) ∼

h≥0

h≥1

as z → 41 . Thus, by singularity analysis, one has

1 −→ [z n ]H (z) ∼ 2 · 4n /n, 1 − 4z √ which gives the expected height [z n ]H (z)/[z n ]y(z) of a binary tree of size n as ∼ 2 π n. Moments of higher order can be similarly analysed. It is of interest to note that the GFs that surface explicitly in the analysis of height in general Catalan trees (eventually due to the continued fraction structure and the implied linear recurrences) appear here as analytic approximations in suitable regions of the complex plane. A precise form of the approximation (154) can also be subjected to singularity analysis, to the effect that the same Theta law expresses in the asymptotic limit the distribution of height in binary trees. Finally, the technique can be extended to all simple varieties of trees satisfying the smooth inverse-function schema (Theorem VII.2, p. 453). In summary, we have the following proposition [230, 246]. Proposition VII.16. Let Y be a simple variety of trees satisfying the conditions of Theorem VII.2, with φ the basic tree constructor and τ the root of the characteristic equation φ(τ ) − τ φ(τ ) = 0. Let χ denote tree height. Then the r th moment of height satisfies H (z) ∼ 2 log

EYn [χ r ] ∼ r (r − 1)Ŵ(r/2)ζ (r )ξ r nr/2 ,

ξ :=

2φ ′ (τ )2 . φ(τ )φ ′′ (τ )

24 In order to obtain the logarithmic approximation of H (z), one can for instance appeal to Mellin transform techniques in a way parallel to the analysis of general Catalan trees (p. 326): set 1 − ε(z) = e−t .

538

VII. APPLICATIONS OF SINGULARITY ANALYSIS

√ The normalized height χ / ξ n converges to a Theta law, both in distribution and in the sense of a local limit law. (The Theta distribution is defined in (67), p. 328; Chapter IX develops the notions of convergence in law and of local limits much further.) In particular the expected height in general Catalan trees [145], binary trees, unary–binary trees, pruned t–ary trees, and Cayley trees [507], is found to be, respectively, asymptotic to p √ √ √ √ π n, 2 π n, 3π n, 2π t/(t − 1), 2π n, and a pleasant universality phenomenon manifests itself in the height of simple trees. A somewhat related analysis of a polynomial iteration in the vicinity of a singularity yields the asymptotic number of balanced trees (Note IV.49, p. 283). . . . . . . . . . . . . . . . . . . . . . . . . . .

VII. 11. Perspective The theorems in this chapter demonstrate the central rˆole of the singularity analysis theory developed in Chapter VI, this in a way that parallels what Chapter V did for Chapter IV with meromorphic function analysis. Exploiting properties of complex functions to develop coefficient asymptotics for abstract schemas helps us solve whole collections of combinatorial constructions at once. Within the context of analytic combinatorics, the results in this chapter have broad reach, and bring us closer to our ideal of a theory covering full analysis of combinatorial objects of any “reasonable” description. Analytic side conditions defining schemas often play a significant rˆole. Adding in this chapter the mathematical support for handling set constructions (with the exp–log schema) and context-free constructions (with coefficient asymptotics of algebraic functions) to the support developed in Chapter V to handle the sequence construction (with the supercritical sequence schema) and regular constructions (with coefficient asymptotics of rational functions) gives us general methods encompassing a broad swathe of combinatorial analysis, with a great many applications (Figure VII.24). Together, the methods covered in Chapter V, this chapter, and, next, Chapter VIII (relative to the saddle-point method) apply to virtually all of the generating functions derived in Part A of this book by means of the symbolic techniques defined there. The S EQ construction and regular specifications lead to poles; the S ET construction leads to algebraic singularities (in the case of logarithmic generators discussed here) or to essential singularities (in most of the remaining cases discussed in Chapter VIII); recursive (context-free) constructions lead to square-root singularities. The surprising end result is that the asymptotic counting sequences from all of these generating functions have one of just a few functional forms. This universality means that comparisons of methods, finding optimal values of parameters, and many other outgrowths of analysis can be very effective in practical situations. Indeed, because of the nature of the asymptotic forms, the results are often extremely accurate, as we have seen repeatedly in this book. The general theory of coefficient asymptotics based on singularities has many applications outside of analytic combinatorics (see the notes below). The broad reach of the theory provides strong indications that universal laws hold for many combinatorial constructions and schemas yet to be discovered.

VII. 11. PERSPECTIVE

Combinatorial Type

539

coeff. asymptotics (subexp. term)

Rooted maps

n −5/2

§VII. 8.2

Unrooted trees

n −5/2

§VII. 5

Rooted trees

n −3/2

§VII. 3, §VII. 4

Excursions

n −3/2

§VII. 8.1

Bridges

n −1/2

§VII. 8.1

Mappings

n −1/2

§VII. 3.3

Exp-log sets

n κ−1

§VII. 2

Increasing d–ary trees

Analytic form

n −(d−2)/(d−1) singularity type

§VII. 9.2

coeff. asymptotics

Positive irred. (polynomial syst.)

Z 1/2

ζ −n n −3/2

§VII. 6

General algebraic

Z p/q

ζ −n n − p/q−1

§VII. 7

Z θ (log Z )ℓ

ζ −n n −θ−1 (log n)ℓ

Regular singularity (ODE)

§VII. 9.1

Figure VII.24. A collection of universality laws summarized by the subexponential factors involved in the asymptotics of counting sequences (top). A summary of the main singularity types and asymptotic coefficient forms of this chapter (bottom).

Bibliographic notes. The exp–log schema, like its companion, the supercritical-sequence schema, illustrates the level of generality that can be attained by singularity analysis techniques. Refinements of the results we have given can be found in the book by Arratia, Barbour, and Tavar´e [20], which develops a stochastic process approach to these questions; see also [19] by the same authors for an accessible introduction. The rest of the chapter deals in an essential manner with recursively defined structures. As noted repeatedly in the course of this chapter, recursion is conducive to square-root singularity and universal behaviours of the form n −3/2 . Simple varieties of trees have been introduced in an important paper of Meir and Moon [435], that bases itself on methods developed earlier by P´olya [488, 491] and Otter [466]. One of the merits of [435] is to demonstrate that a high level of generality is attainable when discussing properties of trees. A similar treatment can be inflicted more generally to recursively defined structures when their generating functions satisfy an implicit equation. In this way, non-plane unlabelled trees are shown to exhibit properties very similar to their plane counterparts. It is of interest to note that some of the enumerative questions in this area had been initially motivated by problems of theoretical chemistry: see the colourful account of Cayley and Sylvester’s works in [67], the reference books by Harary and Palmer [319] and Finch [211], as well as P´olya’s original studies [488, 491]. Algebraic functions are the modern counterpart of the study of curves by classical Greek mathematicians. They are either approached by algebraic methods (this is the core of algebraic geometry) or by transcendental methods. For our purposes, however, only rudiments of the theory of curves are needed. For this, there exist several excellent introductory books, of which

540

VII. APPLICATIONS OF SINGULARITY ANALYSIS

we recommend the ones by Abhyankar [2], Fulton [273], and Kirwan [365]. On the algebraic side, we have aimed at providing an introduction to algebraic functions that requires minimal apparatus. At the same time the emphasis has been put somewhat on algorithmic aspects, since most algebraic models are nowadays likely to be treated with the help of computer algebra. As regards symbolic computational aspects, we recommend the treatise by von zur Gathen and Gerhard [599] for background, while polynomial systems are excellently reviewed in the book by Cox, Little, and O’Shea [135]. In the combinatorial domain, algebraic functions have been used early: in Euler and Segner’s enumeration of triangulations (1753) as well as in Schr¨oder’s famous “Vier combinatorische Probleme” described by Stanley in [554, p. 177]. A major advance was the realization by Chomsky and Sch¨utzenberger that algebraic functions are the “exact” counterpart of contextfree grammars and languages (see their historic paper [119]). A masterful summary of the early theory appears in the proceedings edited by Berstel [54] while a modern and precise presentation forms the subject of Chapter 6 of Stanley’s book [554]. On the analytic asymptotic side, many researchers have long been aware of the power of Puiseux expansions in conjunction with some version of singularity analysis (often in the form of the Darboux–P´olya method: see [491] based on P´olya’s classic paper [488] of 1937). However, there appeared to be difficulties in coping with the fully general problem of algebraic coefficient asymptotics [102, 440]. We believe that Section VII. 7 sketches the first complete theory (though most ingredients are of folklore knowledge). In the case of positive systems, the “Drmota–Lalley–Woods” theorem is the key to most problems encountered in practice—its importance should be clear from the developments of Section VII. 6. The applicability of algebraic function theory to context-free languages has been known for some time (e.g., [220]). Our presentation of one-dimensional walks of a general type follows articles by Lalley [396] and Banderier and Flajolet [27], which can be regarded as the analytic pendant of algebraic studies by Gessel [286, 287]. The kernel method has its origins in problems of queueing theory and random walks [202, 203] and is further explored in an article by Bousquet-M´elou and Petkovˇsek [86]. The algebraic treatment of random maps by the quadratic method is due to brilliant studies of Tutte in the 1960s: see for instance his census [579] and the account in the book by Jackson and Goulden [303]. A combinatorial–analytic treatment of multiconnectivity issues is given in [28], where the possibility of treating in a unified manner about a dozen families of maps appears clearly. Regarding differential equations, an early (and at the time surprising) occurrence in an asymptotic expansion of terms of the form n α , with α an algebraic number, is found in the study [252], dedicated to multidimensional search trees. The asymptotic analysis of coefficients of solutions to linear differential equations can also, in principle, be approached from the recurrences that these coefficients satisfy. Wimp and Zeilberger [611] propose an interesting approach based on results by George Birkhoff and his school (e.g., [70]), which are relative to difference equations in the complex plane. There are, however, some doubts among specialists regarding the completeness of Birkhoff’s programme (see our discussion in Section VIII. 7, p. 581). By contrast, the (easier) singularity theory of linear ODEs is well established, and, as we showed in this chapter, it is possible—in the regular singular case at least—to base a sound method for asymptotic coefficient extraction on it.

VIII

Saddle-point Asymptotics Like a lazy hiker, the path crosses the ridge at a low point; but unlike the hiker, the best path takes the steepest ascent to the ridge. [· · · ] The integral will then be concentrated in a small interval. — DANIEL G REENE AND D ONALD K NUTH [310, sec. 4.3.3]

VIII. 1. Landscapes of analytic functions and saddle-points VIII. 2. Saddle-point bounds VIII. 3. Overview of the saddle-point method VIII. 4. Three combinatorial examples VIII. 5. Admissibility VIII. 6. Integer partitions VIII. 7. Saddle-points and linear differential equations. VIII. 8. Large powers VIII. 9. Saddle-points and probability distributions VIII. 10. Multiple saddle-points VIII. 11. Perspective

543 546 551 558 564 574 581 585 594 600 606

A saddle-point of a surface is a point reminiscent of the inner part of a saddle or of a geographical pass between two mountains. If the surface represents the modulus of an analytic function, saddle-points are simply determined as the zeros of the derivative of the function. In order to estimate complex integrals of an analytic function, it is often a good strategy to adopt as contour of integration a curve that “crosses” one or several of the saddle-points of the integrand. When applied to integrals depending on a large parameter, this strategy provides in many cases accurate asymptotic information. In this book, we are primarily concerned with Cauchy integrals expressing coefficients of large index of generating functions. The implementation of the method is then fairly simple, since integration can be performed along a circle centred at the origin. Precisely, the principle of the saddle-point method for the estimation of contour integrals is to choose a path crossing a saddle-point, then estimate the integrand locally near this saddle-point (where the modulus of the integrand achieves its maximum on the contour), and deduce, by local approximations and termwise integration, an asymptotic expansion of the integral itself. Some sort of “localization” or “concentration” property is required to ensure that the contribution near the saddle-point captures the essential part of the integral. A simplified form of the method provides what are known as saddle-point bounds—these useful and technically simple upper bounds are obtained by applying trivial bounds to an integral relative to a saddle-point path. In 541

542

VIII. SADDLE-POINT ASYMPTOTICS

many cases, the saddle-point method can furthermore provide complete asymptotic expansions. In the context of analytic combinatorics, the method is applicable to Cauchy coefficient integrals, in the case of rapidly varying functions: typical instances are entire functions as well as functions with singularities at a finite distance that exhibit some form of exponential growth. Saddle-point analysis then complements singularity analysis whose scope is essentially the category of functions having only moderate (i.e., polynomial) growth at their singularities. The saddle-point method is also a method of choice for the analysis of coefficients of large powers of some fixed function and, in this context, it paves the way to the study of multivariate asymptotics and limiting Gaussian distributions developed in the next chapter. Applications are given here to Stirling’s formula, as well as the asymptotics of the central binomial coefficients, the involution numbers and the Bell numbers associated to set partitions. The asymptotic enumeration of integer partitions is one of the jewels of classical analysis and we provide an introduction to this rich topic where saddlepoints lead to effective estimates of an amazingly good quality. Other combinatorial applications include balls-in-bins models and capacity, the number of increasing subsequences in permutations, and blocks in set partitions. The counting of acyclic graphs (equivalently forests of unrooted trees), finally takes us beyond the basic paradigm of simple saddle-points by making use of multiple saddle-points, also known as “monkey saddles”. Plan of this chapter. First, we examine the surface determined by the modulus of an analytic function and give, in Section VIII. 1, a classification of points into three kinds: ordinary points, zeros, and saddle-points. Next we develop general purpose saddle-point bounds in Section VIII. 2, which also serves to discuss the properties of saddle-point crossing paths. The saddle-point method per se is presented in Section VIII. 3, both in its most general form and in the way it specializes to Cauchy coefficient integrals. Section VIII. 4 then discusses three examples, involutions, set partitions, and fragmented permutations, which help us get further familiarized with the method. We next jump to a new level of generality and introduce in Section VIII. 5 the abstract concept of admissibility—this approach has the merit of providing easily testable conditions, while opening the possibility of determining broad classes of functions to which the saddle-point method is applicable. In particular, many combinatorial types whose leading construction is a S ET operation are seen to be “automatically” amenable to saddle-point analysis. The case of integer partitions, which is technically more advanced, is treated in a separate section, Section VIII. 6. The saddle-method is also instrumental in analysing coefficients of many generating functions implicitly defined by differential equations, including holonomic functions: see Section VIII. 7. Next, the framework of “large powers”, developed in Section VIII. 8 constitutes a combinatorial counterpart of the central limit theorem of probability theory, and as such it provides a bridge to the study of limit distributions to be treated systematically in Chapter IX. Other applications to discrete probability distributions are examined in Section VIII. 9. Finally, Section VIII. 10 serves as a brief introduction to the rich subject of multiple saddle-points and coalescence.

VIII. 1. LANDSCAPES OF ANALYTIC FUNCTIONS AND SADDLE-POINTS

543

VIII. 1. Landscapes of analytic functions and saddle-points This section introduces a well-known classification of points on the surface representing the modulus of an analytic function. In particular, as we are going to see, saddle-points, which are determined by roots of the function’s derivative, are associated with a simple geometric property that gives them their name. Consider any function f (z) analytic for z ∈ , where is some domain of C. Its modulus | f (x +i y)| can be regarded as a function of the two real quantities, x = ℜ(z) and y = ℑ(z). As such, it can be represented as a surface in three-dimensional space. This surface is smooth (analytic functions are infinitely differentiable), but far from being arbitrary. Let z 0 be an interior point of . The local shape of the surface | f (z)| for z near z 0 depends on which of the initial elements in the sequence f (z 0 ), f ′ (z 0 ), f ′′ (z 0 ), . . ., vanish. As we are going to see, its points can be of only one of three types: ordinary points (the generic case), zeros, and saddle-points; see Figure VIII.1. The classification of points is conveniently obtained by considering polar coordinates, writing z = z 0 + r eiθ , with r small. An ordinary point is such that f (z 0 ) 6= 0, f ′ (z 0 ) 6= 0. This is the generic situation as analytic functions have only isolated zeros. In that case, one has, for small r > 0, (1) | f (z)| = f (z 0 ) + r eiθ f ′ (z 0 ) + O(r 2 ) = | f (z 0 )| 1 + λr ei(θ+φ) + O(r 2 ) ,

where we have set f ′ (z 0 )/ f (z 0 ) = λeiφ , with λ > 0. The modulus then satisfies | f (z)| = | f (z 0 )| 1 + λr cos(θ + φ) + O(r 2 ) .

Thus, for r kept small enough and fixed, as θ varies, | f (z)| is maximum when θ = −φ (where it is ∼ | f (z 0 )|(1 + λr )), and minimum when θ = −φ + π (where it is ∼ | f (z 0 )(1 − λr )). When θ = −φ ± π2 , one has | f (z)| = | f (z 0 )| + o(r ), which means that | f (z)| is essentially constant. This is easily interpreted: the line θ ≡ −φ (mod π ) is (locally) a steepest descent line; the perpendicular line θ ≡ −φ + π2 (mod π ) is locally a level line. In particular, near an ordinary point, the surface | f (z)| has neither a minimum nor a maximum. In figurative terms, this is like standing on the flank of a mountain. A zero is by definition a point such that f (z 0 ) = 0. In this case, the function | f (z)| attains its minimum value 0 at z 0 . Locally, to first order, one has | f (z)| ∼ | f ′ (z 0 )|r for a simple zero and | f (z)| = O(r m ) or a zero of order m. A zero is thus like a sink or the bottom of a lake, save that, in the landscape of an analytic function, all lakes are at sea level. A saddle-point is a point such that f (z 0 ) 6= 0, f ′ (z 0 ) = 0; it thus corresponds to a zero of the derivative, when the function itself does not vanish. It is said to be a simple saddle-point if furthermore f ′′ (z 0 ) 6= 0. In that case, a calculation similar to (1), (2) 1 | f (z)| = f (z 0 ) + r 2 e2iθ f ′′ (z 0 ) + O(r 3 ) = | f (z 0 )| 1 + λr 2 ei(2θ+φ) + O(r 3 ) , 2

544

VIII. SADDLE-POINT ASYMPTOTICS

Ordinary point f (z 0 ) 6= 0, f ′ (z 0 ) 6= 0

1111111111111111 0000000000000000 0000000000000000 1111111111111111 0000000000000000 1111111111111111 0000000000000000 1111111111111111 0000000000000000 1111111111111111 0000000000000000 1111111111111111 0000000000000000 1111111111111111 0000000000000000 1111111111111111 0000000000000000 1111111111111111 0000000000000000 1111111111111111 0000000000000000 1111111111111111 0000000000000000 1111111111111111 0000000000000000 1111111111111111 0000000000000000 1111111111111111 0000000000000000 1111111111111111 0000000000000000 1111111111111111 0000000000000000 1111111111111111

Zero f (z 0 ) = 0

Saddle-point f (z 0 ) 6= 0, f ′ (z 0 ) = 0 f ′′ (z 0 ) 6= 0 000000000000000 111111111111111 111111111111111 000000000000000 000000000000000 111111111111111 000000000000000 111111111111111 000000000000000 111111111111111 000000000000000 111111111111111 000000000000000 111111111111111 000000000000000 111111111111111 000000000000000 111111111111111 000000000000000 111111111111111 0000000000000000 1111111111111111 0000000000000000 1111111111111111 0000000000000000 1111111111111111 0000000000000000 1111111111111111 0000000000000000 1111111111111111 0000000000000000 1111111111111111 0000000000000000 1111111111111111 0000000000000000 1111111111111111 0000000000000000 1111111111111111 0000000000000000 1111111111111111

Figure VIII.1. The different types of points on a surface | f (z)|: an ordinary point, a zero, a simple saddle-point. Top: a diagram showing the local structure of level curves (in solid lines), steepest descent lines (dashed with arrows pointing towards the direction of increase) and regions (hashed) where the surface lies below the reference value | f (z 0 )|. Bottom: the function f (z) = cosh z and the local shape of | f (z)| near an ordinary point (iπ/4), a zero (iπ/2), and a saddle-point (0), with level lines shown on the surfaces.

f ′′ (z 0 )/ f (z 0 ) = λeiφ , shows that the modulus satisfies | f (z)| = | f (z 0 )| 1 + λr 2 cos(2θ + φ) + O(r 3 ) .

where we have set

1 2

Thus, starting at the direction θ = −φ/2 and turning around z 0 , the following sequence of events regarding the modulus | f (z)| = | f (r eiθ )| is observed: it is maximal (θ = −φ/2), stationary (θ = −φ/2 + π/4), minimal (θ = −φ/2 + π/2), stationary, (θ = −φ/2 + 3π /4), maximal again (θ = −φ/2 + π ), and so on. The pattern, symbolically “+ = − =”, repeats itself twice. This is superficially similar to an ordinary point, save for the important fact that changes are observed at twice the angular speed. Accordingly, the shape of the surface looks quite different; it is like the central part of a saddle. Two level curves cross at a right angle: one steepest descent line (away from the saddle-point) is perpendicular to another steepest descent line (towards the saddlepoint). In a mountain landscape, this is thus much like a pass between two mountains. The two regions on each side corresponding to points with an altitude below a simple saddle-point are often referred to as “valleys”.

VIII. 1. LANDSCAPES OF ANALYTIC FUNCTIONS AND SADDLE-POINTS

545

1.0

0.5

y 0.0

−0.5

−1.0

−1.5

−1.0

−0.5

0.0

x

Figure VIII.2. The “tripod”: two views of |1 + z + z 2 + z 3 | as function of x = ℜ(z), y = ℑ(z): (left) the modulus as a surface in R3 ; (right) the projection of level lines on the z-plane.

Generally, a multiple saddle-point has multiplicity p if f (z 0 ) 6= 0 and all derivatives f ′ (z 0 ), . . . , f ( p) (z 0 ) are equal to zero while f ( p+1) (z 0 ) 6= 0. In that case, the basic pattern “+ = − =” repeats itself p + 1 times. For instance, from a double saddle-point ( p = 2), three roads go down to three different valleys separated by the flanks of three mountains. A double saddle-point is also called a “monkey saddle” since it can be visualized as a saddle having places for the legs and the tail: see Figure VIII.12 (p. 602) and Figure VIII.14 (p. 605). Theorem VIII.1 (Classification of points on modulus surfaces). A surface | f (z)| attached to the modulus of a function analytic over an open set has points of only three possible types: (i) ordinary points, (ii) zeros, (iii) saddle-points. Under projection on the complex plane, a simple saddle-point is locally the common apex of two curvilinear sectors with angle π/2, referred to as “valleys”, where the modulus of the function is smaller than at the saddle-point. As a consequence, the surface defined by the modulus of an analytic function has no maximum: this property is known as the Maximum Modulus Principle. It has no minimum either, apart from zeros. It is therefore a peakless landscape, in de Bruijn’s words [143]. Accordingly, for a meromorphic function, peaks are at ∞ and minima are at 0, the other points being either ordinary points or isolated saddle-points. Example VIII.1. The tripod: a cubic polynomial. An idea of the typical shape of the surface representing the modulus of an analytic function can be obtained by examining Figure VIII.2 relative to the third degree polynomial f (z) = 1 + z + z 2 + z 3 . Since f (z) = (1 − z 4 )/(1 − z), the zeros are at −1, i, −i. There are saddle-points at the zeros of the derivative f ′ (z) = 1 + 2z + 3z 2 , that is, at the points

546

VIII. SADDLE-POINT ASYMPTOTICS

1 1 i√ i√ ζ := − + 2, ζ ′ := − − 2. 3 3 3 3 The diagram below summarizes the position of these “interesting” points: i (zero) √ ζ = − 13 + 3i 2 (saddle-point)

ζ

(3)

−1 (zero)

(0) √ ζ ′ = − 31 − 3i 2 (saddle-point)

ζ′

−i (zero)

The three zeros are especially noticeable on Figure VIII.2 (left), where they appear at the end of the three “legs”. The two saddle-points are visible on Figure VIII.2 (right) as intersection points of level curves. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

VIII.1. The Fundamental Theorem of Algebra. This theorem asserts that a non-constant polynomial has at least one root, hence n roots if its degree is n (Note IV.38, p. 270). Let P(z) = 1 + a1 z + · · · an z n be a polynomial of degree n. Consider f (z) = 1/P(z). By basic analysis, one can take R sufficiently large, so that on |z| = R, one has | f (z)| < 12 . Assume a contrario that P(z) has no zero. Then, f (z) which is analytic in |z| ≤ R should attain its maximum at an interior point (since f (0) = 1), so that a contradiction has been reached.

VIII.2. Saddle-points of polynomials and the convex hull of zeros. Let P be a polynomial and H the convex hull of its zeros. Then any root of P ′ (z) lies in H. (Proof: assume distinct zeros and consider X P ′ (z) 1 φ(z) := = . P(z) z−α α : P(α)=0

If z lies outside H, then z “sees” all zeros α in a half-plane, this by elementary geometry. By projection on the normal to the half-plane boundary, it is found that, for some θ , one has ℜ(eiθ φ(z)) < 0, so that P ′ (z) 6= 0.)

VIII. 2. Saddle-point bounds Saddle-point analysis is a general method suited to the estimation of integrals of analytic functions F(z), Z B (4) I = F(z) dz, A

where F(z) ≡ Fn (z) involves some large parameter n. The method is instrumental when the integrand F is subject to rather violent variations, typically when there occurs in it some exponential or some fixed function raised to a large power n → +∞. In this section, we discuss some of the global properties of saddle-point contours, then particularize the discussion to Cauchy coefficient integrals. General saddle-point bounds, which are easy to derive, result from simple geometric considerations (a preliminary discussion appears in Chapter IV, p. 246.).

VIII. 2. SADDLE-POINT BOUNDS

547

Starting from the general form (4), we let C be a contour joining A and B and taken in a domain of the complex plane where F(z) is analytic. By standard inequalities, we have (5)

|I | ≤ ||C|| · sup |F(z)|, z∈C

with ||C|| representing the length of C. This is the common trivial bound from integration theory applied to a fixed contour C. For an analytic integrand F with A and B inside the domain of analyticity, there is an infinite class P of acceptable paths to choose from, all in the analyticity domain of F. Thus, by optimizing the bound (5), we may write " # (6)

|I | ≤ inf ||C|| · sup |F(z)| , C ∈P

z∈C

where the infimum is taken over all paths C ∈ P. Broadly speaking, a bound of this type is called a saddle-point bound1. The length factor ||C|| usually turns out to be unimportant for asymptotic bounding purposes—this is, for instance, the case when paths remain in finite regions of the complex plane. If there happens to be a path C from A to B such that no point is at an altitude higher than sup(|F(A)|, |F(B)|), then a simple bound results, namely, |I | ≤ ||C||·sup(|F(A)|, |F(B)|): this is in a sense the uninteresting case. The common situation, typical of Cauchy coefficient integrals of combinatorics, is that paths have to go at some higher altitude than the end points. A path C that traverses a saddle-point by connecting two points at a lower altitude on the surface |F(z)| and by following two steepest descent lines across the saddle-point is clearly a local minimum for the path functional 8(C) = sup |F(z)|, z∈C

as neighbouring paths must possess a higher maximum. Such a path is called a saddlepoint path or steepest descent path. Then, the search for a path minimizing " # inf sup |F(z)| C

z∈C

(a simplification of (6) to its essential feature) naturally suggests considering saddlepoints and saddle-point paths. This leads to the variant of (6), (7)

|I | ≤ ||C0 || · sup |F(z)|, z∈C0

C0 minimizes sup |F(z)|, z∈C

also referred to as a saddle-point bound. We can summarize this stage of the discussion by a simple generic statement. Theorem VIII.2 (General saddle-point bounds). Let F(z) be a function analytic in R a domain . Consider the class of integral γ F(z) dz where the contour γ connects 1 Notice additionally that the optimization problem need not be solved exactly, as any approximate

solution to (6) still furnishes a valid upper bound because of the universal character of the trivial bound (5).

548

VIII. SADDLE-POINT ASYMPTOTICS

two points A, B and is constrained to a class P of allowable paths in (e.g., those that encircle 0). Then one has the saddle-point bound2: Z F(z) dz ≤ ||C0 || · sup |F(z)|, γ z∈C0 (8) where C0 is any path that minimizes sup |F(z)|. z∈C

If A and B lie in opposite valleys of a saddle-point z 0 , then the minimization problem is solved by saddle-point paths C0 made of arcs connecting A to B through z 0 . In that case, one has Z B F(z) dz ≤ ||C0 || · |F(z 0 )| , F ′ (z 0 ) = 0. A

Borrowing a metaphor of de Bruijn [143], the situation may be described as follows. Estimating a path integral is like estimating the difference of altitude between two villages in a mountain range. If the two villages are in different valleys, the best strategy (this is what road networks often do) consists in following paths that cross boundaries between valleys at passes, i.e., through saddle-points. The statement of Theorem VIII.2 does no fix all details of the contour, when there are several saddle-points “separating” A and B—the problem is like finding the most economical route across a whole mountain range. But at least it suggests the construction of a composite contour made of connected arcs crossing saddle-points from valley to valley. Furthermore, in cases of combinatorial interest, some strong positivity is present and the selection of the suitable saddle-point contour is normally greatly simplified, as we explain next.

VIII.3. An integral of powers. Consider the polynomial P(z) = 1 + z + z 2 + z 3 of Example VIII.1. Define the line integral Z +i In = P(z)n dz. −1

On the segment connecting the end points, the maximum of |P(z)| is 0.63831, giving√the weak trivial bound In = O(0.63831n ). In contrast, there is a saddle-point at ζ = − 13 + 3i 2 where |P(ζ )| = 13 , resulting in the bound n 1 . |In | ≤ λ , λ := |ζ + 1| + |i − ζ | = 1.44141, 3

as follows from adopting a contour made of two segments connecting −1 to i through ζ . Discuss R ′ further the bounds on αα , when (α, α ′ ) ranges over all pairs of roots of P.

Saddle-point bounds for Cauchy coefficient integrals. Saddle-point bounds can be applied to Cauchy coefficient integrals, I dz 1 G(z) n+1 , (9) gn ≡ [z n ]G(z) = 2iπ z

2The form given by (8) is in principle weaker than the form (6), since it does not take into account the

length of the contour itself, but the difference is immaterial in all our asymptotic problems.

VIII. 2. SADDLE-POINT BOUNDS

549

−n−1 . for which we can avail H ourselves of the previous discussion, with F(z) = G(z)z In (9) the symbol indicates that the allowable paths are constrained to encircle the origin (the domain of definition of the integrand is a subset of C \ {0}; the points A, B can then be seen as coinciding and taken somewhere along the negative real line; equivalently, one may take A = −aeiǫ and B = −ae−iǫ , for a > 0 and ǫ → 0). In the particular case where G(z) is a function with non-negative coefficients, a simple condition guarantees the existence of a saddle-point on the positive real axis. Indeed, assume that G(z), which has radius of convergence R with 0 < R ≤ +∞, satisfies G(z) → +∞ as z → R − along the real axis and G(z) not a polynomial. Then the integrand F(z) = G(z)z −n−1 satisfies F(0+ ) = F(R − ) = +∞. This means that there exists at least one local minimum of F over (0, R), hence, at least one value ζ ∈ (0, R) where the derivative F ′ vanishes. (Actually, there can be only one such point; see Note VIII.4, p. 550.) Since ζ corresponds to a local minimum of F, we have additionally F ′′ (ζ ) > 0, so that the saddle-point is crossed transversally by a circle of radius ζ . Thus, the saddle-point bound, specialized to circles centred at the origin, yields the following corollary. Corollary VIII.1 (Saddle-point bounds for generating functions). Let G(z), not a polynomial, be analytic at 0 with non-negative coefficients and radius of convergence R ≤ +∞. Assume that G(R − ) = +∞. Then one has

(10)

[z n ]G(z) ≤

G(ζ ) , ζn

with ζ ∈ (0, R) the unique root of ζ

G ′ (ζ ) = n + 1. G(ζ )

Proof. The saddle-point is the point where the derivative of the integrand is 0. Therefore, we consider (G(z)z −n−1 )′ = 0, or G ′ (z)z −n−1 − (n + 1)G(z)z −n−2 = 0, or

G ′ (z) = n + 1. G(z) We refer to this as the saddle-point equation and use ζ to denote its positive root. The perimeter of the circle is 2π ζ , so that the inequality [z n ]G(z) ≤ G(ζ )/ζ n follows. z

Corollary VIII.1 is equivalent to Proposition IV.1, p. 246, on which it sheds a new light, while paving the way to the full saddle-point method to be developed in the next section.

We examine below two particular cases related to the central binomial and the inverse factorial. The corresponding landscapes of Figure VIII.3, which bear a surprising resemblance to one another, are, by the previous discussion, instances of a general pattern for functions with non-negative coefficients. It is seen on these two examples that the saddle-point bounds already catch the proper exponential growths, being off only by a factor of O(n −1/2 ). Example VIII.2. Saddle-point bounds for central binomials and inverse factorials. Consider the two contour integrals around the origin I I dz dz 1 1 (1 + z)2n n+1 , e z n+1 , (11) Jn = Kn = 2iπ 2iπ z z whose values are otherwise known, by virtue of Cauchy’s coefficient formula, to be Jn = 2n n and K n = 1/n!. In that case, one can think of the end points A and B as coinciding and taken

550

VIII. SADDLE-POINT ASYMPTOTICS

Figure VIII.3. The modulus of the integrands of Jn (central binomials) and K n (inverse factorials) for n = 5 and the corresponding saddle-point contours. somewhat arbitrarily on the negative real axis, while the contour has to encircle the origin once and counter-clockwise. The saddle-point equations are, respectively, n+1 n+1 2n − = 0, 1− = 0, 1+z z z n+1 the corresponding saddle-points being ζ = and ζ ′ = n + 1. This provides the upper n−1 bounds !n 4n 2 2n 1 en+1 16 n ≤ (12) Jn = 4 , K = ≤ , ≤ n n 9 n! (n + 1)n n2 − 1 which are valid for all values n ≥ 2. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

VIII.4. Upward convexity of G(x)x −n . For G(z) having non-negative coefficients at the origin, the quantity G(x)x −n is upward convex for x > 0, so that the saddle-point equation for ζ can have at most one root. Indeed, the second derivative

d 2 G(x) x 2 G ′′ (x) − 2nx G ′ (x) + n(n + 1)G(x) , = dx2 xn x n+2 is positive for x > 0 since its numerator, X (n + 1 − k)(n − k)gk x k , gk := [z k ]G(z), (13)

k≥0

has only non-negative coefficients. (See Note IV.46, p. 280, for an alternative derivation.)

VIII.5. A minor optimization. The bounds of Equation (6), p. 547, which take the length of the contour into account, lead to estimates that closely resemble (10). Indeed, we have G ′ (z) G(b ζ) b ζ root of z [z n ]G(z) ≤ n , = n, b G(z) ζ

VIII. 3. OVERVIEW OF THE SADDLE-POINT METHOD

when optimization is carried out over circles centred at the origin.

551

VIII. 3. Overview of the saddle-point method Given a complex integral with a contour traversing a single saddle-point, the saddle-point corresponds locally to a maximum of the integrand along the path. It is then natural to expect that a small neighbourhood of the saddle-point may provide the dominant contribution to the integral. The saddle-point method is applicable precisely when this is the case and when this dominant contribution can be estimated by means of local expansions. The method then constitutes the complex analytic counterpart of the method of Laplace (Appendix B.6: Laplace’s method, p. 755) for the evaluation of real integrals depending on a large parameter, and we can regard it as being Saddle-point method = Choice of contour + Laplace’s method. Similar to its real-variable counterpart, the saddle-point method is a general strategy rather than a completely deterministic algorithm, since many choices are left open in the implementation of the method concerning details of the contour and choices of its splitting into pieces. To proceed, it is convenient to set F(z) = e f (z) and consider Z B (14) I = e f (z) dz, A

where f (z) ≡ f n (z), as F(z) ≡ Fn (z) in the previous section, involves some large parameter n. Following possibly some preparation based on Cauchy’s theorem, we may assume that the contour C connects two end points A and B lying in opposite valleys of the saddle-point ζ . The saddle-point equation is F ′ (ζ ) = 0, or equivalently since F = e f : f ′ (ζ ) = 0. The saddle-point method, of which a summary is given in Figure VIII.4, is based on a fundamental splitting of the integration contour. We decompose C = C (0) ∪ C (1) , where C (0) called the “central part” contains ζ (or passes very near to it) and C (1) is formed of the two remaining “tails”. This splitting has to be determined in each case in accordance with the growth of the integrand. The basic principle rests on two major conditions: the contributions of the two tails should be asymptotically negligible (condition SP1 ); in the central region, the quantity f (z) in the integrand should be asymptotically well approximated by a quadratic function (condition SP2 ). Under these conditions, the integral is asymptotically equivalent to an incomplete Gaussian integral. It then suffices to verify—this is condition SP3 , usually a minor a posteriori technical verification—that tails can be completed back, introducing only negligible error terms. By this sequence of steps, the original integral is asymptotically reduced to a complete Gaussian integral, which evaluates in closed form. Specifically, the three steps of the saddle-point method involve checking conditions expressed by Equations (15), (16), and (18) below.

552

VIII. SADDLE-POINT ASYMPTOTICS

Goal: Estimate

Z B A

F(z) dz, setting F = e f ; here, F ≡ Fn and f ≡ f n depend on a large

parameter n. — The end points A, B are assumed to lie in opposite valleys of the saddle-point. — A contour C through (or near) a simple saddle-point ζ , so that f ′ (ζ ) = 0, has been chosen. — The contour is split as C = C (0) ∪ C (1) . The following conditions are to be verified. R SP1 : Tails pruning. On the contour C (1) , the tails integral C (1) is negligible: Z Z F(z) dz = o F(z) dz . C (1)

C

SP2 : Central approximation. Along C (0) , a quadratic expansion,

1 ′′ f (ζ )(z − ζ )2 + O(ηn ), 2 is valid, with ηn → 0 as n → ∞, uniformly with respect to z ∈ C (0) . SP3 : Tails completion. The incomplete Gaussian integral resulting from SP2 , taken over the central range, is asymptotically equivalent to a complete Gaussian integral (with f ′′ (ζ ) = eiφ | f ′′ (ζ )| and ε = ±1 depending on orientation): s Z Z ∞ 1 ′′ 2 ′′ 2 2π e 2 f (ζ )(z−ζ ) dz ∼ εie−iφ/2 e−| f (ζ )|x /2 d x ≡ εie−iφ/2 ′′ (ζ )| . (0) | f C −∞ f (z) = f (ζ ) +

Result: Assuming SP1 , SP2 , and SP3 , one has, with ε = ±1 and arg( f ′′ (ζ )) = φ: Z B e f (ζ ) 1 e f (ζ ) = ±p . e f (z) dz ∼ εe−iφ/2 p 2iπ A 2π | f ′′ (ζ )| 2π f ′′ (ζ ) Figure VIII.4. A summary of the basic saddle-point method.

SP1 : Tails pruning. On the contour C (1) , the tail integral Z

(15)

C (1)

F(z) dz = o

Z

C

F(z) dz .

R

C (1)

is negligible:

This condition is usually established by proving that F(z) remains small enough (e.g., exponentially small in the scale of the problem) away from ζ , for z ∈ C (1) . SP2 : Central approximation. Along C (0) , a quadratic expansion, (16)

f (z) = f (ζ ) +

1 ′′ f (ζ )(z − ζ )2 + O(ηn ), 2

is valid, with ηn → 0 as n → ∞, uniformly for z ∈ C (0) . This guarantees that well-approximated by an incomplete Gaussian integral: Z Z 1 ′′ 2 (17) e f (z) dz ∼ e f (ζ ) e 2 f (ζ )(z−ζ ) dz. C (0)

C (0)

R

e f is

VIII. 3. OVERVIEW OF THE SADDLE-POINT METHOD

553

SP3 : Tails completion. The tails can be completed back, at the expense of asymptotically negligible terms, meaning that the incomplete Gaussian integral is asymptotically equivalent to a complete one (itself given by (12), p. 744), s Z ∞ Z 1 ′′ 2π 2 ′′ (ζ )|x 2 /2 f (ζ )(z−ζ ) −iφ/2 −| f −iφ/2 dz ∼ εie e d x ≡ εie (18) e2 . | f ′′ (ζ )| −∞ C (0)

where ε = ±1 is determined by the orientation of the original contour C, and f ′′ (ζ ) = eiφ | f ′′ (ζ )|. This last step deserves a word of explanation. Along a steepest descent curve across ζ , the quantity f ′′ (ζ )(z − ζ )2 is real and negative, as we saw when discussing saddle-point landscapes (p. 543). Indeed, with f ′′ (ζ ) = eiφ | f ′′ (ζ )|, one has arg(z −ζ ) ≡ −φ/2+ π2 (mod π ). Thus, the change of variables x = ±i(z −ζ )e−iφ/2 reduces the left side of (18) to an integral taken along (or close to) the real line3. The condition (18) then demands that this integral can be completed to a complete Gaussian integral, which itself evaluates in closed form. If these conditions are granted, one has the chain s Z Z Z 1 ′′ 2π 2 f (ζ )(z−ζ ) −iφ/2 f (ζ ) f f f (ζ ) dz ∼ ±ie e e dz ∼ e dz ∼ e e2 , ′′ (ζ )| (0) (0) | f C C C by virtue of Equations (15), (17), (18). In summary:

RB Theorem VIII.3 (Saddle-point Algorithm). Consider an integral A F(z) dz, where the integrand F = e f is an analytic function depending on a large parameter and A, B lie in opposite valleys across a saddle-point ζ , which is a root of the saddlepoint equation f ′ (ζ ) = 0 (or, equivalently, F ′ (ζ ) = 0). Assume that the contour C connecting A to B can be split into C = C (0) ∪ C (1) in such a way that the following conditions are satisfied: (i) tails are negligible, in the sense of Equation (15) of SP1 , (ii) a central approximation hold, in the sense of Equation (16) of SP2 , (iii) tails can be completed back, in the sense of Equation (18) of SP3 .

Then one has, with ε = ±1 reflecting orientation and φ = arg( f ′′ (ζ )): Z B e f (ζ ) e f (ζ ) 1 = ±p . e f (z) dz ∼ εe−iφ/2 p (19) 2iπ A 2π | f ′′ (ζ )| 2π f ′′ (ζ )

It can be verified at once that a blind application of the formula to the two integrals of Example VIII.2 produces the expected asymptotic estimates 4n 2n 1 1 ∼√ (20) Jn ≡ and Kn ≡ . ∼ √ n −n n n! πn n e 2π n The complete justification in the case of K n is given in Example VIII.3 below. The case of Jn is covered by the general theory of “large powers” of Section VIII. 8, p. 585. 3The sign in (18) is naturally well-defined, once the data A, B, and f are fixed: one possibility is to adopt the determination of φ/2 (mod π ) such that A and B are sent close to the negative and the positive real axis, respectively, after the final change of variables x = i(z − ζ )e−iφ/2 .

554

VIII. SADDLE-POINT ASYMPTOTICS

In order for the saddle-point method to work, conflicting requirements regarding the dimensioning of C (0) and C (1) must be satisfied. The tails pruning and tails completion conditions, SP1 and SP3 , force C (0) to be chosen large enough, so as to capture the main contribution to the integral; the central approximation condition SP2 requires C (0) to be small enough, to the effect that f (z) can be suitably reduced to its quadratic expansion. Usually, one has to take ||C (0) ||/||C|| → 0, and the following observation may help make the right choices. The error in the two-term expansion being likely given by the next term, which involves a third derivative, it is a good guess to dimension C (0) to be of length δ ≡ δ(n) chosen in such a way that (21)

f ′′ (ζ )δ 2 → ∞,

f ′′′ (ζ )δ 3 → 0,

so that both tail and central approximation conditions can be satisfied. We call this choice the saddle-point dimensioning heuristic. On another register, it often proves convenient to adopt integration paths that come close enough to the saddle-point but need not pass exactly through it. In the same vein, a steepest descent curve may be followed only approximately. Such choices will still lead to valid conclusions, as long as the conditions of Theorem VIII.3 are verified. (Note carefully that these conditions neither impose that the contour should pass strictly through the saddle-point, nor that a steepest descent curve should be exactly followed.) Saddle-point method for Cauchy coefficient integrals. For the purposes of analytic combinatorics, the general saddle-point method specializes. We are given a generating function G(z), assumed to be analytic at the origin and with non-negative coefficients, and seek an asymptotic form of the coefficients, given in integral form by Z 1 dz n [z ]G(z) = G(z) n+1 . 2iπ C z There, C encircles the origin, lies within the domain where G is analytic, and is positively oriented. This is a particular case of the general integral (14) considered earlier, with the integrand being F(z) = G(z)/z n+1 . The geometry of the problem is now simple, and, for reasons seen in the previous section, it suffices to consider as integration contour a circle centred at the origin and passing through (or very near) a saddle-point present on the positive real line. It is then natural to make use of polar coordinates and set z = r eiθ , where the radius r of the circle will be chosen equal to (or close to) the positive saddlepoint value. We thus need to estimate I Z dz r −n +π 1 n G(z) n+1 = G(r eiθ )e−niθ dθ. (22) [z ]G(z) = 2iπ 2π −π z Under the circumstances, the basic split of the contour C = C (0) ∪ C (1) involves a central part C (0) , which is an arc of the circle of radius r determined by |θ | ≤ θ0 for

VIII. 3. OVERVIEW OF THE SADDLE-POINT METHOD

555

some suitably chosen θ0 . On C (0) , a quadratic approximation should hold, according to SP2 [central approximation]. Set (23)

f (z) := log G(z) − n log z.

A natural possibility is to adopt for r the value that cancels f ′ (r ), (24)

r

G ′ (r ) = n, G(r )

which is a version of the saddle-point equation4 relative to polar coordinates. This grants us locally, a quadratic approximation without linear terms, with β(r ) a computable quantity (in terms of f (r ), f ′ (r ), f ′′ (r )), we have 1 f (r eiθ ) − f (r ) = − β(r )θ 2 + o(θ 3 ), 2 which is valid at least for fixed r (i.e., for fixed n), as θ → 0 The cutoff angle θ0 is to be chosen as a function of n (or, equivalently, r ) in accordance with the saddle-point heuristic (21). It then suffices to carry out a verification of the validity of the three conditions of the saddle-point method, SP1 , SP2 (for which a suitably uniform version of (25) needs to be developed), and SP3 of Theorem VIII.3, p. 553, adjusted to take into account polar coordinate notations. (25)

The example below details the main steps of the saddle-point analysis of the generating function of inverse factorials, based on the foregoing principles. Example VIII.3. Saddle-point analysis of the exponential and the inverse factorial I. The goal 1 = [z n ]e z , the starting point being is to estimate n! Z 1 dz Kn = e z n+1 , 2iπ |z|=r z where integration will be performed along a circle centred at the origin. The landscape of the modulus of the integrand has been already displayed in Figure VIII.3, p. 550—there is a saddlepoint of G(z)z −n−1 at ζ = n + 1 with an axis perpendicular to the real line. We thus expect an asymptotic estimate to derive from adopting a circle passing through the saddle-point, or about. We switch to polar coordinates, fix the choice of the radius r = n in accordance with (24), and set z = neiθ . The original integral becomes, in polar coordinates, Z +π iθ en 1 en e −1−iθ dθ, (26) Kn = n · n 2π −π where, for readability, we have taken out the factor G(r )/r n ≡ en /n n . Set h(θ) = eiθ − 1 − iθ . The function |eh(θ) | = ecos θ−1 is unimodal with its peak at θ = 0 and the same property holds for |enh(θ) |, representing the modulus of the integrand in (26), which gets more and more strongly peaked at θ = 0, as n → +∞; see Figure VIII.5. 4Equation (24) is almost the same as ζ G ′ (ζ )/G(ζ ) = n + 1 of (10), which defines the saddle-point in

z-coordinates. The (minor) difference is accounted for by the fact that saddle-points are sensitive to changes of variables in integrals. In practice, it proves workable to integrate along a circle of radius either r or ζ , or even a suitably close approximation of r, ζ , the choice being often suggested by computational convenience.

556

VIII. SADDLE-POINT ASYMPTOTICS

Figure VIII.5. Plots of |e z z −n−1 | for n = 3 and n = 30 (scaled according to the value of the saddle-point) illustrate the essential concentration condition as higher values of n produce steeper saddle-point paths.

In agreement with the saddle-point strategy, the estimation of K n proceeds by isolating a small portion of the contour, corresponding to z near the real axis. We thus introduce Z +θ0 Z 2π −θ0 (0) (1) Kn = enh(θ) dθ, Kn = enh(θ) dθ, −θ0

θ0

and choose θ0 in accordance with the general heuristic of (21), which corresponds to the two conditions: nθ02 → ∞ (informally: θ0 ≫ n −1/2 ) and nθ03 → 0, (informally: θ0 ≪ n −1/3 ). One way of realizing the compromise is to adopt θ0 = n a , where a is any number between −1/2 and −1/3. To be specific, we fix a = −2/5, so θ0 ≡ θ0 (n) = n −2/5 .

(27)

In particular, the angle of the central region tends to zero. (i) Tails pruning. For z = neiθ one has e z = en cos θ , and, by unimodality properties of

the cosine, the tail integral K (1) satisfies (1) (28) K n = O e−n(cos θ0 −1) = O exp −Cn 1/5 ,

for some C > 0. The tail integral is thus is exponentially small.

(ii) Central approximation. Near θ = 0, one has h(θ) ≡ eiθ − 1 − iθ = − 12 θ 2 + O(θ 3 ), so that, for |θ | ≤ θ0 , 2 3 2 enh(θ) = e−nθ /2+O(nθ ) = e−nθ /2 1 + O(nθ03 ) . Since θ0 = n −2/5 , we have (29)

(0)

Kn

=

Z +n −2/5 −n −2/5

2 e−nθ /2 dθ 1 + O(n −1/5 ) ,

√ which, by the change of variables t = θ n, becomes Z +n 1/10 2 1 (0) (30) Kn = √ e−t /2 dt 1 + O(n −1/5 ) . n −n 1/10

The central integral is thus asymptotic to an incomplete Gaussian integral.

VIII. 3. OVERVIEW OF THE SADDLE-POINT METHOD

557

(iii) Tails completion. Given (30), the task is now easy. We have, elementarily, for c > 0, Z +∞ 2 2 (31) e−t /2 dt = O e−c /2 , c

which expresses the exponential smallness of Gaussian tails. As a consequence, r Z +∞ 2 /2 1 2π (0) −t (32) Kn ∼ √ e dt ≡ . n n −∞

Assembling (28) and (32), we obtain r en 1 en (0) 2π (1) (1) (0) Kn + Kn ∼ √ , i.e., K n = . Kn + Kn ∼ n n n 2π n n 2π n

The proof also provides a relative error term of O(n −1/5 ). Stirling’s formula is thus seen to be (inter alia!) a consequence of the saddle-point method. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Complete asymptotic expansions. Just like Laplace’s method, the saddle-point method can often be made to provide complete asymptotic expansions. The idea is still to localize the main contribution in the central region, but now take into account corrections terms to the quadratic approximation. As an illustration of these general principles, we make explicit here the calculations relative to the inverse factorial. Example VIII.4. Saddle-point analysis of the exponential and the inverse factorial II. For a complete expansion of [z n ]e z , we only need to revisit the estimation of K (0) in the previous example, since K (1) is exponentially small anyhow. One first rewrites Z θ0 1 2 2 (0) = Kn e−nθ /2 en(cos θ−1+ 2 θ ) dθ −θ0

=

Z θ0 √n √ 1 −w2 /2 enξ(w/ n) dw, √ √ e n −θ0 n

1 ξ(θ) := cos θ − 1 + θ 2 . 2

The calculation proceeds exactly in the same way as for the Laplace method (Appendix B.6: Laplace’s method, p. 755). It suffices to expand h(θ) to any fixed order, which is legitimate in the central region. In this way, a representation of the form, ! Z θ0 √n M−1 3M X E k (w) 2 1 + w 1 (0) dw, +O e−w /2 1 + Kn = √ n −θ0 √n n k/2 n M/2 k=1

is obtained, where the E k (w) are computable polynomials of degree 3k. Distributing the integral operator over terms in the asymptotic expansion and completing the tails yields an expansion of the form M−1 X 1 d (0) k Kn ∼ √ + O(n −M/2 ) , n n k/2 k=0 √ R +∞ −w2 /2 where d0 = 2π and dk := −∞ e E k (w) dw. All odd terms disappear by parity. The net result is then the following. Proposition VIII.1 (Stirling’s formula). The factorial numbers satisfy en n −n 1 1 139 571 1 ∼ √ + + − + ··· . 1− n! 12n 288 n 2 51840 n 3 2488320 n 4 2π n

558

VIII. SADDLE-POINT ASYMPTOTICS

Notice the amazing similarity with the form obtained directly for n! in Appendix B.6: Laplace’s method, p. 755. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

VIII.6. A factorial surprise. Why is it that the expansion of n! and 1/n! involve the same set

of coefficients, up to sign?

VIII. 4. Three combinatorial examples The saddle-point method permits us to solve a number of asymptotic problems coming from analytic combinatorics. In this section, we illustrate its use by treating in some detail three combinatorial examples5: Involutions (I), Set partitions (S), Fragmented permutations (F). These are all labelled structures introduced in Chapter EGFs are I = S ET(S ET1,2 (Z)) Involutions : (33) Set Partition : S = S ET(S ET≥1 (Z)) Fragmented perms : F = S ET(S EQ (Z)) ≥1

II. Their specifications and H⇒ H⇒ H⇒

2

I (z) = e z+z /2 z S(z) = ee −1 F(z) = e z/(1−z) .

The first two are entire functions (i.e., they only have a singularity at ∞), while the last one has a singularity at z = 1. Each of these functions exhibits a fairly violent growth—of an exponential type—near its positive singularity, at either a finite or infinite distance. As the reader will have noticed, all three combinatorial types are structurally characterized by a set construction applied to some simpler structure. Each example is treated, starting from the easier saddle-point bounds and proceeding with the saddle-point method. The example of involutions deals with a problem that is only a little more complicated than inverse factorials. The case of set partitions (Bell numbers) illustrates the need in general of a good asymptotic technology for implicitly defined saddle-points. Finally, fragmented permutations, with their singularity at a finite distance, pave the way for the (harder) analysis of integer partitions in Section VIII. 6. We recapitulate the main features of the saddle-point analyses of these three structures, together with the case of inverse factorials (urns), in Figure VIII.6. Example VIII.5. Involutions. An involution is a permutation τ such that τ 2 is the identity 2 permutation (p. 122). The corresponding EGF is I (z) = e z+z /2 . We have in the notation of (23) z2 f (z) = z + − n log z, 2 and the saddle-point equation in polar coordinates is r (1 + r ) = n,

√ 1 1 1 1√ 4n + 1 ∼ n − + √ + O(n −3/2 ). implying r = − + 2 2 2 8 n

5The purpose of these examples is to become further familiarized with the practice of the saddle-point method in analytic combinatorics. The impatient reader can jump directly to the next section, where she will find a general theory that covers these and many more cases.

VIII. 4. THREE COMBINATORIAL EXAMPLES

Class urns

559

EGF

radius (r )

angle (θ0 )

coeff [z n ] in EGF

ez

n

n −2/5

en n −n ∼ √ 2π n

n − 21

n −2/5

S ET(Z) (Ex. VIII.3, p. 555)

involutions S ET(C YC1,2 (Z))

2 e z+z /2

∼

√

∼

en/2−1/4 n −n/2 √n e √ 2 πn

(Ex. VIII.5, p. 558)

set partitions S ET(S ET≥1 (Z))

r

z ee −1

∼ log n − log log n e−2r/5 /r

(Ex. VIII.6, p. 560)

ee −1 ∼ n√ r 2πr (r + 1)er

fragmented perms S ET(S EQ≥1 (Z))

e z/(1−z)

n −7/10

∼ 1 − √1 n

√

e−1/2+2 n ∼ √ 3/4 2 πn

(Ex. VIII.7, p. 562)

Figure VIII.6. A summary of some major saddle-point analyses in combinatorics.

The use of the saddle-point bound then gives mechanically √

(34)

en/2+ n In (1 + o(1)), ≤ e−1/4 n! n n/2

√ √ In ≤ e−1/4 2π ne−n/2+ n n n/2 (1 + o(1)).

√ (Notice that if we use instead the approximate saddle-point value, n, we only lose a factor . e−1/4 = 0.77880.) The cutoff point between the central and non-central regions is determined, in agreement with (21), by the fact that the length δ of the contour (in z coordinates) should satisfy f ′′ (r )δ 2 → ∞ and f ′′′ (r )δ 3 → 0. In terms of angles, this means that we should choose a cutoff angle θ0 that satisfies r 2 f ′′ (r )θ02 → ∞,

r 3 f ′′′ (r )θ03 → 0.

Here, we have f ′′ (r ) = O(1) and f ′′′ (r ) = O(n −1/2 ). Thus, θ0 must be of an order somewhere in between n −1/2 and n −1/3 , and we fix θ0 = n −2/5 . (i) Tails pruning. First, some general considerations are to be made, regarding the behaviour of |I (z)| along large circles, z = r eiθ . One has r2 cos 2θ. 2 As a function of θ , this function decreases on (0, π2 ), since it is the sum of two decreasing log |I (r eiθ )| = r cos θ + 2

2

functions. Thus, |I (z)| attains its maximum (er +r /2 ) at r and its minimum (e−r /2 ) at z = ri. r In the left half-plane, first for θ ∈ ( π2 , 3π 4 ), the modulus |I (z)| is at most e √since cos 2θ < 0. 3π Finally, for θ ∈ ( 4 , π ) smallness is granted by the fact that cos θ < −1/ 2 resulting in the √

2 bound |I (z)| ≤ er /2−r/ 2 . The same argument applies to the lower half plane ℑ(z) < 0.

560

VIII. SADDLE-POINT ASYMPTOTICS

√ As a consequence of these bounds, I (z)/I ( n) is strongly peaked at z = r ; in particular, it is exponentially small away from the positive real axis, in the sense that ! I (r eiθ0 ) I (r eiθ ) =O = O exp(−n α ) , θ 6∈ [−θ0 , θ0 ], (35) I (r ) I (r ) for some α > 0.

(ii) Central approximation. We then proceed and consider the central integral Z e f (r ) +θ0 (0) exp f (r eiθ ) − f (r ) dθ. Jn = 2π −θ0 √ What is required is a Taylor expansion with remainder near the point r ∼ n. In the central region, the relations f ′ (r ) = 0 f ′′ (r ) = 2 + O(1/n), and f ′′′ (z) = O(n −1/2 ) yield r 2 ′′ f (r eiθ ) − f (r ) = f (r )(eiθ − 1)2 + O n −1/2 r 3 θ03 = −r 2 θ 2 + O(n −1/5 ). 2 This is enough to guarantee that Z e f (r ) +θ0 −r 2 θ 2 (0) (36) Jn = dθ 1 + O(n −1/5 ) . e 2π −θ0 √ (iii) Tails completion. Since r ∼ n and θ0 = n −2/5 , we have Z +∞ Z +θ0 Z 2 1/5 2 2 1 1 +θ0 r −t 2 . (37) e dt = e−t dt + O e−n e−r θ dθ = r −θ0 r r −∞ −θ0

Finally, Equations (35), (36), and (37) give:

Proposition VIII.2. The number In of involutions satisfies √ e−1/4 In 1 = √ n −n/2 en/2+ n 1 + O . (38) n! 2 πn n 1/5 Comparing the saddle-point bound (34) to the true asymptotic form (38), we see that the former is only off by a factor of O(n 1/2 ). Here is a table further comparing the asymptotic estimate In⋆ provided by the right side of (38) to the exact value of In : n In In⋆

10

100

1000

9496 8839

2.40533 · 1082

2.14392 · 101296 2.12473 · 101296 .

2.34149 · 1082

√ The relative error is empirically close to 0.3/ n, a fact that could be proved by developing a complete asymptotic expansion along the lines expounded in the previous section, p. 557. The estimate (38) of In is given by Knuth in [378]: his derivation is carried out by means of the Laplace method applied to the explicit binomial sum that expresses In . Our complex analytic derivation follows Moser and Wyman’s in [448]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Example VIII.6. Set partitions and Bell numbers. The number of partitions of a set of n elements defines the Bell number Sn (p. 109) and one has Sn = n!e−1 [z n ]G(z)

where

The saddle-point equation relative to G(z)z −n−1 (in z-coordinates) is ζ eζ = n + 1.

z

G(z) = ee .

VIII. 4. THREE COMBINATORIAL EXAMPLES

561

This famous equation admits an asymptotic solution obtained by iteration (or “bootstrapping”): it suffices to write ζ = log(n + 1) − log ζ , and iterate (say, starting from ζ = 1), which provides the solution, ! log log n log2 log n (39) ζ ≡ ζ (n) = log n − log log n + +O log n log2 n (see [143, p. 26] for a detailed discussion). The corresponding saddle-point bound reads ζ

Sn ≤ n!

ee −1 . ζn

The approximate solution b ζ = log n yields in particular the simplified upper bound Sn ≤ n!

en−1 . (log n)n

which is enough to check that there are much fewer set partitions than permutations, the ratio being bounded from above by a quantity e−n log log n+O(n) . In order to implement the saddle-point strategy, integration will be carried out over a circle of radius r ≡ ζ . We then set G(z) f (z) = log n+1 = e z − (n + 1) log z, z and proceed to estimate the integral, Z 1 dz Jn = G(z) n+1 , 2iπ C z along the circle C of radius r . The usual saddle-point heuristic suggests that the range of the saddle-point is determined by a quantity θ0 ≡ θ0 (n) such that the quadratic terms in the expansion of f at r tend to infinity, while the cubic terms tend to zero. In order to carry out the calculations, it is convenient to express all quantities in terms of r alone, which is possible since n can be disposed of by means of the relation n + 1 = r er . We find: f ′′ (r ) = er (1 + r −1 ),

f ′′′ (r ) = er (1 − 2r 2 ).

Thus, θ0 should be chosen such that r 2 er θ02 → ∞, r 3 er θ03 → 0, and the choice r θ0 = e−2r/5 is suitable. (i) Tails pruning. First, observe that the function G(z) is strongly concentrated near the real axis since, with z = r eiθ , there holds z z r cos θ e e = er cos θ , . (40) e ≤ ee

In particular G(r eiθ ) is exponentially smaller than G(r ) for any fixed θ 6= 0, when r gets large. (ii) Central approximation. One then considers the central contribution, Z 1 dz (0) Jn := G(z) n+1 , 2iπ C (0) z

where C (0) is the part of the circle z = r eiθ such that |θ | ≤ θ0 ≡ e−2r/5 r −1 . Since on C (0) , the third derivative is uniformly O(er ), one has there 1 f (r eiθ ) = f (r ) − r 2 θ 2 f ′′ (r ) + O(r 3 θ 3 er ). 2 (0)

This approximation can then be transported into the integral Jn .

562

VIII. SADDLE-POINT ASYMPTOTICS

(iii) Tails completion. Tails can be completed in the usual way. The net effect is the estimate e f (r ) [z n ]G(z) = p 1 + O r 3 θ 3 er , 2π f ′′ (r ) which, upon making the error term explicit rephrases, as follows. Proposition VIII.3. The number Sn of set partitions of size n satisfies r ee −1 (41) Sn = n! n √ 1 + O(e−r/5 ) , r r 2πr (r + 1)e

where r is defined implicitly by r er = n + 1, so that r = log n − log log n + o(1).

Here is a numerical table of the exact values Sn compared to the main term Sn⋆ of the approximation (41): n

10

100

1000

Sn Sn⋆

115975 114204

4.75853 · 10115 4.75537 · 10115

2.98990 · 101927 2.99012 · 101927

The error is about 1.5% for n = 10, less than 10−3 and 10−4 for n = 100 and n = 1000. The asymptotic form in terms of r itself is the proper one as no back substitution of an asymptotic expansion of r (in terms of n and log n) can provide an asymptotic expansion for Sn solely in terms of n. Regarding explicit representations in terms of n, it is only log Sn that can be expanded as ! 1 log log n 1 log log n 2 . log Sn = log n − log log n − 1 + + +O n log n log n log n (Saddle-point estimates of coefficient integrals often involve such implicitly defined quantities.) This example probably constitutes the most famous application of saddle-point techniques to combinatorial enumeration. The first correct treatment by means of the saddle-point method is due to Moser and Wyman [447]. It is used for instance by de Bruijn in [143, pp. 104–108] as a lead example of the method. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Example VIII.7. Fragmented permutations. These correspond to F(z) = exp(z/(1 − z)). The example now illustrates the case of a singularity at a finite distance. We set as usual z f (z) = − (n + 1) log z, 1−z and start with saddle-point bounds. The saddle-point equation is ζ (42) = n + 1, (1 − ζ )2 so that ζ comes close to the singularity at 1 as n gets large: √ 1 2n + 3 − 4n + 5 1 ζ = =1− √ + + O(n −3/2 ). 2n + 2 2n n √ Here, the approximation b ζ (n) = 1 − 1/ n, leads to

(43)

√

[z n ]F(z) ≤ e−1/2 e2 n (1 + o(1)).

The saddle-point method is then applied with integration along a circle of radius r ≡ ζ . The saddle-point heuristic suggests to restrict the integral to a small sector of angle 2θ0 , and, since f ′′ (r ) = O(n 3/2 ) while f ′′′ (r ) = O(n 2 ), this means taking θ0 such that n 3/4 θ0 → ∞

VIII. 4. THREE COMBINATORIAL EXAMPLES

563

and n 2/3 θ0 → 0. For instance, the choice θ0 = n −7/10 is suitable. Concentration is easily verified: we have 1 − r cos θ 1/(1−z) = e · exp , e z=r eiθ 1 − 2r cos θ + r 2 which is a unimodal function of θ for θ ∈ (−π, π ). (The maximum of this function of θ is of order exp((1 − r )−1 ) and is attained at θ = 0; the minimum is O(1), attained at θ = π .) In particular, along the non-central part |θ | ≥ θ0 of the saddle-point circle, one has √ 1/(1−z) 1/10 , = O(exp (44) n − n e iθ z=r e

so that tails are exponentially small. Local expansions then enable us to justify the use of the general saddle-point formula in this case. The net result is the following. Proposition VIII.4. The number of fragmented permutations, Fn = n![z n ]F(z), satisfies √

e−1/2 e2 n Fn ∼ √ 3/4 . n! 2 πn

(45)

Quite characteristically, the corresponding saddle-point bound (43) turns out to be off the asymptotic estimate (45) only by a factor of order n 3/4 . The relative error of the approximation (45) is about 4%, 1%, 0.3% for n = 10, 100, 1000, respectively. The expansion above has been extended by E. Maitland Wright [618, 619] to several classes of functions with a singularity whose type is an exponential of a function of the form (1 − z)−ρ ; see Note VIII.7. (For the case of (45), Wright [618] refers to an earlier article of Perron published in 1914.) His interest was due, at least partly, to applications to generalized partition asymptotics, of which the basic cases are discussed in Section VIII. 6, p. 574. . . . .

VIII.7. Wright’s expansions. Consider the function F(z) = (1 − z)−β exp

A (1 − z)ρ

,

A > 0,

ρ > 0.

Then, a saddle-point analysis yields, when ρ < 1:

N β−1−ρ/2 exp A(ρ + 1)N ρ [z n ]F(z) ∼ , √ 2π Aρ(ρ + 1)

N :=

n Aρ

1 ρ+1

.

(The case ρ ≥ 1 involves more terms of the asymptotic expansion of the saddle-point.) The method generalizes to analytic and logarithmic multipliers, as well as to a sum of terms of the form A(1 − z)−ρ inside the exponential. See [618, 619] for details.

VIII.8. Some oscillating coefficients. Define the function s(z) = sin

z 1−z

.

The coefficients sn = [z n ]s(z) are seen to change sign at n = 6, 21, 46, 81, 125, 180, . . . . Do signs change infinitely many times? (Hint: Yes. there are two complex conjugate saddle-points √ a eb n with an oscillating and the associated asymptotic forms combine a growth of the type n √ factor similar to sin n.) The sum n X n (−1)k Un = k! k k=0

exhibits similar fluctuations.

564

VIII. SADDLE-POINT ASYMPTOTICS

VIII. 5. Admissibility The saddle-point method is a versatile approach to the analysis of coefficients of fast-growing generating functions, but one which is often cumbersome to apply step-by-step. Fortunately, it proves possible to encapsulate the conditions repeatedly encountered in our previous examples into a general framework. This leads to the notion of an admissible function presented in Subsection VIII. 5.1. By design, saddlepoint analysis applies to such functions and asymptotic forms for their coefficients can be systematically determined: this follows an approach initiated by Hayman in 1956. A great merit of abstraction in this context is that admissible functions satisfy useful closure properties, so that an infinite class of admissible functions of relevance to combinatorial applications can be determined—we develop this theme in Subsection VIII. 5.2, relative to enumeration. Finally, Subsection VIII. 5.3 presents an approach to the probabilistic problem known as depoissonization, which is much akin to admissibility. VIII. 5.1. Admissibility theory. The notion of admissibility is in essence an axiomatization of the conditions underlying Theorem VIII.3 particularized to the case of Cauchy coefficient integrals. In this section, we base our discussion on H –admissibility, the prefix H being a token of Hayman’s original contribution [325]. A crisp account of the theory is given in Section II.7 of Wong’s book [614] and in Odlyzko’s authoritative survey [461, Sec. 12]. We consider here a function G(z) that is analytic at the origin and whose coefficients [z n ]G(z) are to be estimated by Z dz 1 G(z) n+1 . gn ≡ [z n ]G(z) = 2iπ C z The switch to polar coordinates is natural, so that the expansion of G(r eiθ ) for small θ plays a central rˆole: with r a positive real number lying within the disc of analyticity of G(z), the fundamental expansion is

(46)

log G(r eiθ ) = log G(r ) +

∞ X ν=1

αν (r )

(iθ )ν . ν!

Not surprisingly, the most important quantities are the first two terms, and once G(z) has been put into exponential form, G(z) = eh(z) , a simple computation yields a(r ) := α1 (r ) = r h ′ (r ) (47) b(r ) := α2 (r ) = r 2 h ′′ (r ) + r h ′ (r ), with h(z) := log G(z). In terms of G, itself, one thus has (48)

G ′ (r ) , a(r ) = r G(r )

G ′ (r ) G ′′ (r ) b(r ) = r + r2 − r2 G(r ) G(r )

G ′ (r ) G(r )

2

.

Whenever G(z) has non-negative Taylor coefficients at the origin, b(r ) is positive for r > 0 and a(r ) increases as r → ρ, with ρ the radius of convergence of G. (This follows from the argument developed in Note VIII.4, p. 550.)

VIII. 5. ADMISSIBILITY

565

Definition VIII.1 (Hayman–admissibility). Let G(z) have radius of convergence ρ with 0 < ρ ≤ +∞ and be always positive on some subinterval (R0 , ρ) of (0, ρ). The function G(z) is said to be H –admissible (Hayman admissible) if, with a(r ) and b(r ) as defined in (47), it satisfies the following three conditions: H1 . [Capture condition] lim a(r ) = +∞ and lim b(r ) = +∞. r →ρ

r →ρ

H2 . [Locality condition] For some function θ0 (r ) defined over (R0 , ρ) and satisfying 0 < θ0 < π , one has G(r eiθ ) ∼ G(r )eiθa(r )−θ

2 b(r )/2

as r → ρ,

uniformly in |θ | ≤ θ0 (r ). H3 . [Decay condition] Uniformly in θ0 (r ) ≤ |θ | < π G(r ) iθ G(r e ) = o √ . b(r ) Note that the conditions in the definition are intrinsic to the function: they only make reference to the function’s values along circles, no parameter n being involved z yet. It can be easily verified, from the previous examples, that the functions e z , ee −1 , 2 and e z+z /2 are admissible with ρ = +∞, and that the function e z/(1−z) is admissible with ρ = 1 (refer in each case to the discussion of the behaviour of the modulus of 2 2 G(r eiθ ), as θ varies). By contrast, functions such as e z and e z +e z are not admissible since they attain values that are too large when arg(z) is near π . Coefficients of H –admissible functions can be systematically analysed to first asymptotic order, as expressed by the following theorem: Theorem VIII.4 (Coefficients of admissible functions). Let G(z) be an H –admissible function and ζ ≡ ζ (n) be the unique solution in the interval (R0 , ρ) of the equation (49)

ζ

G ′ (ζ ) = n. G(ζ )

The Taylor coefficients of G(z) satisfy, as n → ∞: (50) gn ≡ [z n ]G(z) ∼

G(ζ ) , √ n ζ 2π b(ζ )

b(z) := z 2

d d2 log G(z) + z log G(z). 2 dz dz

Proof. The proof simply amounts to transcribing the definition of admissibility into the conditions of Theorem VIII.3. Integration is carried out over a circle centred at the origin, of some radius r to be specified shortly. Under the change of variable z = r eiθ , the Cauchy coefficient formula becomes Z r −n +π (51) gn ≡ [z n ]G(z) = G(r eiθ )e−niθ dθ. 2π −π In order to obtain a quadratic approximation without a linear term, one chooses the radius of the circle as the positive solution ζ of the equation a(ζ ) = n, that is, a solution of Equation (49). (Thus ζ is a saddle-point of G(z)z −n .) By the capture condition H1 , we have ζ → ρ − as n → +∞. Following the general saddle-point strategy,

566

VIII. SADDLE-POINT ASYMPTOTICS

we decompose the integration domain and set, with θ0 as specified in conditions H2 and H3 : Z +θ0 Z 2π −θ0 (0) iθ −niθ (1) J = G(ζ e )e dθ, J = G(ζ eiθ )e−niθ dθ. −θ0

θ0

(i) Tails pruning. By the decay condition H3 , we have a trivial bound, which suffices for our purposes: G(ζ ) (52) J (1) = o √ . b(ζ ) (ii) Central approximation. The uniformity of the locality condition H2 implies Z +θ0 2 (53) J (0) ∼ G(ζ ) e−θ b(ζ )/2 dθ. −θ0

(iii) Tails completion. A combination of the locality condition H2 and the decay condition H3 instantiated at θ = θ0 , shows that b(ζ )θ 2 → +∞ as n → +∞. There results that tails can be completed back, and Z +θ0 /√b(ζ ) Z +∞ Z +θ0 1 1 2 −b(r )θ 2 /2 −t 2 /2 e−t /2 dt. (54) e dθ ∼ √ e dt ∼ √ √ b(r ) −θ0 / b(ζ ) b(r ) −∞ −θ0 From (52), (53), and (54) (or equivalently via an application of Theorem VIII.3), the conclusion of the theorem follows. The usual comments regarding the choice of the function θ0 (r ) apply. Considering the expansion (46), we must have α2 (r )θ02 → ∞ and α3 (r )θ03 → 0. Thus, in order to succeed, the method necessitates a priori α3 (r )2 /α2 (r )3 → 0. Then, θ0 should be taken according to the saddle-point dimensioning heuristic, which can be figuratively summarized as6 1 1 (55) ≪ θ0 ≪ , α2 (r )1/2 α3 (r )1/3 −1/4 −1/6

a possible choice being the geometric mean of the two bounds θ0 = α2 α3 . The original proof by Hayman [325] contains in addition a general result that describes the shape of the individual terms gn r n in the Taylor expansion of G(r ) as r gets closer to its limit value ρ: these appear to exhibit a bell-shaped profile. Precisely, for G with non-negative coefficients, define a family of discrete random variables X (r ) indexed by r ∈ (0, R) as follows: P(X (r ) = n) =

gn r n . G(r )

The model in which a random F structure with GF G(z) is drawn with its size being the random value X (r ) is known as a Boltzmann model. Then: 6We occasionally write A ≪ B, equivalently, B ≫ A, if A = o(B).

VIII. 5. ADMISSIBILITY

567

0.06

0.06 0.05

0.05 0.04

0.04 0.03

0.03 0.02

0.02

0.01

0.01

0

0 0

20

40

60

80

100

120

0

20

40

60

80

100

120

Figure VIII.7. The families of Boltzmann distributions associated with involutions, 2 z G(z) = e z+z /2 with r = 4 . . 8, and set partitions, G(z) = ee −1 with r = 2 . . 3, obey an approximate Gaussian profile.

Proposition VIII.5. The Boltzmann probabilities associated to an admissible function G(z) satisfy, as r → ρ − , a “local” Gaussian estimate; namely, " # ! (a(r ) − n)2 gn r n 1 exp − (56) =√ + ǫn , G(r ) 2b(r ) 2π b(r ) where the error term satisfies ǫn = o(1) as r → ρ uniformly with respect to integers n; that is, limr →ρ supn |ǫn | = 0. The proof is entirely similar to that of Theorem VIII.4; see Note VIII.9 and Figure VIII.7 for a suggestive illustration.

VIII.9. Admissibility and Boltzmann models. The Boltzmann distribution is accessible from gn r n =

Z 2π −θ0 1 G(r eiθ )e−inθ dθ. 2π −θ0

The estimation of this integral is once more based on a fundamental split Z +θ0 Z 2π −θ0 1 1 , J (1) = , gn r n = J (0) + J (1) where J (0) = 2π −θ0 2π +θ0 and θ0 = θ0 (n) is as specified by the admissibility definition. Only the central approximation and tails completion deserves adjustments. The “locality” condition H2 gives uniformly in n, Z G(r ) +θ0 i(a(r )−n)θ− 1 b(r )θ 2 2 J (0) = e (1 + o(1)) dθ 2π "−θ0 # Z +∞ Z +θ0 (57) 1 1 2 2 G(r ) i(a(r )−n)θ− b(r )θ b(r )θ − 2 dθ . e dθ + o e 2 = 2π −θ0 −∞ and setting (a(r ) − n)(2/b(r ))1/2 = c, we obtain "Z # √ +θ0 b(r )/2 2 +ict G(r ) (0) −t (58) J = √ e dt + o(1) . π 2b(r ) −θ0 √b(r )/2

568

VIII. SADDLE-POINT ASYMPTOTICS

The integral in (58) can then be routinely extended to a complete Gaussian integral, introducing only o(1) error terms, Z +∞ 2 G(r ) e−t +ict dt + o(1) . (59) J (0) = √ π 2b(r ) −∞ √ 2 Finally, the Gaussian integral evaluates to πe−c /4 , as is seen by completing the square and shifting vertically the integration line.

VIII.10. Hayman’s original. The condition H1 of Theorem VIII.4 can be replaced by H′1 . [Capture condition] lim b(r ) = +∞. r →ρ

That is, a(r ) → +∞ is a consequence of H′1 , H2 , and H3 . (See [325, §5].)

VIII.11. Non-admissible functions. Singularity analysis and H –admissibility conditions are in a sense complementary. Indeed, the function G(z) = (1 − z)−1 fails to be be admissible 1 !! e ∼√ , as the asymptotic form that Theorem VIII.4 would imply is the erroneous [z n ] 1−z 2π corresponding to a saddle-point near 1 − n −1 . The explanation of the discrepancy is as follows: Expansion (46) has αν (r ) of the order of (1 − r )−ν , so that the locality condition and the decay condition cannot be simultaneously satisfied. Singularity analysis salvages the situation by using a larger contour and by normalizing to a global Hankel Gamma integral instead of a more “local” Gaussian integral. This is also in accordance with the fact that the saddle-point formula gives, in the case of [z n ](1 − z)−1 , an estimate, which is within a constant factor of the true value 1. (More generally, functions of the form (1 − z)−β are typical instances with too slow a growth to be admissible.) Closure properties. An important aspect of Hayman’s work is that it leads to general theorems, which guarantee that large classes of functions are admissible. Theorem VIII.5 (Closure of H –admissible functions). Let G(z) and H (z) be admissible functions and let P(z) be a polynomial with real coefficients. Then: (i) The product G(z)H (z) and the exponential e G(z) are admissible functions. (ii) The sum G(z) + P(z) is admissible. If the leading coefficient of P(z) is positive then G(z)P(z) and P(G(z)) are admissible. (iii) If the Taylor coefficients of e P(z) are eventually positive, then e P(z) is admissible. Proof. (Sketch) The easy proofs essentially reduce to making an inspired guess for the choice of the θ0 function, which may be guided by Equation (55) in the usual way, and then routinely checking the conditions of the admissibility definition. For instance, in the case of the exponential, K (z) = e G(z) , the conditions H1 , H2 , H3 of Definition VIII.1 are satisfied if one takes θ0 (r ) = (G(r ))−2/5 . We refer to Hayman’s original paper [325] for details. Exponentials of polynomials. The closure theorem also implies as a very special case that any GF of the form e P(z) with P(z) a polynomial with positive coefficients can be subjected to saddle-point analysis, a fact first noted by Moser and Wyman [449, 450]. P Corollary VIII.2 (Exponentials of polynomials). Let P(z) = mj=1 a j z j have nonnegative coefficients and be aperiodic in the sense that gcd{j | a j 6= 0} = 1. Let

VIII. 5. ADMISSIBILITY

f (z) = e P(z) . Then, one has

e P(r ) f n ≡ [z ] f (z) ∼ √ , 2π λ r n n

1

where

569

d 2 P(r ), λ= r dr

d and r is a function of n given implicitly by r dr P(r ) = n.

The computations are in this case purely mechanical, since they only involve the asymptotic expansion (with respect to n) of an algebraic equation. Granted the basic admissibility theorem and closures properties, many functions are immediately seen to be admissible, including ez ,

ee

z −1

,

e z+z

2 /2

,

which have previously served as lead examples for illustrating the saddle-point method. Corollary VIII.2 also covers involutions, permutations of a fixed order in the symmetric group, permutations with cycles of bounded length, as well as set partitions with bounded block sizes: see Note VIII.12 below. More generally, Corollary VIII.2 applies to any labelled set construction, F = S ET(G), when the sizes of G–components are restricted to a finite set, in which case one has m j X z Gj . H⇒ F [m] (z) = exp F [m] = S ET ∪rj=1 G j , j! j=1

This covers all sorts of graphs (plain or functional) whose connected components are of bounded size.

VIII.12. Applications of “exponentials of polynomials”. Corollary VIII.2 applies to the following combinatorial situations: Permutations of order p (σ p = 1) Permutations with longest cycle ≤ p Partitions of sets with largest block ≤ p

P

zj P j | p jj p z f (z) = exp P j=1 jj p z f (z) = exp j=1 j! .

f (z) = exp

For instance, the number of solutions of σ p = 1 in the symmetric group is asymptotic to n n(1−1/ p) p−1/2 exp(n 1/ p ), e for any fixed prime p ≥ 3 (Moser and Wyman [449, 450]).

Complete asymptotic expansions. Harris and Schoenfeld have introduced in [323] a technical condition of admissibility that is stronger than Hayman admissibility and is called H S–admissibility. Under such H S–admissibility, a complete asymptotic expansion can be obtained. We omit the definition here due to its technical character but refer instead to the original paper [323] and to Odlyzko’s survey [461]. Odlyzko and Richmond [462] later showed that, if g(z) is H –admissible, then f (z) = e g(z) is H S– admissible. Thus, taking H –admissibility to mean at least exponential growth, full asymptotic expansions are to be systematically expected at double exponential growth and beyond. The principles of developing full asymptotic expansions are essentially the same as the ones explained on p. 557—only the discussion of the asymptotic scales involved becomes a bit intricate, at this level of generality.

570

VIII. SADDLE-POINT ASYMPTOTICS

VIII. 5.2. Higher-level structures and admissibility. The concept of admissibility and its surrounding properties (Theorems VIII.4 and VIII.5, Corollary VIII.2) afford a neat discussion of which combinatorial classes should lead to counting sequences that are amenable to the saddle-point method. For simplicity, we restrict ourselves here to the labelled universe. Start from the first-level structures, namely S EQ(Z),

C YC(Z),

S ET(Z),

corresponding, respectively, to permutations, circular graphs, and urns, with EGFs 1 1 , log , ez . 1−z 1−z The first two are of singularity analysis class; the last is, as we saw, within the reach of the saddle-point method and is H –admissible. Next consider second-level structures defined by arbitrary composition of two constructions taken among S EQ, C YC, S ET; see Subsection II. 4.2, p. 124 for a preliminary discussion (In the case of the internal construction, it is understood that, for definiteness, the number of components is constrained to be ≥ 1.) There are three structures whose external construction is of the sequence type, namely, S EQ ◦ S EQ,

S EQ ◦ C YC,

S EQ ◦ S ET,

corresponding, respectively, to labelled compositions, alignments, and surjections. All three have a dominant singularity that is a pole; hence they are amenable to meromorphic coefficient asymptotics (Chapters IV and V), or, with weaker remainder estimates, to singularity analysis (Chapters VI and VII). Similarly there are three structures whose external construction is of the cycle type, namely, C YC ◦ S EQ, C YC ◦ C YC, C YC ◦ S ET, corresponding to cyclic versions of the previous ones. In that case, the EGFs have a logarithmic singularity; hence they are amenable to singularity analysis, or

ANALYTIC COMBINATORICS PHILIPPE FLAJOLET Algorithms Project INRIA Rocquencourt 78153 Le Chesnay France

& ROBERT SEDGEWICK Department of Computer Science Princeton University Princeton, NJ 08540 USA

ISBN 978-0-521-89806-5 c

Cambridge University Press 2009 (print version) c Flajolet and R. Sedgewick 2009 (e-version)

P.

This version is dated June 26, 2009. It is essentially identical to the print version. c

Philippe Flajolet and Robert Sedgewick 2009, for e-version c

Cambridge University Press 2009, for print version ISBN-13: 9780521898065

On-screen viewing and printing of individual copy of this free PDF version for research purposes (non-commercial single-use) is permitted.

ANALYTIC COMBINATORICS

Analytic combinatorics aims to enable precise quantitative predictions of the properties of large combinatorial structures. The theory has emerged over recent decades as essential both for the analysis of algorithms and for the study of scientific models in many disciplines, including probability theory, statistical physics, computational biology and information theory. With a careful combination of symbolic enumeration methods and complex analysis, drawing heavily on generating functions, results of sweeping generality emerge that can be applied in particular to fundamental structures such as permutations, sequences, strings, walks, paths, trees, graphs and maps. This account is the definitive treatment of the topic. In order to make it selfcontained, the authors give full coverage of the underlying mathematics and give a thorough treatment of both classical and modern applications of the theory. The text is complemented with exercises, examples, appendices and notes throughout the book to aid understanding. The book can be used as a reference for researchers, as a textbook for an advanced undergraduate or a graduate course on the subject, or for self-study. PHILIPPE FLAJOLET is Research Director of the Algorithms Project at INRIA Rocquencourt. ROBERT SEDGEWICK is William O. Baker Professor of Computer Science at Princeton University.

(from print version, front)

Contents P REFACE

vii

A N I NVITATION TO A NALYTIC C OMBINATORICS

1

Part A. SYMBOLIC METHODS

13

I. C OMBINATORIAL S TRUCTURES AND O RDINARY G ENERATING F UNCTIONS I. 1. Symbolic enumeration methods I. 2. Admissible constructions and specifications I. 3. Integer compositions and partitions I. 4. Words and regular languages I. 5. Tree structures I. 6. Additional constructions I. 7. Perspective

15 16 24 39 49 64 83 92

II. L ABELLED S TRUCTURES AND E XPONENTIAL G ENERATING F UNCTIONS II. 1. Labelled classes II. 2. Admissible labelled constructions II. 3. Surjections, set partitions, and words II. 4. Alignments, permutations, and related structures II. 5. Labelled trees, mappings, and graphs II. 6. Additional constructions II. 7. Perspective

95 96 100 106 119 125 136 147

III. C OMBINATORIAL PARAMETERS AND M ULTIVARIATE G ENERATING F UNCTIONS III. 1. An introduction to bivariate generating functions (BGFs) III. 2. Bivariate generating functions and probability distributions III. 3. Inherited parameters and ordinary MGFs III. 4. Inherited parameters and exponential MGFs III. 5. Recursive parameters III. 6. Complete generating functions and discrete models III. 7. Additional constructions III. 8. Extremal parameters III. 9. Perspective

151 152 156 163 174 181 186 198 214 218

Part B. COMPLEX ASYMPTOTICS

221

IV. C OMPLEX A NALYSIS , R ATIONAL AND M EROMORPHIC A SYMPTOTICS IV. 1. Generating functions as analytic objects IV. 2. Analytic functions and meromorphic functions

223 225 229

iii

iv

CONTENTS

IV. 3. IV. 4. IV. 5. IV. 6. IV. 7. IV. 8.

Singularities and exponential growth of coefficients Closure properties and computable bounds Rational and meromorphic functions Localization of singularities Singularities and functional equations Perspective

238 249 255 263 275 286

V. A PPLICATIONS OF R ATIONAL AND M EROMORPHIC A SYMPTOTICS V. 1. A roadmap to rational and meromorphic asymptotics V. 2. The supercritical sequence schema V. 3. Regular specifications and languages V. 4. Nested sequences, lattice paths, and continued fractions V. 5. Paths in graphs and automata V. 6. Transfer matrix models V. 7. Perspective

289 290 293 300 318 336 356 373

VI. S INGULARITY A NALYSIS OF G ENERATING F UNCTIONS VI. 1. A glimpse of basic singularity analysis theory VI. 2. Coefficient asymptotics for the standard scale VI. 3. Transfers VI. 4. The process of singularity analysis VI. 5. Multiple singularities VI. 6. Intermezzo: functions amenable to singularity analysis VI. 7. Inverse functions VI. 8. Polylogarithms VI. 9. Functional composition VI. 10. Closure properties VI. 11. Tauberian theory and Darboux’s method VI. 12. Perspective

375 376 380 389 392 398 401 402 408 411 418 433 437

VII. A PPLICATIONS OF S INGULARITY A NALYSIS VII. 1. A roadmap to singularity analysis asymptotics VII. 2. Sets and the exp–log schema VII. 3. Simple varieties of trees and inverse functions VII. 4. Tree-like structures and implicit functions VII. 5. Unlabelled non-plane trees and P´olya operators VII. 6. Irreducible context-free structures VII. 7. The general analysis of algebraic functions VII. 8. Combinatorial applications of algebraic functions VII. 9. Ordinary differential equations and systems VII. 10. Singularity analysis and probability distributions VII. 11. Perspective

439 441 445 452 467 475 482 493 506 518 532 538

VIII. S ADDLE - POINT A SYMPTOTICS VIII. 1. Landscapes of analytic functions and saddle-points VIII. 2. Saddle-point bounds VIII. 3. Overview of the saddle-point method VIII. 4. Three combinatorial examples VIII. 5. Admissibility VIII. 6. Integer partitions

541 543 546 551 558 564 574

CONTENTS

VIII. 7. VIII. 8. VIII. 9. VIII. 10. VIII. 11.

Saddle-points and linear differential equations. Large powers Saddle-points and probability distributions Multiple saddle-points Perspective

v

581 585 594 600 606

Part C. RANDOM STRUCTURES

609

IX. M ULTIVARIATE A SYMPTOTICS AND L IMIT L AWS IX. 1. Limit laws and combinatorial structures IX. 2. Discrete limit laws IX. 3. Combinatorial instances of discrete laws IX. 4. Continuous limit laws IX. 5. Quasi-powers and Gaussian limit laws IX. 6. Perturbation of meromorphic asymptotics IX. 7. Perturbation of singularity analysis asymptotics IX. 8. Perturbation of saddle-point asymptotics IX. 9. Local limit laws IX. 10. Large deviations IX. 11. Non-Gaussian continuous limits IX. 12. Multivariate limit laws IX. 13. Perspective

611 613 620 628 638 644 650 666 690 694 699 703 715 716

Part D. APPENDICES

719

Appendix A. AUXILIARY E LEMENTARY N OTIONS A.1. Arithmetical functions A.2. Asymptotic notations A.3. Combinatorial probability A.4. Cycle construction A.5. Formal power series A.6. Lagrange inversion A.7. Regular languages A.8. Stirling numbers. A.9. Tree concepts

721 721 722 727 729 730 732 733 735 737

Appendix B. BASIC C OMPLEX A NALYSIS B.1. Algebraic elimination B.2. Equivalent definitions of analyticity B.3. Gamma function B.4. Holonomic functions B.5. Implicit Function Theorem B.6. Laplace’s method B.7. Mellin transforms B.8. Several complex variables

739 739 741 743 748 753 755 762 767

Appendix C. C ONCEPTS OF P ROBABILITY T HEORY C.1. Probability spaces and measure C.2. Random variables C.3. Transforms of distributions

769 769 771 772

vi

CONTENTS

C.4. C.5.

Special distributions Convergence in law

774 776

B IBLIOGRAPHY

779

I NDEX

801

Preface A NALYTIC C OMBINATORICS aims at predicting precisely the properties of large structured combinatorial configurations, through an approach based extensively on analytic methods. Generating functions are the central objects of study of the theory. Analytic combinatorics starts from an exact enumerative description of combinatorial structures by means of generating functions: these make their first appearance as purely formal algebraic objects. Next, generating functions are interpreted as analytic objects, that is, as mappings of the complex plane into itself. Singularities determine a function’s coefficients in asymptotic form and lead to precise estimates for counting sequences. This chain of reasoning applies to a large number of problems of discrete mathematics relative to words, compositions, partitions, trees, permutations, graphs, mappings, planar configurations, and so on. A suitable adaptation of the methods also opens the way to the quantitative analysis of characteristic parameters of large random structures, via a perturbational approach. T HE APPROACH to quantitative problems of discrete mathematics provided by analytic combinatorics can be viewed as an operational calculus for combinatorics organized around three components. Symbolic methods develops systematic relations between some of the major constructions of discrete mathematics and operations on generating functions that exactly encode counting sequences. Complex asymptotics elaborates a collection of methods by which one can extract asymptotic counting information from generating functions, once these are viewed as analytic transformations of the complex domain. Singularities then appear to be a key determinant of asymptotic behaviour. Random structures concerns itself with probabilistic properties of large random structures. Which properties hold with high probability? Which laws govern randomness in large objects? In the context of analytic combinatorics, these questions are treated by a deformation (adding auxiliary variables) and a perturbation (examining the effect of small variations of such auxiliary variables) of the standard enumerative theory. The present book expounds this view by means of a very large number of examples concerning classical objects of discrete mathematics and combinatorics. The eventual goal is an effective way of quantifying metric properties of large random structures. vii

viii

PREFACE

Given its capacity of quantifying properties of large discrete structures, Analytic Combinatorics is susceptible to many applications, not only within combinatorics itself, but, perhaps more importantly, within other areas of science where discrete probabilistic models recurrently surface, like statistical physics, computational biology, electrical engineering, and information theory. Last but not least, the analysis of algorithms and data structures in computer science has served and still serves as an important incentive for the development of the theory. ⋆⋆⋆⋆⋆⋆ Part A: Symbolic methods. This part specifically develops Symbolic methods, which constitute a unified algebraic theory dedicated to setting up functional relations between counting generating functions. As it turns out, a collection of general (and simple) theorems provide a systematic translation mechanism between combinatorial constructions and operations on generating functions. This translation process is a purely formal one. In fact, with regard to basic counting, two parallel frameworks coexist—one for unlabelled structures and ordinary generating functions, the other for labelled structures and exponential generating functions. Furthermore, within the theory, parameters of combinatorial configurations can be easily taken into account by adding supplementary variables. Three chapters then form Part A: Chapter I deals with unlabelled objects; Chapter II develops labelled objects in a parallel way; Chapter III treats multivariate aspects of the theory suitable for the analysis of parameters of combinatorial structures. ⋆⋆⋆⋆⋆⋆ Part B: Complex asymptotics. This part specifically expounds Complex asymptotics, which is a unified analytic theory dedicated to the process of extracting asymptotic information from counting generating functions. A collection of general (and simple) theorems now provide a systematic translation mechanism between generating functions and asymptotic forms of coefficients. Five chapters form this part. Chapter IV serves as an introduction to complex-analytic methods and proceeds with the treatment of meromorphic functions, that is, functions whose singularities are poles, rational functions being the simplest case. Chapter V develops applications of rational and meromorphic asymptotics of generating functions, with numerous applications related to words and languages, walks and graphs, as well as permutations. Chapter VI develops a general theory of singularity analysis that applies to a wide variety of singularity types, such as square-root or logarithmic, and has consequences regarding trees as well as other recursively-defined combinatorial classes. Chapter VII presents applications of singularity analysis to 2–regular graphs and polynomials, trees of various sorts, mappings, context-free languages, walks, and maps. It contains in particular a discussion of the analysis of coefficients of algebraic functions. Chapter VIII explores saddle-point methods, which are instrumental in analysing functions with a violent growth at a singularity, as well as many functions with a singularity only at infinity (i.e., entire functions). ⋆⋆⋆⋆⋆⋆

PREFACE

ix

Part C: Random structures. This part is comprised of Chapter IX, which is dedicated to the analysis of multivariate generating functions viewed as deformation and perturbation of simple (univariate) functions. Many known laws of probability theory, either discrete or continuous, from Poisson to Gaussian and stable distributions, are found to arise in combinatorics, by a process combining symbolic methods, complex asymptotics, and perturbation methods. As a consequence, many important characteristics of classical combinatorial structures can be precisely quantified in distribution. ⋆⋆⋆⋆⋆⋆ Part D: Appendices. Appendix A summarizes some key elementary concepts of combinatorics and asymptotics, with entries relative to asymptotic expansions, languages, and trees, among others. Appendix B recapitulates the necessary background in complex analysis. It may be viewed as a self-contained minicourse on the subject, with entries relative to analytic functions, the Gamma function, the implicit function theorem, and Mellin transforms. Appendix C recalls some of the basic notions of probability theory that are useful in analytic combinatorics. ⋆⋆⋆⋆⋆⋆ T HIS BOOK is meant to be reader-friendly. Each major method is abundantly illustrated by means of concrete Examples1 treated in detail—there are scores of them, spanning from a fraction of a page to several pages—offering a complete treatment of a specific problem. These are borrowed not only from combinatorics itself but also from neighbouring areas of science. With a view to addressing not only mathematicians of varied profiles but also scientists of other disciplines, Analytic Combinatorics is self-contained, including ample appendices that recapitulate the necessary background in combinatorics, complex function theory, and probability. A rich set of short Notes—there are more than 450 of them—are inserted in the text2 and can provide exercises meant for self-study or for student practice, as well as introductions to the vast body of literature that is available. We have also made every effort to focus on core ideas rather than technical details, supposing a certain amount of mathematical maturity but only basic prerequisites on the part of our gentle readers. The book is also meant to be strongly problem-oriented, and indeed it can be regarded as a manual, or even a huge algorithm, guiding the reader to the solution of a very large variety of problems regarding discrete mathematical models of varied origins. In this spirit, many of our developments connect nicely with computer algebra and symbolic manipulation systems. C OURSES can be (and indeed have been) based on the book in various ways. Chapters I–III on Symbolic methods serve as a systematic yet accessible introduction to the formal side of combinatorial enumeration. As such it organizes transparently some of the rich material found in treatises3 such as those of Bergeron– Labelle–Leroux, Comtet, Goulden–Jackson, and Stanley. Chapters IV–VIII relative to Complex asymptotics provide a large set of concrete examples illustrating the power 1Examples are marked by “Example · · · ”.

2Notes are indicated by

· · · .

3References are to be found in the bibliography section at the end of the book.

x

PREFACE

of classical complex analysis and of asymptotic analysis outside of their traditional range of applications. This material can thus be used in courses of either pure or applied mathematics, providing a wealth of non-classical examples. In addition, the quiet but ubiquitous presence of symbolic manipulation systems provides a number of illustrations of the power of these systems while making it possible to test and concretely experiment with a great many combinatorial models. Symbolic systems allow for instance for fast random generation, close examination of non-asymptotic regimes, efficient experimentation with analytic expansions and singularities, and so on. Our initial motivation when starting this project was to build a coherent set of methods useful in the analysis of algorithms, a domain of computer science now welldeveloped and presented in books by Knuth, Hofri, Mahmoud, and Szpankowski, in the survey by Vitter–Flajolet, as well as in our earlier Introduction to the Analysis of Algorithms published in 1996. This book, Analytic Combinatorics, can then be used as a systematic presentation of methods that have proved immensely useful in this area; see in particular the Art of Computer Programming by Knuth for background. Studies in statistical physics (van Rensburg, and others), statistics (e.g., David and Barton) and probability theory (e.g., Billingsley, Feller), mathematical logic (Burris’ book), analytic number theory (e.g., Tenenbaum), computational biology (Waterman’s textbook), as well as information theory (e.g., the books by Cover–Thomas, MacKay, and Szpankowski) point to many startling connections with yet other areas of science. The book may thus be useful as a supplementary reference on methods and applications in courses on statistics, probability theory, statistical physics, finite model theory, analytic number theory, information theory, computer algebra, complex analysis, or analysis of algorithms. Acknowledgements. This book would be substantially different and much less informative without Neil Sloane’s Encyclopedia of Integer Sequences, Steve Finch’s Mathematical Constants, Eric Weisstein’s MathWorld, and the MacTutor History of Mathematics site hosted at St Andrews. We have also greatly benefited of the existence of open on-line archives such as Numdam, Gallica, GDZ (digitalized mathematical documents), ArXiv, as well as the Euler Archive. All the corresponding sites are (or at least have been at some stage) freely available on the Internet. Bruno Salvy and Paul Zimmermann have developed algorithms and libraries for combinatorial structures and generating functions that are based on the M APLE system for symbolic computations and that have proven to be extremely useful. We are deeply grateful to the authors of the free software Unix, Linux, Emacs, X11, TEX and LATEX as well as to the designers of the symbolic manipulation system M APLE for creating an environment that has proved invaluable to us. We also thank students in courses at Barcelona, Berkeley (MSRI), Bordeaux, ´ ´ Caen, Graz, Paris (Ecole Polytechnique, Ecole Normale Sup´erieure, University), Princeton, Santiago de Chile, Udine, and Vienna whose reactions have greatly helped us prepare a better book. Thanks finally to numerous colleagues for their contributions to this book project. In particular, we wish to acknowledge the support, help, and interaction provided at a high level by members of the Analysis of Algorithms (AofA) community, with a special mention for Nico´ Fusy, Hsien-Kuei Hwang, Svante Janson, Don Knuth, Guy las Broutin, Michael Drmota, Eric Louchard, Andrew Odlyzko, Daniel Panario, Carine Pivoteau, Helmut Prodinger, Bruno Salvy, Mich`ele Soria, Wojtek Szpankowski, Brigitte Vall´ee, Mark Daniel Ward, and Mark Wilson. In addition, Ed Bender, Stan Burris, Philippe Dumas, Svante Janson, Philippe Robert, Lo¨ıc Turban, and Brigitte Vall´ee have provided insightful suggestions and generous feedback that have

PREFACE

xi

led us to revise the presentation of several sections of this book and correct many errors. We were also extremely lucky to work with David Tranah, the mathematics editor of Cambridge University Press, who has been an exceptionally supportive (and patient) companion of this book project, throughout all these years. Finally, support of our home institutions (INRIA and Princeton University) as well as various grants (French government, European Union, and NSF) have contributed to making our collaboration possible.

An Invitation to Analytic Combinatorics diä d summeignÔmena aÎt te prä aÍt kaÈ prä llhla tn poikilan âstÈn peira; © d deØ jewroÌ ggnesjai toÌ mèllonta perÈ fÔsew eÊkìti lìgú

— P LATO, The Timaeus1

A NALYTIC C OMBINATORICS is primarily a book about combinatorics, that is, the study of finite structures built according to a finite set of rules. Analytic in the title means that we concern ourselves with methods from mathematical analysis, in particular complex and asymptotic analysis. The two fields, combinatorial enumeration and complex analysis, are organized into a coherent set of methods for the first time in this book. Our broad objective is to discover how the continuous may help us to understand the discrete and to quantify its properties. C OMBINATORICS is, as told by its name, the science of combinations. Given basic rules for assembling simple components, what are the properties of the resulting objects? Here, our goal is to develop methods dedicated to quantitative properties of combinatorial structures. In other words, we want to measure things. Say that we have n different items like cards or balls of different colours. In how many ways can we lay them on a table, all in one row? You certainly recognize this counting problem—finding the number of permutations of n elements. The answer is of course the factorial number n ! = 1 · 2 · . . . · n.

This is a good start, and, equipped with patience or a calculator, we soon determine that if n = 31, say, then the number of permutations is the rather large quantity 31 ! = 8222838654177922817725562880000000, .

an integer with 34 decimal digits. The factorials solve an enumeration problem, one that took mankind some time to sort out, because the sense of the “· · · ” in the formula for n! is not that easily grasped. In his book The Art of Computer Programming 1“So their combinations with themselves and with each other give rise to endless complexities, which anyone who is to give a likely account of reality must survey.” Plato speaks of Platonic solids viewed as idealized primary constituents of the physical universe.

1

2

AN INVITATION TO ANALYTIC COMBINATORICS

3

ր

4

ց

1

ր

5

ց

2

5 4 3

2 1

Figure 0.1. An example of the correspondence between an alternating permutation (top) and a decreasing binary tree (bottom): each binary node has two descendants, which bear smaller labels. Such constructions, which give access to generating functions and eventually provide solutions to counting problems, are the main subject of Part A.

(vol III, p. 23), Donald Knuth traces the discovery to the Hebrew Book of Creation (c. AD 400) and the Indian classic Anuyogadv¯ara-sutra (c. AD 500). Here is another more subtle problem. Assume that you are interested in permutations such that the first element is smaller than the second, the second is larger than the third, itself smaller than the fourth, and so on. The permutations go up and down and they are diversely known as up-and-down or zigzag permutations, the more dignified name being alternating permutations. Say that n = 2m + 1 is odd. An example is for n = 9: 8 7 9 3 ր ց ր ց ր ց ր ց 4 6 5 1 2 The number of alternating permutations for n = 1, 3, 5, . . . , 15 turns out to be 1, 2, 16, 272, 7936, 353792, 22368256, 1903757312. What are these numbers and how do they relate to the total number of permutations of corresponding size? A glance at the corresponding figures, that is, 1!, 3!, 5!, . . . , 15!, or 1, 6, 120, 5040, 362880, 39916800, 6227020800, 1307674368000, suggests that the factorials grow somewhat faster—just compare the lengths of the last two displayed lines. But how and by how much? This is the prototypical question we are addressing in this book. Let us now examine the counting of alternating permutations. In 1881, the French mathematician D´esir´e Andr´e made a startling discovery. Look at the first terms of the Taylor expansion of the trigonometric function tan z: z3 z5 z7 z9 z 11 z + 2 + 16 + 272 + 7936 + 353792 + ··· . 1! 3! 5! 7! 9! 11! The counting sequence for alternating permutations, 1, 2, 16, . . ., curiously surfaces. We say that the function on the left is a generating function for the numerical sequence (precisely, a generating function of the exponential type, due to the presence of factorials in the denominators). tan z = 1

AN INVITATION TO ANALYTIC COMBINATORICS

3

Andr´e’s derivation may nowadays be viewed very simply as reflecting the construction of permutations by means of certain labelled binary trees (Figure 0.1 and p. 143): given a permutation σ a tree can be obtained once σ has been decomposed as a triple hσ L , max, σ R i, by taking the maximum element as the root, and appending, as left and right subtrees, the trees recursively constructed from σ L and σ R . Part A of this book develops at length symbolic methods by which the construction of the class T of all such trees, T = 1 ∪ (T , max , T ) , translates into an equation relating generating functions, Z z T (z) = z + T (w)2 dw. P

0

z n /n!

is the exponential generating function of the In this equation, T (z) := n Tn sequence (Tn ), where Tn is the number of alternating permutations of (odd) length n. There is a compelling formal analogy between the combinatorial specification and its generating function: Unions (∪) give rise to sums (+), max-placement gives an R integral ( ), forming a pair of trees corresponds to taking a square ([·]2 ). At this stage, we know that T (z) must solve the differential equation d T (z) = 1 + T (z)2 , dz

T (0) = 0,

which, by classical manipulations2, yields the explicit form T (z) = tan z. The generating function then provides a simple algorithm to compute the coefficients recurrently. Indeed, the formula, z− sin z tan z = = cos z 1−

z3 3! z2 2!

+

+

z5 5! z4 4!

− ···

− ···

,

implies, for n odd, the relation (extract the coefficient of z n in T (z) cos z = sin z) a! a n n = Tn−4 − · · · = (−1)(n−1)/2 , where Tn−2 + Tn − b 4 2 b!(a − b)! is the conventional notation for binomial coefficients. Now, the exact enumeration problem may be regarded as solved since a very simple algorithm is available for determining the counting sequence, while the generating function admits an explicit expression in terms of well-known mathematical objects. A NALYSIS, by which we mean mathematical analysis, is often described as the art and science of approximation. How fast do the factorial and the tangent number sequences grow? What about comparing their growths? These are typical problems of analysis. 2We have T ′ /(1 + T 2 ) = 1, hence arctan(T ) = z and T = tan z.

4

AN INVITATION TO ANALYTIC COMBINATORICS

4

2

K6

K4

K2

0

2

4

6

z

K2 K4

Figure 0.2. Two views of the function z 7→ tan z. Left: a plot for √ real values of z ∈ [−6, 6]. Right: the modulus | tan z| when z = x + i y (with i = −1) is assigned complex values in the square ±6 ± 6i. As developed at length in Part B, it is the nature of singularities in the complex domain that matters.

First, consider the number of permutations, n!. Quantifying its growth, as n gets large, takes us to the realm of asymptotic analysis. The way to express factorial numbers in terms of elementary functions is known as Stirling’s formula3 √ n! ∼ n n e−n 2π n,

where the ∼ sign means “approximately equal” (in the precise sense that the ratio of both terms tends to 1 as n gets large). This beautiful formula, associated with the name of the Scottish mathematician James Stirling (1692–1770), curiously involves both the basis e of natural logarithms and the perimeter 2π of the circle. Certainly, you cannot get such a thing without analysis. As a first step, there is an estimate Z n n n X , log j ∼ log n! = log x d x ∼ n log e 1 j=1

n n e−n

explaining at least the term, but already requiring a certain amount of elementary calculus. (Stirling’s formula precisely came a few decades after the fundamental bases of calculus had been laid by Newton and Leibniz.) Note the utility of Stirling’s formula: it tells us almost instantly that 100! has 158 digits, while 1000! borders the astronomical 102568 . We are now left with estimating the growth of the sequence of tangent numbers, Tn . The analysis leading to the derivation of the generating function tan(z) has been so far essentially algebraic or “formal”. Well, we can plot the graph of the tangent function, for real values of its argument and see that the function becomes infinite at the points ± π2 , ±3 π2 , and so on (Figure 0.2). Such points where a function ceases to be 3 In this book, we shall encounter five different proofs of Stirling’s formula, each of interest for its

own sake: (i) by singularity analysis of the Cayley tree function (p. 407); (ii) by singularity analysis of polylogarithms (p. 410); (iii) by the saddle-point method (p. 555); (iv) by Laplace’s method (p. 760); (v) by the Mellin transform method applied to the logarithm of the Gamma function (p. 766).

AN INVITATION TO ANALYTIC COMBINATORICS

5

“smooth” (differentiable) are called singularities. By methods amply developed in this book, it is the local nature of a generating function at its “dominant” singularities (i.e., the ones closest to the origin) that determines the asymptotic growth of the sequence of coefficients. From this perspective, the basic fact that tan z has dominant singularities at ± π2 enables us to reason as follows: first approximate the generating function tan z near its two dominant singularities, namely, tan(z)

∼

z→±π/2 π 2

8z ; − 4z 2

then extract coefficients of this approximation; finally, get in this way a valid approximation of coefficients: n+1 Tn 2 ∼ 2· (n odd). n! n→∞ π With present day technology, we also have available symbolic manipulation systems (also called “computer algebra” systems) and it is not difficult to verify the accuracy of our estimates. Here is a small pyramid for n = 3, 5, . . . , 21, 2 16 272 7936 353792 22368256 1903757312 209865342976 29088885112832 4951498053124096 (Tn )

1 15 27 1 793 5 35379 1 2236825 1 1903757 267 20986534 2434 290888851 04489 495149805 2966307 (Tn⋆ )

comparing the exact values of Tn against the approximations Tn⋆ , where (n odd) $ n+1 % 2 ⋆ , Tn := 2 · n! π and discrepant digits of the approximation are displayed in bold. For n = 21, the error is only of the order of one in a billion. Asymptotic analysis (p. 269) is in this case wonderfully accurate. In the foregoing discussion, we have played down a fact—one that is important. When investigating generating functions from an analytic standpoint, one should generally assign complex values to arguments not just real ones. It is singularities in the complex plane that matter and complex analysis is needed in drawing conclusions regarding the asymptotic form of coefficients of a generating function. Thus, a large portion of this book relies on a complex analysis technology, which starts to be developed in Part B dedicated to Complex asymptotics. This approach to combinatorial enumeration parallels what happened in the nineteenth century, when Riemann first recognized P the deep relation between complex analytic properties of the zeta function, ζ (s) := 1/n s , and the distribution of primes, eventually leading to the long-sought proof of the Prime Number Theorem by Hadamard and de la Vall´ee-Poussin in 1896. Fortunately, relatively elementary complex analysis suffices for our purposes, and we

6

AN INVITATION TO ANALYTIC COMBINATORICS

Figure 0.3. The collection of binary trees with n = 0, 1, 2, 3 binary nodes, with respective cardinalities 1, 1, 2, 5.

can include in this book a complete treatment of the fragment of the theory needed to develop the fundamentals of analytic combinatorics. Here is yet another example illustrating the close interplay between combinatorics and analysis. When discussing alternating permutations, we have enumerated binary trees bearing distinct integer labels that satisfy a constraint—to decrease along branches. What about the simpler problem of determining the number of possible shapes of binary trees? Let Cn be the number of binary trees that have n binary branching nodes, hence n + 1 “external nodes”. It is not hard to come up with an exhaustive listing for small values of n (Figure 0.3), from which we determine that C0 = 1,

C1 = 1,

C2 = 2,

C3 = 5,

C4 = 14,

C5 = 42.

These numbers are probably the most famous ones of combinatorics. They have come to be known as the Catalan numbers as a tribute to the Franco-Belgian mathematician Eug`ene Charles Catalan (1814–1894), but they already appear in the works of Euler and Segner in the second half of the eighteenth century (see p. 20). In his reference treatise Enumerative Combinatorics, Stanley, over 20 pages, lists a collection of some 66 different types of combinatorial structures that are enumerated by the Catalan numbers. First, one can write a combinatorial equation, very much in the style of what has been done earlier, but without labels: C

=

∪

2

(C, • , C) .

(Here, the 2–symbol represents an external node.) With symbolic methods, it is easy to see that the ordinary generating function of the Catalan numbers, defined as X C(z) := Cn z n , n≥0

satisfies an equation that is a direct reflection of the combinatorial definition, namely, C(z)

=

1

+

z C(z)2 .

This is a quadratic equation whose solution is √ 1 − 1 − 4z . C(z) = 2z

AN INVITATION TO ANALYTIC COMBINATORICS

7

3 0.55

0.5

2.5

0.45 2 0.4

0.35

1.5

0.3 1 0.25 -0.3

-0.2

-0.1

0

0.1

0.2

10

20

30

40

50

Figure 0.4. Left: the real values of the Catalan generating function, which has a square-root singularity at z = 14 . Right: the ratio Cn /(4n n −3/2 ) plotted together √ . with its asymptote at 1/ π = 0.56418. The correspondence between singularities and asymptotic forms of coefficients is the central theme of Part B.

Then, by means of Newton’s theorem relative to the expansion of (1 + x)α , one finds easily (x = −4z, α = 21 ) the closed form expression 2n 1 . Cn = n+1 n Stirling’s asymptotic formula now comes to the rescue: it implies 4n where Cn⋆ := √ . π n3 . This last approximation is quite usable4: it gives C1⋆ = 2.25 (whereas C1 = 1), which is off by a factor of 2, but the error drops to 10% already for n = 10, and it appears to be less than 1% for any n ≥ 100. A plot of the generating function C(z) in Figure 0.4 illustrates the fact that C(z) has a singularity at z = 41 as it ceases to be differentiable (its derivative becomes infinite). That singularity is quite different from a pole and for natural reasons it is known as a square-root singularity. As we shall see repeatedly, under suitable conditions in the complex plane, a square root singularity for a function at a point ρ invariably entails an asymptotic form ρ −n n −3/2 for its coefficients. More generally, it suffices to estimate a generating function near a singularity in order to deduce an asymptotic approximation of its coefficients. This correspondence is a major theme of the book, one that motivates the five central chapters (Chapters IV to VIII). A consequence of the complex analytic vision of combinatorics is the detection of universality phenomena in large random structures. (The term is originally borrowed from statistical physics and is nowadays finding increasing use in areas of mathematics such as probability theory.) By universality is meant here that many quantitative Cn ∼ Cn⋆

. 4We use α = d to represent a numerical approximation of the real α by the decimal d, with the last

digit of d being at most ±1 from its actual value.

8

AN INVITATION TO ANALYTIC COMBINATORICS

properties of combinatorial structures only depend on a few global features of their definitions, not on details. For instance a growth in the counting sequence of the form K · An n −3/2 , arising from a square-root singularity, will be shown to be universal across all varieties of trees determined by a finite set of allowed node degrees—this includes unary– binary trees, ternary trees, 0–11–13 trees, as well as many variations such as non-plane trees and labelled trees. Even though generating functions may become arbitrarily complicated—as in an algebraic function of a very high degree or even the solution to an infinite functional equation—it is still possible to extract with relative ease global asymptotic laws governing counting sequences. R ANDOMNESS is another ingredient in our story. How useful is it to determine, exactly or approximately, counts that may be so large as to require hundreds if not thousands of digits in order to be written down? Take again the example of alternating permutations. When estimating their number, we have indeed quantified the proportion of these among all permutations. In other words, we have been predicting the probability that a random permutation of some size n is alternating. Results of this sort are of interest in all branches of science. For instance, biologists routinely deal with genomic sequences of length 105 , and the interpretation of data requires developing enumerative or probabilistic models where the number of possibilities is of 5 the order of 410 . The language of probability theory then proves of great convenience when discussing characteristic parameters of discrete structures, since we can interpret exact or asymptotic enumeration results as saying something concrete about the likelihood of values that such parameters assume. Equally important of course are results from several areas of probability theory: as demonstrated in the last chapter of this book, such results merge extremely well with the analytic–combinatorial framework. Say we are now interested in runs in permutations. These are the longest fragments of a permutation that already appear in (increasing) sorted order. Here is a permutation with 4 runs, separated by vertical bars: 2 5 8 | 3 9 | 1 4 7 | 6. Runs naturally present in a permutation are for instance exploited by a sorting algorithm called “natural list mergesort”, which builds longer and longer runs, starting from the original ones and merging them until the permutation is eventually sorted. For our understanding of this algorithm, it is then of obvious interest to quantify how many runs a permutation is likely to have. Let Pn,k be the number of permutations of size n having k runs. Then, the problem is once more best approached by generating functions and one finds that the coefficient of u k z n inside the bivariate generating function, z3 1−u z2 u(u + 1) + u(u 2 + 4u + 1) + · · · , = 1 + zu + 2! 3! 1 − ue z(1−u) gives the desired numbers Pn,k /n!. (A simple way of establishing the last formula bases itself on the tree decomposition of permutations and on the symbolic method; the numbers Pn,k , whose importance seems to have been first recognized by Euler, P(z, u) ≡

AN INVITATION TO ANALYTIC COMBINATORICS

9

10

0.6 5

0.5 z 0

0.2

0.4

0.6

0.4 0.8

1

1.2

0

0.3 0.2 -5

0.1 0

0.2

0.4

0.6

0.8

1

-10

Figure 0.5. Left: A partial plot of the real values of the Eulerian generating function z 7→ P(z, u) for z ∈ [0, 54 ], illustrates the presence of a movable pole for A as u varies between 0 and 45 . Right: A suitable superposition of the histograms of the distribution of the number of runs, for n = 2, . . . , 60, reveals the convergence to a Gaussian distribution (p. 695). Part C relates systematically the analysis of such a collection of singular behaviours to limit distributions.

are related to the Eulerian numbers, p. 210.) From here, we can easily determine effectively the mean, variance, and even the higher moments of the number of runs that a random permutation has: it suffices to expand blindly, or even better with the help of a computer, the bivariate generating function above as u → 1: 1 z (2 − z) 1 z2 6 − 4 z + z2 1 + (u − 1) + (u − 1)2 + · · · . 1−z 2 (1 − z)2 2 (1 − z)3 When u = 1, we just enumerate all permutations: this is the constant term 1/(1 − z) equal to the exponential generating function of all permutations. The coefficient of the term u − 1 gives the generating function of the mean number of runs, the next one provides the second moment, and so on. In this way, we discover the expectation and standard deviation of the number of runs in a permutation of size n: r n+1 n+1 µn = , σn = . 2 12 Then, by easy analytic–probabilistic inequalities (Chebyshev inequalities) that otherwise form the basis of what is known as the second moment method, we learn that the distribution of the number of runs is concentrated around its mean: in all likelihood, if one takes a random permutation, the number of its runs is going to be very close to its mean. The effects of such quantitative laws are quite tangible. It suffices to draw a sample of one element for n = 30 to get, for instance: 13, 22, 29|12, 15, 23|8, 28|18|6, 26|4, 10, 16|1, 5, 27|3, 14, 17, 20|2, 21, 30|25|11, 19|9|7, 24.

For n = 30, the mean is 15 12 , and this sample comes rather close as it has 13 runs. We shall furthermore see in Chapter IX that even for moderately large permutations of size 10 000 and beyond, the probability for the number of observed runs to deviate

10

AN INVITATION TO ANALYTIC COMBINATORICS 2

y 1.5

1

0.5

0 0

0.05

0.1

0.15

0.2

0.25

0.3

Figure 0.6. Left: The bivariate generating function z 7→ C(z, u) enumerating binary trees by size and number of leaves exhibits consistently a square-root singularity, for several values of u. Right: a binary tree of size 300 drawn uniformly at random has 69 leaves. As shown in Part C, singularity perturbation properties are at the origin of many randomness properties of combinatorial structures.

by more than 10% from the mean is less than 10−65 . As witnessed by this example, much regularity accompanies properties of large combinatorial structures. More refined methods combine the observation of singularities with analytic results from probability theory (e.g., continuity theorems for characteristic functions). In the case of runs in permutations, the quantity P(z, u) viewed as a function of z when u is fixed appears to have a pole: this fact is suggested by Figure 0.5 [left]. Then we are confronted with a fairly regular deformation of the generating function of all permutations. A parameterized version (with parameter u) of singularity analysis then gives access to a description of the asymptotic behaviour of the Eulerian numbers Pn,k . This enables us to describe very precisely what goes on: in a random permutation of large size n, once it has been centred by its mean and scaled by its standard deviation, the distribution of the number of runs is asymptotically Gaussian; see Figure 0.5 [right]. A somewhat similar type of situation prevails for binary trees. Say we are interested in leaves (also sometimes figuratively known as “cherries”) in trees: these are binary nodes that are attached to two external nodes (2). Let Cn,k be the number of trees P of size n having k leaves. The bivariate generating function C(z, u) := n,k Cn,k z n u k encodes all the information relative to leaf statistics in random binary trees. A modification of previously seen symbolic arguments shows that C(z, u) still satisfies a quadratic equation resulting in the explicit form, p 1 − 1 − 4z + 4z 2 (1 − u) . C(z, u) = 2z This reduces to C(z) for u = 1, as it should, and the bivariate generating function C(z, u) is a deformation of C(z) as u varies. In fact, the network of curves of Figure 0.6 for several fixed values of u illustrates the presence of a smoothly varying square-root singularity (the aspect of each curve is similar to that of Figure 0.4). It is possible to analyse the perturbation induced by varying values of u, to the effect that

AN INVITATION TO ANALYTIC COMBINATORICS

11

Combinatorial structures

SYMBOLIC METHODS (Part A) Generating functions, OGF, EGF Chapters I, II

Multivariate generating functions, MGF Chapter III

COMPLEX ASYMPTOTICS (Part B) Singularity analysis Chapters IV, V, VI, VII Saddle−point method Chapter VIII

Exact counting

RANDOM STRUCTURES (Part C) Multivariate asymptotics and limit laws Chapter IX

Limit laws, large deviations

Asymptotic counting, moments of parameters

Figure 0.7. The logical structure of Analytic Combinatorics.

C(z, u) is of the global analytic type r 1−

z , ρ(u)

for some analytic ρ(u). The already evoked process of singularity analysis then shows that the probability generating function of the number of leaves in a tree of size n is of the rough form ρ(1) n (1 + o(1)) . ρ(u) This is known as a “quasi-powers” approximation. It resembles very much the probability generating function of a sum of n independent random variables, a situation that gives rise to the classical Central Limit Theorem of probability theory. Accordingly, one gets that the limit distribution of the number of leaves in a large random binary tree is Gaussian. In abstract terms, the deformation induced by the secondary parameter (here, the number of leaves, previously, the number of runs) is susceptible to a perturbation analysis, to the effect that a singularity gets smoothly displaced without changing its nature (here, a square root singularity, earlier a pole) and a limit law systematically results. Again some of the conclusions can be verified even by very small samples: the single tree of size 300 drawn at random and displayed in Figure 0.6 (right) has 69 leaves, whereas the expected value of this number . is = 75.375 and the standard deviation is a little over 4. In a large number of cases of which this one is typical, we find metric laws of combinatorial structures that govern large structures with high probability and eventually make them highly predictable. Such randomness properties form the subject of Part C of this book dedicated to random structures. As our earlier description implies, there is an extreme degree of

12

AN INVITATION TO ANALYTIC COMBINATORICS

generality in this analytic approach to combinatorial parameters, and after reading this book, the reader will be able to recognize by herself dozens of such cases at sight, and effortlessly establish the corresponding theorems. A RATHER ABSTRACT VIEW of combinatorics emerges from the previous discussion; see Figure 0.7. A combinatorial class, as regards its enumerative properties, can be viewed as a surface in four-dimensional real space: this is the graph of its generating function, considered as a function from the set C ∼ = R2 of complex numbers to itself, and is otherwise known as a Riemann surface. This surface has “cracks”, that is, singularities, which determine the asymptotic behaviour of the counting sequence. A combinatorial construction (such as those freely forming sequences, sets, and so on) can then be examined through the effect it has on singularities. In this way, seemingly different types of combinatorial structures appear to be subject to common laws governing not only counting but also finer characteristics of combinatorial structures. For the already discussed case of universality in tree enumerations, additional universal laws valid across many tree varieties constrain for instance height (which, with high probability, is proportional to the square root of size) and the number of leaves (which is invariably normal in the asymptotic limit). What happens regarding probabilistic properties of combinatorial parameters is this. A parameter of a combinatorial class is fully determined by a bivariate generating function, which is a deformation of the basic counting generating function of the class (in the sense that setting the secondary variable u to 1 erases the information relative to the parameter and leads back to the univariate counting generating function). Then, the asymptotic distribution of a parameter of interest is characterized by a collection of surfaces, each having its own singularities. The way the singularities’ locations move or their nature changes under deformation encodes all the necessary information regarding the distribution of the parameter under consideration. Limit laws for combinatorial parameters can then be obtained and the corresponding phenomena can be organized into broad categories, called schemas. It would be inconceivable to attain such a far-reaching classification of metric properties of combinatorial structures by elementary real analysis alone. Objects on which we are going to inflict the treatments just described include many of the most important ones of discrete mathematics, as well as the ones that surface recurrently in several branches of the applied sciences. We shall thus encounter words and sequences, trees and lattice paths, graphs of various sorts, mappings, allocations, permutations, integer partitions and compositions, polyominoes and planar maps, to name but a few. In most cases, their principal characteristics will be finely quantified by the methods of analytic combinatorics. This book indeed develops a coherent theory of random combinatorial structures based on a powerful analytic methodology. Literally dozens of quite diverse combinatorial types can then be treated by a logically transparent chain. You will not find ready-made answers to all questions in this book, but, hopefully, methods that can be successfully used to address a great many of them. Bienvenue! Welcome!

Part A

SYMBOLIC METHODS

I

Combinatorial Structures and Ordinary Generating Functions Laplace discovered the remarkable correspondence between set theoretic operations and operations on formal power series and put it to great use to solve a variety of combinatorial problems. — G IAN –C ARLO ROTA [518]

I. 1. I. 2. I. 3. I. 4. I. 5. I. 6. I. 7.

Symbolic enumeration methods Admissible constructions and specifications Integer compositions and partitions Words and regular languages Tree structures Additional constructions Perspective

16 24 39 49 64 83 92

This chapter and the next are devoted to enumeration, where the problem is to determine the number of combinatorial configurations described by finite rules, and do so for all possible sizes. For instance, how many different words are there of length 17? Of length n, for general n? These questions are easy, but what if some constraints are imposed, e.g., no four identical elements in a row? The solutions are exactly encoded by generating functions, and, as we shall see, generating functions are the central mathematical object of combinatorial analysis. We examine here a framework that, contrary to traditional treatments based on recurrences, explains the surprising efficiency of generating functions in the solution of combinatorial enumeration problems. This chapter serves to introduce the symbolic approach to combinatorial enumerations. The principle is that many general set-theoretic constructions admit a direct translation as operations over generating functions. This principle is made concrete by means of a dictionary that includes a collection of core constructions, namely the operations of union, cartesian product, sequence, set, multiset, and cycle. Supplementary operations such as pointing and substitution can also be similarly translated. In this way, a language describing elementary combinatorial classes is defined. The problem of enumerating a class of combinatorial structures then simply reduces to finding a proper specification, a sort of computer program for the class expressed in terms of the basic constructions. The translation into generating functions becomes, after this, a purely mechanical symbolic process. We show here how to describe in such a context integer partitions and compositions, as well as many word and tree enumeration problems, by means of ordinary 15

16

I. COMBINATORIAL STRUCTURES AND ORDINARY GENERATING FUNCTIONS

generating functions. A parallel approach, developed in Chapter II, applies to labelled objects—in contrast the plain structures considered in this chapter are called unlabelled. The methodology is susceptible to multivariate extensions with which many characteristic parameters of combinatorial objects can also be analysed in a unified manner: this is to be examined in Chapter III. The symbolic method also has the great merit of connecting nicely with complex asymptotic methods that exploit analyticity properties and singularities, to the effect that precise asymptotic estimates are usually available whenever the symbolic method applies—a systematic treatment of these aspects forms the basis of Part B of this book Complex asymptotics (Chapters IV–VIII). I. 1. Symbolic enumeration methods First and foremost, combinatorics deals with discrete objects, that is, objects that can be finitely described by construction rules. Examples are words, trees, graphs, permutations, allocations, functions from a finite set into itself, topological configurations, and so on. A major question is to enumerate such objects according to some characteristic parameter(s). Definition I.1. A combinatorial class, or simply a class, is a finite or denumerable set on which a size function is defined, satisfying the following conditions: (i) the size of an element is a non-negative integer; (ii) the number of elements of any given size is finite. If A is a class, the size of an element α ∈ A is denoted by |α|, or |α|A in the few cases where the underlying class needs to be made explicit. Given a class A, we consistently denote by An the set of objects in A that have size n and use the same group of letters for the counts An = card(An ) (alternatively, also an = card(An )). An axiomatic presentation is then as follows: a combinatorial class is a pair (A, | · |) where A is at most denumerable and the mapping | · | ∈ (A 7→ Z≥0 ) is such that the inverse image of any integer is finite. Definition I.2. The counting sequence of a combinatorial class is the sequence of integers (An )n≥0 where An = card(An ) is the number of objects in class A that have size n. Example I.1. Binary words. Consider first the set W of binary words, which are sequences of elements taken from the binary alphabet A = {0,1}, W := {ε, 0, 1, 00, 01, 10, 11, 000, 001, 010, . . . , 1001101, . . . }, with ε the empty word. Define size to be the number of letters that a word comprises. There are two possibilities for each letter and possibilities multiply, so that the counting sequence (Wn ) satisfies Wn = 2n . (This sequence has a well-known legend associated with the invention of the game of chess: the inventor was promised by his king one grain of rice for the first square of the chessboard, two for the second, four for the third, and so on. The king naturally could not deliver the promised 264 − 1 grains!) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

I. 1. SYMBOLIC ENUMERATION METHODS

17

Figure I.1. The collection T of all triangulations of regular polygons (with size defined as the number of triangles) is a combinatorial class, whose counting sequence starts as T0 = 1, T1 = 1, T2 = 2, T3 = 5, T4 = 14, T5 = 42. Example I.2. Permutations. A permutation of size n is by definition a bijective mapping of the integer interval1 In := [1 . . n]. It is thus representable by an array, 1 2 n σ1 σ2 · · · σn , or equivalently by the sequence σ1 σ2 · · · σn of its distinct elements. The set P of permutations is P = {. . . , 12, 21, 123, 132, 213, 231, 312, 321, 1234, . . . , 532614, . . . }, For a permutation written as a sequence of n distinct numbers, there are n places where one can accommodate n, then n − 1 remaining places for n − 1, and so on. Therefore, the number Pn of permutations of size n satisfies Pn = n! = 1 · 2 · . . . · n . As indicated in our Invitation chapter (p. 2), this formula has been known for at least fifteen centuries. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Example I.3. Triangulations. The class T of triangulations comprises triangulations of convex polygonal domains which are decompositions into non-overlapping triangles (taken up to smooth deformations of the plane). We define the size of a triangulation to be the number of triangles it is composed of. For instance, a convex quadrilateral ABC D can be decomposed into two triangles in two ways (by means of either the diagonal AC or the diagonal B D); similarly, there are five different ways to dissect a convex pentagon into three triangles: see Figure I.1. Agreeing that T0 = 1, we then find T0 = 1,

T1 = 1,

T2 = 2,

T3 = 5,

T4 = 14,

T5 = 42.

It is a non-trivial combinatorial result due to Euler and Segner [146, 196, 197] around 1750 that the number Tn of triangulations is 1 (2n)! 2n (1) Tn = = , n+1 n (n + 1)! n! a central quantity of combinatorial analysis known as a Catalan number: see our Invitation, p. 7, the historical synopsis on p. 20, the discussion on p. 35, and Subsection I. 5.3, p. 73. 1We borrow from computer science the convenient practice of denoting an integer interval by 1 . . n or

[1 . . n], whereas [0, n] represents a real interval.

18

I. COMBINATORIAL STRUCTURES AND ORDINARY GENERATING FUNCTIONS

Following Euler [196], the counting of triangulations is best approached by generating functions: see again Figure I.2, p. 20 for historical context. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Although the previous three examples are simple enough, it is generally a good idea, when confronted with a combinatorial enumeration problem, to determine the initial values of counting sequences, either by hand or better with the help of a computer, somehow. Here, we find:

(2)

n

0

1

2

3

4

5

6

7

8

9

10

Wn Pn Tn

1 1 1

2 1 1

4 2 2

8 6 5

16 24 14

32 120 42

64 720 132

128 5040 429

256 40320 1430

512 362880 4862

1024 3628800 16796

Such an experimental approach may greatly help identify sequences. For instance, had we not known the formula (1) for triangulations, observing unusual factorizations such as T40 = 22 · 5 · 72 · 11 · 23 · 43 · 47 · 53 · 59 · 61 · 67 · 71 · 73 · 79, which contains all prime numbers from 43 to 79 and no prime larger than 80, would quickly put us on the track of the right formula. There even exists nowadays a huge On-line Encyclopedia of Integer Sequences (EIS) due to Sloane that is available in electronic form [543] (see also an earlier book by Sloane and Plouffe [544]) and contains more than 100 000 sequences. Indeed, the three sequences (Wn ), (Pn ), and (Tn ) are respectively identified2 as EIS A000079, EIS A000142, and EIS A000108.

I.1. Necklaces. How many different types of necklace designs can you form with n beads, each having one of two colours, ◦ and •, where it is postulated that orientation matters? Here are the possibilities for n = 1, 2, 3, . This is equivalent to enumerating circular arrangements of two letters and an exhaustive listing program can be based on the smallest lexicographical representation of each word, as suggested by (20), p. 26. The counting sequence starts as 2, 3, 4, 6, 8, 14, 20, 36, 60, 108, 188, 352 and constitutes EIS A000031. [An explicit formula appears later in this chapter (p. 64).] What if two necklace designs that are mirror images of one another are identified?

I.2. Unimodal permutations. Such a permutation has exactly one local maximum. In other words it is of the form σ1 · · · σn with σ1 < σ2 < · · · < σk = n and σk = n > σk+1 > · · · > σn , for some k ≥ 1. How many such permutations are there of size n? For n = 5, the number is 16: the permutations are 12345, 12354, 12453, 12543, 13452, 13542, 14532 and 15432 and their reversals. [Due to Jon Perry, see EIS A000079.]

It is also of interest to note that words and permutations may be enumerated using the most elementary counting principles, namely, for finite sets B and C card(B ∪ C) = card(B) + card(C) (provided B ∩ C = ∅) (3) card(B × C) = card(B) · card(C).

2 Throughout this book, a reference such as EIS Axxx points to Sloane’s Encyclopedia of Integer

Sequences [543]. The database contains more than 100 000 entries.

I. 1. SYMBOLIC ENUMERATION METHODS

19

We shall see soon that these principles, which lie at the basis of our very concept of number, admit a powerful generalization (Equation (19), p. 23, below). Next, for combinatorial enumeration purposes, it proves convenient to identify combinatorial classes that are merely variants of one another. Definition I.3. Two combinatorial classes A and B are said to be (combinatorially) isomorphic, which is written A ∼ = B, iff their counting sequences are identical. This condition is equivalent to the existence of a bijection from A to B that preserves size, and one also says that A and B are bijectively equivalent. We normally identify isomorphic classes and accordingly employ a plain equality sign (A = B). We then confine the notation A ∼ = B to stress cases where combinatorial isomorphism results from some non-trivial transformation. Definition I.4. The ordinary generating function (OGF) of a sequence (An ) is the formal power series (7)

A(z) =

∞ X

An z n .

n=0

The ordinary generating function (OGF) of a combinatorial class A is the generating function of the numbers An = card(An ). Equivalently, the OGF of class A admits the combinatorial form X (8) A(z) = z |α| . α∈A

It is also said that the variable z marks size in the generating function. The combinatorial form of an OGF in (8) results straightforwardly from observing that the term z n occurs as many times as there are objects in A having size n. We stress the fact that, at this stage and throughout Part A, generating functions are manipulated algebraically as formal sums; that is, they are considered as formal power series (see the framework of Appendix A.5: Formal power series, p. 730) Naming convention. We adhere to a systematic naming convention: classes, their counting sequences, and their generating functions are systematically denoted by the same groups of letters: for instance, A for a class, {An } (or {an }) for the counting sequence, and A(z) (or a(z)) for its OGF. Coefficient extraction. We let generally [z n ] f (z) denote of extractPthe operation ing the coefficient of z n in the formal power series f (z) = f n z n , so that (9)

X [z n ] fn zn = fn . n≥0

(The coefficient extractor [z n ] f (z) reads as “coefficient of z n in f (z)”.)

20

I. COMBINATORIAL STRUCTURES AND ORDINARY GENERATING FUNCTIONS

1. On September 4, 1751, Euler writes to his friend Goldbach [196]: Ich bin neulich auf eine Betrachtung gefallen, welche mir nicht wenig merkw¨urdig vorkam. Dieselbe betrifft, auf wie vielerley Arten ein gegebenes polygonum durch Diagonallinien in triangula zerchnitten werden k¨onne.

I have recently encountered a question, which appears to me rather noteworthy. It concerns the number of ways in which a given [convex] polygon can be decomposed into triangles by diagonal lines.

Euler then describes the problem (for an n–gon, i.e., (n − 2) triangles) and concludes: Setze ich nun die Anzahl dieser verschiedenen Arten = x [. . . ]. Hieraus habe ich nun den Schluss gemacht, dass generaliter sey 2.6.10.14....(4n − 10) x= 2.3.4.5....(n − 1) [. . . ] Ueber die Progression der Zahlen 1, 2, 5, 14, 42, 132, etc. habe ich auch diese Eigenschaft angemerket, dass 1 + 2a +√5a 2 +

1−4a . 14a 3 + 42a 4 + 132a 5 + etc. = 1−2a− 2aa

Let me now denote by x this number of ways [. . . ]. I have then reached the conclusion that in all generality 2.6.10.14....(4n − 10) x= 2.3.4.5....(n − 1) [. . . ] Regarding the progression of the numbers 1, 2, 5, 14, 42, 132, and so on, I have also observed the following property: 1 + 2a +√5a 2 +

1−4a . 14a 3 + 42a 4 + 132a 5 + etc. = 1−2a− 2aa

Thus, as early as 1751, Euler knew the solution as well as the associated generating function. From his writing, it is however unclear whether he had found complete proofs. 2. In the course of the 1750s, Euler communicated the problem, together with initial elements of the counting sequence, to Segner, who writes in his publication [146] dated 1758: “The great Euler has benevolently communicated these numbers to me; the way in which he found them, and the law of their progression having remained hidden to me” [“quos numeros mecum beneuolus communicauit summus Eulerus; modo, quo eos reperit, atque progressionis ordine, celatis”]. Segner develops a recurrence approach to Catalan numbers. By a root decomposition analogous to ours, on p. 35, he proves (in our notation, for decompositions into n triangles) (4)

Tn =

n−1 X k=0

Tk Tn−1−k ,

T0 = 1,

a recurrence by which the Catalan numbers can be computed to any desired order. (Segner’s work was to be reviewed in [197], anonymously, but most probably, by Euler.) 3. During the 1830s, Liouville circulated the problem and wrote to Lam´e, who answered the next day(!) with a proof [399] based on recurrences similar to (4) of the explicit expression: 2n 1 . (5) Tn = n+1 n Interestingly enough, Lam´e’s three-page note [399] appeared in the 1838 issue of the Journal de math´ematiques pures et appliqu´ees (“Journal de Liouville”), immediately followed by a longer study by Catalan [106], who also observed that the Tn intervene in the number of ways of multiplying n numbers (this book, §I. 5.3, p. 73). Catalan would then return to these problems [107, 108], and the numbers 1, 1, 2, 5, 14, 42, . . . eventually became known as the Catalan numbers. In [107], Catalan finally proves the validity of Euler’s generating function: √ X 1 − 1 − 4z Tn z n = (6) T (z) := . 2z n 4. Nowadays, symbolic methods directly yield the generating function (6), from which both the recurrence (4) and the explicit form (5) follow easily; see pp. 6 and 35. Figure I.2. The prehistory of Catalan numbers.

I. 1. SYMBOLIC ENUMERATION METHODS

21

N HC

CH 3

CH C

HC

N CH CH 2

HC

H⇒

C10 H14 N2

;

z 26

CH 2

H 2C

Figure I.3. A molecule, methylpyrrolidinyl-pyridine (nicotine), is a complex assembly whose description can be reduced to a single formula corresponding here to a total of 26 atoms.

The OGFs corresponding to our three examples W, P, T are then ∞ X 1 W (z) = 2n z n = 1 − 2z n=0 ∞ X n! z n P(z) = (10) n=0 √ ∞ X 1 1 − 1 − 4z 2n n T (z) = z = . n+1 n 2z n=0

The first expression relative to W (z) is immediate as it is the sum of a geometric progression. The second generating function P(z) is not clearly related to simple functions of analysis. (Note that the expression still makes sense within the strict framework of formal power series.) The third expression relative to T (z) is equivalent to the explicit form of Tn via Newton’s expansion of (1 + x)1/2 (pp. 7 and 35 as well as Figure I.2). The OGFs W (z) and T (z) can then be interpreted as standard analytic objects, upon assigning values in the complex domain C to the formal variable z. In effect, the series W (z) and T (z) converge in a neighbourhood of 0 and represent complex functions that are well defined near the origin, namely when |z| < 12 for W (z) and |z| < 14 for T (z). The OGF P(z) is a purely formal power series (its radius of convergence is 0) that can nonetheless be subjected to the usual algebraic operations of power series. (Permutation enumeration is most conveniently approached by the exponential generating functions developed in Chapter II.) Combinatorial form of generating functions (GFs). The combinatorial form (8) shows that generating functions are nothing but a reduced representation of the combinatorial class, where internal structures are destroyed and elements contributing to size (atoms) are replaced by the variable z. In a sense, this is analogous to what chemists do by writing linear reduced (“molecular”) formulae for complex molecules (Figure I.3). Great use of this observation was made by Sch¨utzenberger as early as the 1950s and 1960s. It explains the many formal similarities that are observed between combinatorial structures and generating functions.

22

I. COMBINATORIAL STRUCTURES AND ORDINARY GENERATING FUNCTIONS

H=

H (z) =

zzzz

zz

zzz

zzzz

z

zzzz

zzz

+ z4

+ z2

+ z3

+ z4

+z

+ z4

+ z3

z + z 2 + 2z 3 + 3z 4

Figure I.4. A finite family of graphs and its eventual reduction to a generating function.

Figure I.4 provides a combinatorial illustration: start with a (finite) family of graphs H, with size taken as the number of vertices. Each vertex in each graph is replaced by the variable z and the graph structure is “forgotten”; then the monomials corresponding to each graph are formed and the generating function is finally obtained by gathering all the monomials. For instance, there are 3 graphs of size 4 in H, in agreement with the fact that [z 4 ]H (z) = 3. If size had been instead defined by number of edges, another generating function would have resulted, namely, with y marking the new size: 1+ y + y 2 +2y 3 + y 4 + y 6 . If both number of vertices and number of edges are of interest, then a bivariate generating function is obtained: H (z, y) = z+z 2 y+z 3 y 2 +z 3 y 3 +z 4 y 3 +z 4 y 4 +z 4 y 6 ; such multivariate generating functions are developed systematically in Chapter III. A path often taken in the literature is to decompose the structures to be enumerated into smaller structures either of the same type or of simpler types, and then extract from such a decomposition recurrence relations that are satisfied by the {An }. In this context, the recurrence relations are either solved directly—whenever they are simple enough—or by means of ad hoc generating functions, introduced as mere technical artifices. By contrast, in the framework of this book, classes of combinatorial structures are built directly in terms of simpler classes by means of a collection of elementary combinatorial constructions. This closely resembles the description of formal languages by means of grammars, as well as the construction of structured data types in programming languages. The approach developed here has been termed symbolic, as it relies on a formal specification language for combinatorial structures. Specifically, it is based on so–called admissible constructions that permit direct translations into generating functions. Definition I.5. Let 8 be an m–ary construction that associates to any collection of classes B (1) , . . . B (m) a new class A = 8[B (1) , . . . , B (m) ]. The construction 8 is admissible iff the counting sequence (An ) of A only depends on (1) (m) the counting sequences (Bn ), . . . , (Bn ) of B (1) , . . . , B (m) .

I. 1. SYMBOLIC ENUMERATION METHODS

23

For such an admissible construction, there then exists a well-defined operator 9 acting on the corresponding ordinary generating functions: A(z) = 9[B (1) (z), . . . , B (m) ], and it is this basic fact about admissibility that will be used throughout the book. As an introductory example, take the construction of cartesian product, which is the usual one enriched with a natural notion of size. Definition I.6. The cartesian product construction applied to two classes B and C forms ordered pairs, (11)

A=B×C

iff A = {α = (β, γ ) | β ∈ B, γ ∈ C },

with the size of a pair α = (β, γ ) being defined by (12)

|α|A = |β|B + |γ |C .

By considering all possibilities, it is immediately seen that the counting sequences corresponding to A, B, C are related by the convolution relation (13)

An =

n X

Bk Cn−k ,

k=0

which means admissibility. Furthermore, we recognize here the formula for a product of two power series: (14)

A(z) = B(z) · C(z).

In summary: the cartesian product is admissible and it translates as a product of OGFs. Similarly, let A, B, C be combinatorial classes satisfying (15)

A = B ∪ C,

with B ∩ C = ∅,

with size defined in a consistent manner: for ω ∈ A, |ω| B if ω ∈ B (16) |ω|A = |ω| if ω ∈ C. C

One has (17)

An = Bn + Cn ,

which, at generating function level, means (18)

A(z) = B(z) + C(z).

Thus, the union of disjoint sets is admissible and it translates as a sum of generating functions. (A more formal version of this statement is given in the next section.) The correspondences provided by (11)–(14) and (15)–(18) are summarized by the strikingly simple dictionary A = B ∪ C H⇒ A(z) = B(z) + C(z) (provided B ∩ C = ∅) (19) A = B × C H⇒ A(z) = B(z) · C(z),

24

I. COMBINATORIAL STRUCTURES AND ORDINARY GENERATING FUNCTIONS

to be compared with the plain arithmetic case of (3), p. 18. The merit of such relations is that they can be stated as general purpose translation rules that only need to be established once and for all. As soon as the problem of counting elements of a union of disjoint sets or a cartesian product is recognized, it becomes possible to dispense altogether with the intermediate stages of writing explicitly coefficient relations or recurrences as in (13) or (17). This is the spirit of the symbolic method for combinatorial enumerations. Its interest lies in the fact that several powerful set-theoretic constructions are amenable to such a treatment, as we see in the next section.

I.3. Continuity, Lipschitz and H¨older conditions. An admissible construction is said to be continuous if it is a continuous function on the space of formal power series equipped with its standard ultrametric distance (Appendix A.5: Formal power series, p. 730). Continuity captures the desirable property that constructions depend on their arguments in a finitary way. For all the constructions of this book, there furthermore exists a function ϑ(n), such that (An ) only (1) (m) depends on the first ϑ(n) elements of the (Bk ), . . . , (Bk ), with ϑ(n) ≤ K n + L (H¨older condition) or ϑ(n) ≤ n + L (Lipschitz condition). For instance, the functional f (z) 7→ f (z 2 ) is H¨older; the functional f (z) 7→ ∂z f (z) is Lipschitz. I. 2. Admissible constructions and specifications The main goal of this section is to introduce formally the basic constructions that constitute the core of a specification language for combinatorial structures. This core is based on disjoint unions, also known as combinatorial sums, and on cartesian products that we have just discussed. We shall augment it by the constructions of sequence, cycle, multiset, and powerset. A class is constructible or specifiable if it can be defined from primal elements by means of these constructions. The generating function of any such class satisfies functional equations that can be transcribed systematically from a specification; see Theorems I.1 (p. 27) and I.2 (p. 33), as well as Figure I.18 (p. 93) at the end of this chapter for a summary. I. 2.1. Basic constructions. First, we assume we are given a class E called the neutral class that consists of a single object of size 0; any such object of size 0 is called a neutral object and is usually denoted by symbols such as ǫ or 1. The reason for this terminology becomes clear if one considers the combinatorial isomorphism A∼ = A × E. =E ×A∼ We also assume as given an atomic class Z comprising a single element of size 1; any such element is called an atom; an atom may be used to describe a generic node in a tree or graph, in which case it may be represented by a circle (• or ◦), but also a generic letter in a word, in which case it may be instantiated as a, b, c, . . . . Distinct copies of the neutral or atomic class may also be subscripted by indices in various ways. Thus, for instance, we may use the classes Za = {a}, Zb = {b} (with a, b of size 1) to build up binary words over the alphabet {a, b}, or Z• = {•}, Z◦ = {◦} (with •, ◦ taken to be of size 1) to build trees with nodes of two colours. Similarly, we may introduce E2 , E1 , E2 to denote a class comprising the neutral objects 2, ǫ1 , ǫ2 respectively. Clearly, the generating functions of a neutral class E and an atomic class Z are E(z) = 1,

Z (z) = z,

I. 2. ADMISSIBLE CONSTRUCTIONS AND SPECIFICATIONS

25

corresponding to the unit 1, and the variable z, of generating functions. Combinatorial sum (disjoint union). The intent of combinatorial sum also known as disjoint union is to capture the idea of a union of disjoint sets, but without any extraneous condition (disjointness) being imposed on the arguments of the construction. To do so, we formalize the (combinatorial) sum of two classes B and C as the union (in the standard set-theoretic sense) of two disjoint copies, say B 2 and C 3 , of B and C. A picturesque way to view the construction is as follows: first choose two distinct colours and repaint the elements of B with the first colour and the elements of C with the second colour. This is made precise by introducing two distinct “markers”, say 2 and 3, each a neutral object (i.e., of size zero); the disjoint union B + C of B, C is then defined as a standard set-theoretic union: B + C := ({2} × B) ∪ ({3} × C) .

The size of an object in a disjoint union A = B + C is by definition inherited from its size in its class of origin, as in Equation (16). One good reason behind the definition adopted here is that the combinatorial sum of two classes is always well defined, no matter whether or not the classes intersect. Furthermore, disjoint union is equivalent to a standard union whenever it is applied to disjoint sets. Because of disjointness of the copies, one has the implication A=B+C

H⇒

An = Bn + Cn

and

A(z) = B(z) + C(z),

so that disjoint union is admissible. Note that, in contrast, standard set-theoretic union is not an admissible construction since card(Bn ∪ Cn ) = card(Bn ) + card(Cn ) − card(Bn ∩ Cn ),

and information on the internal structure of B and C (i.e., the nature of their intersection) is needed in order to be able to enumerate the elements of their union. Cartesian product. This construction A = B ×C forms all possible ordered pairs in accordance with Definition I.6. The size of a pair is obtained additively from the size of components in accordance with (12). Next, we introduce a few fundamental constructions that build upon set-theoretic union and product, and form sequences, sets, and cycles. These powerful constructions suffice to define a broad variety of combinatorial structures. Sequence construction. If B is a class then the sequence class S EQ(B) is defined as the infinite sum S EQ(B) = {ǫ} + B + (B × B) + (B × B × B) + · · · with ǫ being a neutral structure (of size 0). In other words, we have A = (β1 , . . . , βℓ ) ℓ ≥ 0, β j ∈ B ,

which matches our intuition as to what sequences should be. (The neutral structure in this context corresponds to ℓ = 0; it plays a rˆole similar to that of the “empty” word in formal language theory.) It is then readily checked that the construction A = S EQ(B) defines a proper class satisfying the finiteness condition for sizes if and only if B contains no object of size 0. From the definition of size for sums and products, it

26

I. COMBINATORIAL STRUCTURES AND ORDINARY GENERATING FUNCTIONS

follows that the size of an object α ∈ A is to be taken as the sum of the sizes of its components: α = (β1 , . . . , βℓ )

H⇒

|α| = |β1 | + · · · + |βℓ |.

Cycle construction. Sequences taken up to a circular shift of their components define cycles, the notation being C YC(B). In precise terms, one has3 C YC(B) := (S EQ(B) \ {ǫ}) /S, where S is the equivalence relation between sequences defined by (β1 , . . . , βr ) S (β1′ , . . . , βr′ ) iff there exists some circular shift τ of [1 . . r ] such that for all j, β ′j = βτ ( j) ; in other words, for some d, one has β ′j = β1+( j−1+d) mod r . Here is, for instance, a depiction of the cycles formed from the 8 and 16 sequences of lengths 3 and 4 over two types of objects (a, b): the number of cycles is 4 (for n = 3) and 6 (for n = 4). Sequences are grouped into equivalence classes according to the relation S: (20)

3–cycles :

(

aaa aab aba baa abb bba bab , bbb

aaaa aaab aaba abaa baaa aabb abba bbaa baab 4–cycles : . abab baba abbb bbba bbab babb bbbb

According to the definition, this construction corresponds to the formation of directed cycles (see also the necklaces of Note I.1, p. 18). We make only a limited use of it for unlabelled objects; however, its counterpart plays a rather important rˆole in the context of labelled structures and exponential generating functions of Chapter II. Multiset construction. Following common mathematical terminology, multisets are like finite sets (that is the order between elements does not count), but arbitrary repetitions of elements are allowed. The notation is A = MS ET(B) when A is obtained by forming all finite multisets of elements from B. The precise way of defining MS ET(B) is as a quotient: MS ET(B) := S EQ(B)/R with R, the equivalence relation of sequences being defined by (α1 , . . . , αr ) R (β1 , . . . , βr ) iff there exists some arbitrary permutation σ of [1 . . r ] such that for all j, β j = ασ ( j) . Powerset construction. The powerset class (or set class) A = PS ET(B) is defined as the class consisting of all finite subsets of class B, or equivalently, as the class PS ET(B) ⊂ MS ET(B) formed of multisets that involve no repetitions. We again need to make explicit the way the size function is defined when such constructions are performed: as for products and sequences, the size of a composite object—set, multiset, or cycle—is defined to be the sum of the sizes of its components.

I.4. The semi-ring of combinatorial classes. Under the convention of identifying isomorphic classes, sum and product acquire pleasant algebraic properties: combinatorial sums and cartesian products become commutative and associative operations, e.g., (A + B) + C = A + (B + C), A × (B × C) = (A × B) × C, while distributivity holds, (A + B) × C = (A × C) + (B × C). 3By convention, there are no “empty” cycles.

I. 2. ADMISSIBLE CONSTRUCTIONS AND SPECIFICATIONS

27

I.5. Natural numbers. Let Z := {•} with • an atom (of size 1). Then I = S EQ(Z) \

{ǫ} is a way of describing positive integers in unary notation: I = {•, • •, •••, . . .}. The corresponding OGF is I (z) = z/(1 − z) = z + z 2 + z 3 + · · · .

I.6. Interval coverings. Let Z := {•} be as before. Then A = Z + (Z × Z) is a set of two elements, • and (•, •), which we choose to draw as {•, •–•}. Then C = S EQ(A) contains •, • •, •–•, • •–•, •–• •, •–• •–•, • • • •, . . .

With the notion of size adopted, the objects of size n in C = S EQ(Z +(Z ×Z)) are (isomorphic to) the coverings of [0, n] by intervals (matches) of length either 1 or 2. The OGF C(z) = 1 + z + 2 z 2 + 3 z 3 + 5 z 4 + 8 z 5 + 13 z 6 + 21 z 7 + 34 z 8 + 55 z 9 + · · · ,

is, as we shall see shortly (p. 42), the OGF of Fibonacci numbers.

I. 2.2. The admissibility theorem for ordinary generating functions. This section is a formal treatment of admissibility proofs for the constructions that we have introduced. The final implication is that any specification of a constructible class translates directly into generating function equations. The translation of the cycle construction involves the Euler totient function ϕ(k) defined as the number of integers in [1, k] that are relatively prime to k (Appendix A.1: Arithmetical functions, p. 721). Theorem I.1 (Basic admissibility, unlabelled universe). The constructions of union, cartesian product, sequence, powerset, multiset, and cycle are all admissible. The associated operators are as follows. Sum:

A=B+C

Cartesian product: A = B × C

H⇒ A(z) = B(z) + C(z) H⇒ A(z) = B(z) · C(z)

Sequence:

A = S EQ(B)

H⇒ A(z) =

Powerset:

A = PS ET(B)

H⇒ A(z) =

Multiset:

A = MS ET(B) H⇒ A(z) =

1 1 − B(z) Y (1 + z n ) Bn n≥1

X ∞ (−1)k−1 k exp B(z ) k k=1 Y (1 − z n )−Bn n≥1

X ∞ 1 k B(z ) exp k k=1

Cycle:

A = C YC(B)

H⇒ A(z) =

∞ X ϕ(k) k=1

k

log

1 . 1 − B(z k )

For the sequence, powerset, multiset, and cycle translations, it is assumed that B0 = ∅.

28

I. COMBINATORIAL STRUCTURES AND ORDINARY GENERATING FUNCTIONS

The class E = {ǫ} consisting of the neutral object only, and the class Z consisting of a single “atomic” object (node, letter) of size 1 have OGFs E(z) = 1

Z (z) = z.

and

Proof. The proof proceeds case by case, building upon what we have just seen regarding unions and products. Combinatorial sum (disjoint union). Let A = B + C. Since the union is disjoint, and the size of an A–element coincides with its size in B or C, one has An = Bn + Cn and A(z) = B(z) + C(z), as discussed earlier. The rule also follows directly from the combinatorial form of generating functions as expressed by (8), p. 19: X X X A(z) = z |α| = z |α| + z |α| = B(z) + C(z). α∈A

α∈B

α∈C

Cartesian product. The admissibility result for A = B × C was considered as an example for Definition I.6, the convolution equation (13) leading to the relation A(z) = B(z) · C(z). We can also offer a direct derivation based on the combinatorial form of generating functions (8), p. 19, X X X X A(z) = z |α| = z |β|+|γ | = z |β| × z |γ | = B(z) · C(z), α∈A

(β,γ )∈(B×C )

γ ∈C

β∈B

as follows from distributing products over sums. This derivation readily extends to an arbitrary number of factors. Sequence construction. Admissibility for A = S EQ(B) (with B0 = ∅) follows from the union and product relations. One has so that

A = {ǫ} + B + (B × B) + (B × B × B) + · · · ,

1 , 1 − B(z) where the geometric sum converges in the sense of formal power series since [z 0 ]B(z) = 0, by assumption. Powerset construction. Let A = PS ET(B) and first take B to be finite. Then, the class A of all the finite subsets of B is isomorphic to a product, Y (21) PS ET(B) ∼ ({ǫ} + {β}), = A(z) = 1 + B(z) + B(z)2 + B(z)3 + · · · =

β∈B

with ǫ a neutral structure of size 0. Indeed, distributing the products in all possible ways forms all the possible combinations (sets with no repetition allowed) of elements of B; the reasoning is the same as what leads to an identity such as (1 + a)(1 + b)(1 + c) = 1 + [a + b + c] + [ab + bc + ac] + abc,

where all combinations of variables appear in monomials. Then, directly from the combinatorial form of generating functions and the sum and product rules, we find Y Y (22) A(z) = (1 + z |β| ) = (1 + z n ) Bn . β∈B

n

I. 2. ADMISSIBLE CONSTRUCTIONS AND SPECIFICATIONS

29

The exp–log transformation A(z) = exp(log A(z)) then yields X ∞ A(z) = exp Bn log(1 + z n ) (23)

= =

n=1 X ∞

exp

∞ nk X k−1 z (−1) Bn · k

k=1 n=1 B(z) B(z 2 ) B(z 3 ) exp − + − ··· , 1 2 3

where the second line results from expanding the logarithm, u2 u3 u − + − ··· , 1 2 3 and the third line results from exchanging the order of summations. The proof finally extends to the case of B being infinite by noting that each An depends only on those B j for which j ≤ n, to which the relations given above for the P (≤m) = PS ET(B (≤m) ). Then, finite case apply. Precisely, let B (≤m) = m k=1 B j and A m+1 with O(z ) denoting any series that has no term of degree ≤ m, one has log(1 + u) =

A(z) = A(≤m) (z) + O(z m+1 )

and

B(z) = B (≤m) (z) + O(z m+1 ).

On the other hand, A(≤m) (z) and B (≤m) (z) are connected by the fundamental exponential relation (23) , since B (≤m) is finite. Letting m tend to infinity, there follows in the limit B(z) B(z 2 ) B(z 3 ) A(z) = exp − + − ··· . 1 2 3 (See Appendix A.5: Formal power series, p. 730 for the notion of formal convergence.) Multiset construction. First for finite B (with B0 = ∅), the multiset class A = MS ET(B) is definable by Y (24) MS ET(B) ∼ S EQ({β}). = β∈B

In words, any multiset can be sorted, in which case it can be viewed as formed of a sequence of repeated elements β1 , followed by a sequence of repeated elements β2 , where β1 , β2 , . . . is a canonical listing of the elements of B. The relation translates into generating functions by the product and sequence rules, A(z) = (25)

Y

β∈B

=

exp

=

exp

(1 − z |β| )−1 =

X ∞ n=1

∞ Y

n=1

(1 − z n )−Bn

n −1

Bn log(1 − z )

B(z 2 ) B(z 3 ) B(z) + + + ··· , 1 2 3

30

I. COMBINATORIAL STRUCTURES AND ORDINARY GENERATING FUNCTIONS

where the exponential form results from the exp–log transformation. The case of an infinite class B follows by a limit argument analogous the one used for powersets. Cycle construction. The translation of the cycle relation A = C YC(B) turns out to be ∞ X 1 ϕ(k) log , A(z) = k 1 − B(z k ) k=1

where ϕ(k) is the Euler totient function. The first terms, with L k (z) := log(1 − B(z k ))−1 are 1 1 2 2 4 2 A(z) = L 1 (z) + L 2 (z) + L 3 (z) + L 4 (z) + L 5 (z) + L 6 (z) + · · · . 1 2 3 4 5 6 We reserve the proof to Appendix A.4: Cycle construction, p. 729, since it relies in part on multivariate generating functions to be officially introduced in Chapter III. The results for sets, multisets, and cycles are particular cases of the well-known P´olya theory that deals more generally with the enumeration of objects under group symmetry actions; for P´olya’s original and its edited version, see [488, 491]. This theory is described in many textbooks, for instance, those of Comtet [129] and Harary and Palmer [129, 319]; Notes I.58–I.60, pp. 85–86, distil its most basic aspects. The approach adopted here amounts to considering simultaneously all possible values of the number of components by means of bivariate generating functions. Powerful generalizations within Joyal’s elegant theory of species [359] are presented in the book by Bergeron, Labelle, and Leroux [50].

I.7. Vall´ee’s identity. Let M = MS ET(C), P = PS ET(C). One has combinatorially:

M(z) = P(z)M(z 2 ). (Hint: a multiset contains elements of either odd or even multiplicity.) Accordingly, one can deduce the translation of powersets from the formula for multisets. Iterating the relation above yields M(z) = P(z)P(z 2 )P(z 4 )P(z 8 ) · · · : this is closely related to the binary representation of numbers and to Euler’s identity (p. 49). It is used for instance in Note I.66 p. 91.

Restricted constructions. In order to increase the descriptive power of the framework of constructions, we ought to be able to allow restrictions on the number of components in sequences, sets, multisets, and cycles. Let K be a metasymbol representing any of S EQ, C YC, MS ET, PS ET and let be a predicate over the integers; then K (A) will represent the class of objects constructed by K, with a number of components constrained to satisfy . For instance, the notation (26)

S EQ=k (or simply S EQk ), S EQ>k , S EQ1 . . k

refers to sequences whose number of components are exactly k, larger than k, or in the interval 1 . . k respectively. In particular, k times

}| { z S EQk (B) := B × · · · × B ≡ B k ,

MS ETk (B) := S EQk (B)/R.

S EQ≥k (B) =

X j≥k

Bj ∼ = B k × S EQ(B),

Similarly, S EQodd , S EQeven will denote sequences with an odd or even number of components, and so on.

I. 2. ADMISSIBLE CONSTRUCTIONS AND SPECIFICATIONS

31

Translations for such restricted constructions are available, as shown generally in Subsection I. 6.1, p. 83. Suffice it to note for the moment that the construction A = S EQk (B) is really an abbreviation for a k-fold product, hence it admits the translation into OGFs (27)

A = S EQk (B)

H⇒

A(z) = B(z)k .

I. 2.3. Constructibility and combinatorial specifications. By composing basic constructions, we can build compact descriptions (specifications) of a broad variety of combinatorial classes. Since we restrict attention to admissible constructions, we can immediately derive OGFs for these classes. Put differently, the task of enumerating a combinatorial class is reduced to programming a specification for it in the language of admissible constructions. In this subsection, we first discuss the expressive power of the language of constructions, then summarize the symbolic method (for unlabelled classes and OGFs) by Theorem I.2. First, in the framework just introduced, the class of all binary words is described by W = S EQ(A), where A = {a, b} ∼ = Z + Z, the ground alphabet, comprises two elements (letters) of size 1. The size of a binary word then coincides with its length (the number of letters it contains). In other terms, we start from basic atomic elements and build up words by forming freely all the objects determined by the sequence construction. Such a combinatorial description of a class that only involves a composition of basic constructions applied to initial classes E, Z is said to be an iterative (or non-recursive) specification. Other examples already encountered include binary necklaces (Note I.1, p. 18) and the positive integers (Note I.5, p. 27) respectively defined by N = C YC(Z + Z)

and

I = S EQ≥1 (Z).

From this, one can construct ever more complicated objects. For instance, P = MS ET(I) ≡ MS ET(S EQ≥1 (Z))

means the class of multisets of positive integers, which is isomorphic to the class of integer partitions (see Section I. 3 below for a detailed discussion). As such examples demonstrate, a specification that is iterative can be represented as a single term built on E, Z and the constructions +, ×, S EQ, C YC, MS ET, PS ET. An iterative specification can be equivalently listed by naming some of the subterms (for instance, partitions in terms of natural integers I, themselves defined as sequences of atoms Z). Semantics of recursion. We next turn our attention to recursive specifications, starting with trees (cf also Appendix A.9: Tree concepts, p. 737, for basic definitions). In graph theory, a tree is classically defined as an undirected graph that is connected and acyclic. Additionally, a tree is rooted if a particular vertex is specified (this vertex is then kown as the root). Computer scientists commonly make use of trees called plane4 that are rooted but also embedded in the plane, so that the ordering of subtrees 4 The alternative terminology “planar tree” is also often used, but it is frowned upon by some as incorrect (all trees are planar graphs). We have thus opted for the expression “plane tree”, which parallels the phrase “plane curve”.

32

I. COMBINATORIAL STRUCTURES AND ORDINARY GENERATING FUNCTIONS

attached to any node matters. Here, we will give the name of general plane trees to such rooted plane trees and call G their class, where size is the number of vertices; see, e.g., reference [538]. (The term “general” refers to the fact that all nodes degrees are allowed.) For instance, a general tree of size 16, drawn with the root on top, is:

τ=

.

As a consequence of the definition, if one interchanges, say, the second and third root subtrees, then a different tree results—the original tree and its variant are not equivalent under a smooth deformation of the plane. (General trees are thus comparable to graphical renderings of genealogies where children are ordered by age.). Although we have introduced plane trees as two-dimensional diagrams, it is obvious that any tree also admits a linear representation: a tree τ with root ζ and root subtrees τ1 , . . . , τr (in that order) can be seen as the object ζ τ1 , . . . , τr , where the box encloses similar representations of subtrees. Typographically, a box · may be reduced to a matching pair of parentheses, “(·)”, and one gets in this way a linear description that illustrates the correspondence between trees viewed as plane diagrams and functional terms of mathematical logic and computer science. Trees are best described recursively. A plane tree is a root to which is attached a (possibly empty) sequence of trees. In other words, the class G of general trees is definable by the recursive equation (28)

G = Z × S EQ(G),

where Z comprises a single atom written “•” that represents a generic node. Although such recursive definitions are familiar to computer scientists, the specification (28) may look dangerously circular to some. One way of making good sense of it is via an adaptation of the numerical technique of iteration. Start with G [0] = ∅, the empty set, and define successively the classes G [ j+1] = Z × S EQ(G [ j] ). For instance, G [1] = Z × S EQ(∅) = {(•, ǫ)} ∼ = {•} describes the tree of size 1, and G [2] = • , • • , • • • , • • • • , . . . G [3] = •, • • , • •• , • • • • , ... , • • • , • • •• , • •• • , • • •• •• ,... .

First, each G [ j] is well defined since it corresponds to a purely iterative specification. Next, we have the inclusion G [ j] ⊂ G [ j+1] (a simple interpretation of G [ j] is the class of all trees of height < j). We canS therefore regard the complete class G as defined by the limit of the G [ j] ; that is, G := j G [ j] .

I.8. Lim-sup of classes. Let {A[ j] } be any increasing sequence of combinatorial classes, in S the sense that A[ j] ⊂ A[ j+1] , and the notions of size are compatible. If A[∞] =

[ j] is a jA

I. 2. ADMISSIBLE CONSTRUCTIONS AND SPECIFICATIONS

33

combinatorial class (there are finitely many elements of size n, for each n), then the corresponding OGFs satisfy A[∞] (z) = lim j→∞ A[ j] (z) in the formal topology (Appendix A.5: Formal power series, p. 730).

Definition I.7. A specification for an collection of r equations, (1) A(2) = A = (29) (r ) · · · A =

r –tuple AE = (A(1) , . . . , A(r ) ) of classes is a 81 (A(1) , . . . , A(r ) ) 82 (A(1) , . . . , A(r ) ) 8r (A(1) , . . . , A(r ) )

where each 8i denotes a term built from the A using the constructions of disjoint union, cartesian product, sequence, powerset, multiset, and cycle, as well as the initial classes E (neutral) and Z (atomic). We also say that the system is a specification of A(1) . A specification for a combinatorial class is thus a sort of formal grammar defining that class. Formally, the system (29) is an iterative or non-recursive specification if it is strictly upper-triangular, that is, A(r ) is defined solely in terms of initial classes Z, E; the definition of A(r −1) only involves A(r ) , and so on; in that case, by back substitutions, it is apparent that for an iterative specification, A(1) can be equivalently described by a single term involving only the initial classes and the basic constructors. Otherwise, the system is said to be recursive. In the latter case, the semantics of recursion is identical to the one introduced in the case oftrees: start with the “empty” vector of classes, AE[0] := (∅, . . . , ∅), E AE[ j] , and finally take the limit. iterate AE[ j+1] = 8 There is an alternative and convenient way to visualize these notions. Given a specification of the form (29), we can associate its dependency (di)graph Ŵ to it as follows. The set of vertices of Ŵ is the set of indices {1, . . . , r }; for each equation A(i) = 4i (A(1) , . . . , A(r ) ) and for each j such that A( j) appears explicitly on the right-hand side of the equation, place a directed edge (i → j) in Ŵ. It is then easily recognized that a class is iterative if the dependency graph of its specification is acyclic; it is recursive is the dependency graph has a directed cycle. (This notion will serve to define irreducible linear systems, p. 341, and irreducible polynomial systems, p. 482, which enjoy strong asymptotic properties.) Definition I.8. A class of combinatorial structures is said to be constructible or specifiable iff it admits a (possibly recursive) specification in terms of sum, product, sequence, set, multiset, and cycle constructions. At this stage, we have therefore available a specification language for combinatorial structures which is some fragment of set theory with recursion added. Each constructible class has by virtue of Theorem I.1 an ordinary generating function for which functional equations can be produced systematically. (In fact, it is even possible to use computer algebra systems in order to compute it automatically! See the article by Flajolet, Salvy, and Zimmermann [255] for the description of such a system.) Theorem I.2 (Symbolic method, unlabelled universe). The generating function of a constructible class is a component of a system of functional equations whose terms

34

I. COMBINATORIAL STRUCTURES AND ORDINARY GENERATING FUNCTIONS

are built from 1, z, + , × , Q , Exp , Exp , Log, where Q[ f ]

Exp[ f ]

1 , 1− f ! ∞ X f (z k ) = exp , k

=

Log[ f ] Exp[ f ]

k=1

∞ X ϕ(k)

1 , k 1 − f (z k ) k=1 ! ∞ k X k−1 f (z ) . = exp (−1) k

=

log

k=1

P´olya operators. The operator Q translating sequences (S EQ) is classically known as the quasi-inverse. The operator Exp (multisets, MS ET) is called the P´olya exponential5 and Exp (powersets, PS ET) is the modified P´olya exponential. The operator Log is the P´olya logarithm. They are named after P´olya who first developed the general enumerative theory of objects under permutation groups (pp. 85–86). The statement of Theorem I.2 signifies that iterative classes have explicit generating functions involving compositions of the basic operators only, while recursive structures have OGFs that are accessible indirectly via systems of functional equations. As we shall see at various places in this chapter, the following classes are constructible: binary words, binary trees, general trees, integer partitions, integer compositions, non-plane trees, polynomials over finite fields, necklaces, and wheels. We conclude this section with a few simple illustrations of the symbolic method expressed by Theorem I.2. Binary words. The OGF of binary words, as seen already, can be obtained directly from the iterative specification, W = S EQ(Z + Z)

H⇒

W (z) =

1 , 1 − 2z

whence the expected result, Wn = 2n . (Note: in our framework, if a, b are letters, then Z + Z ∼ = {a, b}.) General trees. The recursive specification of general trees leads to an implicit definition of their OGF, z G = Z × S EQ(G) H⇒ G(z) = . 1 − G(z) From this point on, basic algebra6 does the rest. First the original equation is equivalent (in the ring of formal power series) to G − G 2 − z = 0. Next, the quadratic equation 5It is a notable fact that, although the P´olya operators look algebraically “difficult” to compute with, their treatment by complex asymptotic methods, as regards coefficient asymptotics, is comparatively “easy”. We shall see many examples in Chapters IV–VII (e.g., pp. 252, 475). 6Methodological note: for simplicity, our computation is developed using the usual language of mathematics. However, analysis is not needed in this derivation, and operations such as solving quadratic equations and expanding fractional powers can all be cast within the purely algebraic framework of formal power series (p. 730).

I. 2. ADMISSIBLE CONSTRUCTIONS AND SPECIFICATIONS

35

is solvable by radicals, and one finds √ G(z) = 12 1 − 1 − 4z

z + z 2 + 2 z 3 + 5 z 4 + 14 z 5 + 42 z 6 + 132 z 7 + 429 z 8 + · · · X 1 2n − 2 zn . = n n−1

=

n≥1

(The conjugate root is to be discarded since it involves a term z −1 as well as negative coefficients.) The expansion then results from Newton’s binomial expansion, α α(α − 1) 2 (1 + x)α = 1 + x + x + ··· , 1 2! applied with α = 21 and x = −4z. The numbers √ 1 (2n)! 2n 1 − 1 − 4z (30) Cn = = with OGF C(z) = n+1 n (n + 1)! n! 2z are known as the Catalan numbers (EIS A000108) in the honour of Eug`ene Catalan, the mathematician who first studied their properties in geat depth (pp. 6 and 20). In summary, general trees are enumerated by Catalan numbers: 1 2n − 2 . G n = Cn−1 ≡ n n−1 For this reason the term Catalan tree is often employed as synonymous to “general (rooted unlabelled plane) tree”. Triangulations. Fix n + 2 points arranged in anticlockwise order on a circle and conventionally numbered from 0 to n + 1 (for instance the (n + 2)th roots of unity). A triangulation is defined as a (maximal) decomposition of the convex (n + 2)-gon defined by the points into n triangles (Figure I.1, p. 17). Triangulations are taken here as abstract topological configurations defined up to continuous deformations of the plane. The size of the triangulation is the number of triangles; that is, n. Given a triangulation, we define its “root” as a triangle chosen in some conventional and unambiguous manner (e.g., at the start, the triangle that contains the two smallest labels). Then, a triangulation decomposes into its root triangle and two subtriangulations (that may well be “empty”) appearing on the left and right sides of the root triangle; the decomposition is illustrated by the following diagram:

=

+

The class T of all triangulations can be specified recursively as T

=

{ǫ}

+

(T × ∇ × T ) ,

36

I. COMBINATORIAL STRUCTURES AND ORDINARY GENERATING FUNCTIONS

provided that we agree to consider a 2-gon (a segment) as giving rise to an “empty” triangulation of size 0. (The subtriangulations are topologically and combinatorially equivalent to standard ones, with vertices regularly spaced on a circle.) Consequently, the OGF T (z) satisfies the equation √ 1 (31) T (z) = 1 + zT (z)2 , so that T (z) = 1 − 1 − 4z . 2z As a result of (30) and (31), triangulations are enumerated by Catalan numbers: 2n 1 . Tn = Cn ≡ n+1 n This particular result goes back to Euler and Segner, a century before Catalan; see Figure I.1 on p. 17 for first values and p. 73 below for related bijections.

I.9. A bijection. Since both general trees and triangulations are enumerated by Catalan numbers, there must exist a size-preserving bijection between the two classes. Find one such bijection. [Hint: the construction of triangulations is evocative of binary trees, while binary trees are themselves in bijective correspondence with general trees (p. 73).]

I.10. A variant specification of triangulations. Consider the class U of “non-empty” triangulations of the n-gon, that is, we exclude the 2-gon and the corresponding “empty” triangulation of size 0. Then U = T \ {ǫ} admits the specification U = ∇ + (∇ × U) + (U × ∇) + (U × ∇ × U)

which also leads to the Catalan numbers via U = z(1 + U )2 , so that U (z) = (1 − 2z − √ 1 − 4z)/(2z) ≡ T (z) − 1.

I. 2.4. Exploiting generating functions and counting sequences. In this book we are going to see altogether more than a hundred applications of the symbolic method. Before engaging in technical developments, it is worth inserting a few comments on the way generating functions and counting sequences can be put to good use in order to solve combinatorial problems. Explicit enumeration formulae. In a number of situations, generating functions are explicit and can be expanded in such a way that explicit formulae result for their coefficients. A prime example is the counting of general trees and of triangulations above, where the quadratic equation satisfied by an OGF is amenable to an explicit solution—the resulting OGF could then be expanded by means of Newton’s binomial theorem. Similarly, we derive later in this chapter an explicit form for the number of integer compositions by means of the symbolic method (the answer turns out to be simply 2n−1 ) and obtain in this way, through OGFs, many related enumeration results. In this book, we assume as known the elementary techniques from basic calculus by which the Taylor expansion of an explicitly given function can be obtained. (Elementary references on such aspects are Wilf’s Generatingfunctionology [608], Graham, Knuth, and Patashnik’s Concrete Mathematics [307], and our book [538].) Implicit enumeration formulae. In a number of cases, the generating functions obtained by the symbolic method are still in a sense explicit, but their form is such that their coefficients are not clearly reducible to a closed form. It is then still possible to obtain initial values of the corresponding counting sequence by means of a symbolic

I. 2. ADMISSIBLE CONSTRUCTIONS AND SPECIFICATIONS

37

manipulation system. Furthermore, from generating functions, it is possible systematically to derive recurrences that lead to a procedure for computing an arbitrary number of terms of the counting sequence in a reasonably efficient manner. A typical example of this situation is the OGF of integer partitions, ∞ Y

m=1

1 , 1 − zm

for which recurrences obtained from the OGF and associated to fast algorithms are given in Note I.13 (p. 42) and Note I.19 (p. 49). An even more spectacular example is the OGF of non-plane trees, which is proved below (p. 71) to satisfy the infinite functional equation 1 1 H (z) = z exp H (z) + H (z 2 ) + H (z 3 ) + · · · , 2 3 and for which coefficients are computable in low complexity: see Note I.43, p. 72. (The references [255, 264, 456] develop a systematic approach to such problems.) The corresponding asymptotic analysis constitutes the main theme of Section VII. 5, p. 475. Asymptotic formulae. Such forms are our eventual goal as they allow for an easy interpretation and comparison of counting sequences. From a quick glance at the table of initial values of Wn (words), Pn (permutations), Tn (triangulations), as given in (2), p. 18, it is apparent that Wn grows more slowly than Tn , which itself grows more slowly than Pn . The classification of growth rates of counting sequences belongs properly to the asymptotic theory of combinatorial structures which neatly relates to the symbolic method via complex analysis. A thorough treatment of this part of the theory is presented in Chapters IV–VIII. Given the methods expounded there, it becomes possible to estimate asymptotically the coefficients of virtually any generating function, however complicated, that is provided by the symbolic method; that is, implicit enumerations in the sense above are well covered by complex asymptotic methods. Here, we content ourselves with a few remarks based on elementary real analysis. (The basic notations are described in Appendix A.2: Asymptotic notation, p. 722.) The sequence Wn = 2n grows exponentially and, in such an extreme simple case, the exact form coincides with the asymptotic form. The sequence Pn = n! must grow faster. But how fast? The answer is provided by Stirling’s formula, an important approximation originally due to James Stirling (Invitation, p. 4): n n √ 1 (n → +∞). 2π n 1 + O (32) n! = e n

(Several proofs are given in this book, based on the method of Laplace, p. 760, Mellin transforms, p. 766, singularity analysis, p. 407, and the saddle-point method, p 555.) The ratios of the exact values to Stirling’s approximations n n! √

n n e−n 2π n

1

2

5

10

100

1 000

1.084437

1.042207

1.016783

1.008365

1.000833

1.000083

38

I. COMBINATORIAL STRUCTURES AND ORDINARY GENERATING FUNCTIONS

60

50

Figure I.5. The growth regimes of three sequences f (n) = 2n , Tn , n! (from bottom to top) rendered by a plot of log10 f (n) versus n.

40

30

20

10

0 0

10

20

30

40

50

show an excellent quality of the asymptotic estimate: the error is only 8% for n = 1, less than 1% for n = 10, and less than 1 per thousand for any n greater than 100. Stirling’s formula provides in turn the asymptotic form of the Catalan numbers, by means of a simple calculation: √ 1 (2n)2n e−2n 4π n 1 (2n)! ∼ , Cn = n + 1 (n!)2 n n 2n e−2n 2π n which simplifies to (33)

4n Cn ∼ √ . π n3

n Thus, the growth of Catalan numbers is roughly √ comparable to an exponential, 4 , 3 modulated by a subexponential factor, here 1/ π n . A surprising consequence of this asymptotic estimate in the area of boolean function complexity appears in Example I.17 below (p. 77). Altogether, the asymptotic number of general trees and triangulations is well summarized by a simple formula. Approximations become more and more accurate as n becomes large. Figure I.5 illustrates the different growth regimes of our three reference sequences while Figure I.6 exemplifies the quality of the approximation with subtler phenomena also apparent on the figures and well explained by asymptotic theory. Such asymptotic formulae then make comparison between the growth rates of sequences easy. The interplay between combinatorial structure and asymptotic structure is indeed the principal theme of this book. We shall see in Part B that the generating functions provided by the symbolic method typically admit similarly simple asymptotic coefficient estimates.

I.11. The complexity of coding. A company specializing in computer-aided design has sold to you a scheme that (they claim) can encode any triangulation of size n ≥ 100 using at most 1.5n bits of storage. After reading these pages, what do you do? [Hint: sue them!] See also Note I.24 (p. 53) for related coding arguments.

I. 3. INTEGER COMPOSITIONS AND PARTITIONS

n

Cn

Cn⋆

Cn⋆ /Cn 2.25675 83341 91025 14779 23178 ˙ 1.11383 05127 5244589437 89064

1

1

2.25

10

16796

18707.89

100

0.89651 · 1057

0.90661 · 1057

1 000 10 000 100 000 1 000 000

0.20461 · 10598

0.22453 · 106015

0.17805 · 1060199

0.55303 · 10602051

0.20484 · 10598

0.22456 · 106015

0.17805 · 1060199

0.55303 · 10602051

39

1.01126 32841 24540 52257 13957 1.00112 51328 15424 16470 12827 1.00011 25013 28127 92913 51406 1.00001 12500 13281 25292 96322 1.00000 11250 00132 81250 29296

√ Figure I.6. The Catalan numbers Cn , their Stirling approximation Cn⋆ = 4n / π n 3 , and the ratio Cn⋆ /Cn . ⋆ /C I.12. Experimental asymptotics. From the data of Figure I.6, guess the values7 of C10 7 107

and of C ⋆ 6 /C5·106 to 25D. (See, Figure VI.3, p. 384, as well as, e.g., [385] for related 5·10 asymptotic expansions and [80] for similar properties.)

I. 3. Integer compositions and partitions This section and the next few provide examples of counting via specifications in classical areas of combinatorial theory. They illustrate the benefits of the symbolic method: generating functions are obtained with hardly any computation, and at the same time, many counting refinements follow from a basic combinatorial construction. The most direct applications described here relate to the additive decomposition of integers into summands with the classical combinatorial–arithmetic structures of partitions and compositions. The specifications are iterative and simply combine two levels of constructions of type S EQ, MS ET, C YC, PS ET. I. 3.1. Compositions and partitions. Our first examples have to do with decomposing integers into sums. Definition I.9. A composition of an integer n is a sequence (x1 , x2 , . . . , xk ) of integers (for some k) such that n = x1 + x2 + · · · + xk ,

x j ≥ 1.

A partition of an integer n is a sequence (x1 , x2 , . . . , xk ) of integers (for some k) such that n = x1 + x2 + · · · + xk and x1 ≥ x2 ≥ · · · ≥ xk ≥ 1. In both cases, the xi are called the summands or the parts and the quantity n is called the size. By representing summands in unary using small discs (“•”), we can render graphically a composition by drawing bars between some of the balls; if we arrange summands vertically, compositions appear as ragged landscapes. In contrast, partitions appear as staircases, also known as Ferrers diagrams [129, p. 100]; see Figure I.7. We 7In this book, we abbreviate a phrase such as “25 decimal places” by “25D”.

40

I. COMBINATORIAL STRUCTURES AND ORDINARY GENERATING FUNCTIONS

Figure I.7. Graphical representations of compositions and partitions: (left) the composition 1 + 3 + 1 + 4 + 2 + 3 = 14 with its “ragged landscape” and “balls-and-bars” models; (right) the partition 8 + 8 + 6 + 5 + 4 + 4 + 4 + 2 + 1 + 1 = 43 with its staircase (Ferrers diagram) model.

let C and P denote the class of all compositions and all partitions, respectively. Since a set can always be presented in sorted order, the difference between compositions and partitions lies in the fact that the order of summands does or does not matter. This is reflected by the use of a sequence construction (for C) against a multiset construction (for P). From this perspective, it proves convenient to regard 0 as obtained by the empty sequence of summands (k = 0), and we shall do so from now on.

Integers, as a combinatorial class. Let I = {1, 2, . . .} denote the combinatorial class of all integers at least 1 (the summands), and let the size of each integer be its value. Then, the OGF of I is X z , (34) I (z) = zn = 1−z n≥1

since In = 1 for n ≥ 1, corresponding to the fact that there is exactly one object in I for each size n ≥ 1. If integers are represented in unary, say by small balls, one has (35) I = {1, 2, 3, . . .} ∼ = {•, • •, • • •, . . .} = S EQ≥1 {•}, which constitutes a direct way to visualize the equality I (z) = z/(1 − z).

Compositions. First, the specification of compositions as sequences admits, by Theorem I.1, a direct translation into OGF: 1 (36) C = S EQ(I) H⇒ C(z) = . 1 − I (z) The collection of equations (34), (36) thus fully determines C(z): C(z)

=

1−z 1 z = 1 − 1−z 1 − 2z

= 1 + z + 2z 2 + 4z 3 + 8z 4 + 16z 5 + 32z 6 + · · · . From here, the counting problem for compositions is solved by a straightforward expansion of the OGF: one has X X C(z) = 2n z n − 2n z n+1 , n≥0

n≥0

I. 3. INTEGER COMPOSITIONS AND PARTITIONS 0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200 210 220 230 240 250

1 1024 1048576 1073741824 1099511627776 1125899906842624 1152921504606846976 1180591620717411303424 1208925819614629174706176 1237940039285380274899124224 1267650600228229401496703205376 1298074214633706907132624082305024 1329227995784915872903807060280344576 1361129467683753853853498429727072845824 1393796574908163946345982392040522594123776 1427247692705959881058285969449495136382746624 1461501637330902918203684832716283019655932542976 1496577676626844588240573268701473812127674924007424 1532495540865888858358347027150309183618739122183602176 1569275433846670190958947355801916604025588861116008628224 1606938044258990275541962092341162602522202993782792835301376 1645504557321206042154969182557350504982735865633579863348609024 1684996666696914987166688442938726917102321526408785780068975640576 1725436586697640946858688965569256363112777243042596638790631055949824 1766847064778384329583297500742918515827483896875618958121606201292619776 1809251394333065553493296640760748560207343510400633813116524750123642650624

41 1 42 627 5604 37338 204226 966467 4087968 15796476 56634173 190569292 607163746 1844349560 5371315400 15065878135 40853235313 107438159466 274768617130 684957390936 1667727404093 3972999029388 9275102575355 21248279009367 47826239745920 105882246722733 230793554364681

Figure I.8. For n = 0, 10, 20, . . . , 250 (left), the number of compositions Cn (middle) and the number of partitions Pn (right). √ The figure illustrates the difference in growth between Cn = 2n−1 and Pn = e O( n) .

implying C0 = 1 and Cn = 2n − 2n−1 for n ≥ 1; that is, Cn = 2n−1 , n ≥ 1.

(37)

This agrees with basic combinatorics since a composition of n can be viewed as the placement of separation bars at a subset of the n − 1 existing places in between n aligned balls (the “balls-and-bars” model of Figure I.7), of which there are clearly 2n−1 possibilities. Partitions. For partitions specified as multisets, the general translation mechanism of Theorem I.1, p. 27, provides 1 1 2 3 (38) P = MS ET(I) H⇒ P(z) = exp I (z) + I (z ) + I (z ) + · · · , 2 3 together with the product form corresponding to (25), p. 29, P(z) = (39)

∞ Y

m=1

1 1 − zm

= 1 + z + z2 + · · ·

1 + z2 + z4 + · · ·

1 + z3 + z6 + · · · · · ·

= 1 + z + 2z 2 + 3z 3 + 5z 4 + 7z 5 + 11z 6 + 15z 7 + 22z 8 + · · ·

(the counting sequence is EIS A000041). Contrary to compositions that are counted by the explicit formula 2n−1 , no simple form exists for Pn . Asymptotic analysis of the OGF (38) based on the saddle-point method (Chapter VIII, p. 574) shows that √ Pn = e O( n) . In fact an extremely famous theorem of Hardy and Ramanujan later improved by Rademacher (see Andrews’ book [14] and Chapter VIII) provides a full expansion of which the asymptotically dominant term is r ! 1 2n (40) Pn ∼ √ exp π . 3 4n 3

42

I. COMBINATORIAL STRUCTURES AND ORDINARY GENERATING FUNCTIONS

There are consequently appreciably fewer partitions than compositions (Figure I.8).

I.13. A recurrence for the partition numbers. Logarithmic differentiation gives z

∞ X nz n P ′ (z) = P(z) 1 − zn

implying

n=1

n Pn =

n X

σ ( j)Pn− j ,

j=1

where σ (n) is the sum of the divisors of n (e.g., σ (6) = 1 + 2 + 3 + 6 = 12). Consequently, P1 , . . . , PN can be computed in O(N 2 ) integer-arithmetic operations. (The technique is generally applicable to powersets and multisets; √ see Note I.43 (p. 72) for another application. Note I.19 (p. 49) further lowers the bound to O(N N ), in the case of partitions.)

By varying (36) and (38), we can use the symbolic method to derive a number of counting results in a straightforward manner. First, we state the following proposition. Proposition I.1. Let T ⊆ I be a subset of the positive integers. The OGFs of the classes C T := S EQ(S EQT (Z)) and P T := MS ET(S EQT (Z)) of compositions and partitions having summands restricted to T ⊂ Z≥1 are given by C T (z) =

1−

1 P

n∈T

zn

=

1 , 1 − T (z)

P T (z) =

Y

n∈T

1 . 1 − zn

Proof. A direct consequence of the specifications and Theorem I.1, p. 27.

This proposition permits us to enumerate compositions and partitions with restricted summands, as well as with a fixed number of parts. Example I.4. Compositions with restricted summands. In order to enumerate the class C {1,2} of compositions of n whose parts are only allowed to be taken from the set {1, 2}, simply write C {1,2} = S EQ(I {1,2} )

with I {1,2} = {1, 2}.

Thus, in terms of generating functions, one has C {1,2} (z) =

1 1 − I {1,2} (z)

with

I {1,2} (z) = z + z 2 .

This formula implies C {1,2} (z) =

1 = 1 + z + 2z 2 + 3z 3 + 5z 4 + 8z 5 + 13z 6 + · · · , 1 − z − z2

and the number of compositions of n in this class is expressed by a Fibonacci number, " √ !n # √ !n 1 1− 5 1+ 5 {1,2} = Fn+1 where Fn = √ Cn , − 2 2 5 of daisy–artichoke–rabbit fame In particular, the rate of growth is of the exponential type ϕ n , √ 1+ 5 where ϕ := is the golden ratio. 2 Similarly, compositions all of whose summands lie in the set {1, 2, . . . , r } have generating function (41)

C {1,...,r } (z) =

1 1 1−z = , r = 1−z 1 − z − z 2 − · · · zr 1 − 2z + zr +1 1 − z 1−z

I. 3. INTEGER COMPOSITIONS AND PARTITIONS

43

and the corresponding counts are generalized Fibonacci numbers. A double combinatorial sum expresses these counts X z(1 − zr ) j X j n − rk − 1 {1,...,r } . (−1)k (42) Cn = [z n ] = k j −1 (1 − z) j

j,k

This result is perhaps not too useful for grasping the rate of growth of the sequence when n gets large, so that asymptotic analysis is called for. Asymptotically, for any fixed r ≥ 2, there is a unique root ρr of the denominator 1 − 2z + zr +1 in ( 21 , 1), this root dominates all the other roots and is simple. Methods amply developed in Chapter IV and Example V.4 (p. 308) imply that, for some constant cr > 0, {1,...,r }

(43)

Cn

∼ cr ρr−n

for fixed r as n → ∞.

The quantity ρr plays a rˆole similar to that of the golden ratio when r = 2. . . . . . . . . . . . . . . .

I.14. Compositions into primes. The additive decomposition of integers into primes is still surrounded with mystery. For instance, it is not known whether every even number is the sum of two primes (Goldbach’s conjecture). However, the number of compositions of n into prime summands (any number of summands is permitted) is Bn = [z n ]B(z) where −1 −1 X B(z) = 1 − z p = 1 − z 2 − z 3 − z 5 − z 7 − z 11 − · · · p prime

=

1 + z 2 + z 3 + z 4 + 3 z 5 + 2 z 6 + 6 z 7 + 6 z 8 + 10 z 9 + 16 z 10 + · · ·

(EIS A023360), and complex asymptotic methods make it easy to determine the asymptotic form Bn ∼ 0.30365 · 1.47622n ; see Example V.2, p. 297. Example I.5. Partitions with restricted summands (denumerants). Whenever summands are restricted to a finite set, the special partitions that result are called denumerants. A denumerant problem popularized by P´olya [493, §3] consists in finding the number of ways of giving change of 99 cents using coins that are pennies (1 cent), nickels (5 cents), dimes (10 cents) and quarters (25 cents). (The order in which the coins are taken does not matter and repetitions are allowed.) For the case of a finite T , we predict from Proposition I.1 that P T (z) is always a rational function with poles that are at roots of unity; also the PnT satisfy a linear recurrence related to the structure of T . The solution to the original coin change problem is found to be 1 [z 99 ] = 213. (1 − z)(1 − z 5 )(1 − z 10 )(1 − z 25 ) In the same vein, one proves that 2n + 3 {1,2} Pn = 4

{1,2,3} Pn =

&

(n + 3)2 12

%

;

here ⌈x⌋ ≡ ⌊x + 12 ⌋ denotes the integer closest to the real number x. Such results are typically obtained by the two-step process: (i) decompose the rational generating function into simple fractions; (ii) compute the coefficients of each simple fraction and combine them to get the final result [129, p. 108]. The general argument also gives the generating function of partitions whose summands lie in the set {1, 2, . . . , r } as (44)

P {1,...,r } (z) =

r Y

m=1

1 . 1 − zm

44

I. COMBINATORIAL STRUCTURES AND ORDINARY GENERATING FUNCTIONS

In other words, we are enumerating partitions according to the value of the largest summand. One then finds by looking at the poles (Theorem IV.9, p. 256): {1,...,r }

(45)

Pn

∼ cr nr −1

with

cr =

1 . r !(r − 1)!

A similar argument provides the asymptotic form of PnT when T is an arbitrary finite set: PnT ∼

1 nr −1 τ (r − 1)!

Y

with τ :=

n, r := card(T ).

n∈T

This last estimate, originally due to Schur, is proved in Proposition IV.2, p. 258. . . . . . . . . . .

We next examine compositions and partitions with a fixed number of summands. Example I.6. Compositions with a fixed number of parts. Let C (k) denote the class of compositions made of k summands, k a fixed integer ≥ 1. One has C (k) = S EQk (I) ≡ I × I × · · · × I,

where the number of terms in the cartesian product is k. From here, the corresponding generating function is found to be k z C (k) (z) = I (z) with I (z) = . 1−z The number of compositions of n having k parts is thus zk n−1 (k) Cn = [z n ] , = k−1 (1 − z)k

a result which constitutes a combinatorial refinement of Cn = 2n−1 . (Note that the formula (k) Cn = n−1 k−1 also results easily from the balls-and-bars model of compositions (Figure I.7)). (k)

In such a case, the asymptotic estimate Cn ∼ n k−1 /(k − 1)! results immediately from the polynomial form of the binomial coefficient n−1 k−1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Example I.7. Partitions with a fixed number of parts. Let P (≤k) be the class of integer partitions with at most k summands. With our notation for restricted constructions (p. 30), this class is specified as P (≤k) = MS ET≤k (I).

It would be possible to appeal to the admissibility of such restricted compositions as developed in Subsection I. 6.1 below, but the following direct argument suffices in the case at hand. Geometrically, partitions, are represented as collections of points: this is the staircase model of Figure I.7, p. 40. A symmetry around the main diagonal (also known in the specialized literature as conjugation) exchanges number of summands and value of largest summand; one then has (with earlier notations) P (≤k) ∼ = P {1, . . k}

H⇒

P (≤k) (z) = P {1, . . k} (z),

so that, by (44), (46)

P (≤k) (z) ≡ P {1,...,k} =

k Y

m=1

1 . 1 − zm

I. 3. INTEGER COMPOSITIONS AND PARTITIONS

45

As a consequence, the OGF of partitions with exactly k summands, P (k) (z) = P (≤k) (z) − P (≤k−1) (z), evaluates to zk . (1 − z)(1 − z 2 ) · · · (1 − z k ) Given the equivalence between number of parts and largest part in partitions, the asymptotic estimate (45) applies verbatim here. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . P (k) (z) =

I.15. Compositions with summands bounded in number and size. The number of compositions of size n with k summands each at most r is expressible as 1 − zr k , [z n ] z 1−z which reduces to a simple binomial convolution (the calculation is similar to (42), p. 43). I.16. Partitions with summands bounded in number and size. The number of partitions of size n with at most k summands each at most ℓ is (1 − z)(1 − z 2 ) · · · (1 − z k+ℓ ) . [z n ] 2 (1 − z)(1 − z ) · · · (1 − z k ) · (1 − z)(1 − z 2 ) · · · (1 − z ℓ ) (Verifying this by recurrence is easy.) The GF reduces to the binomial coefficient k+ℓ as k , or a “q–analogue” of z → 1; it is known as a Gaussian binomial coefficient, denoted k+ℓ k z the binomial coefficient [14, 129]. The last example of this section illustrates the close interplay between combinatorial decompositions and special function identities, which constitutes a recurrent theme of classical combinatorial analysis. Example I.8. The Durfee square of partitions and stack polyominoes. The diagram of any partition contains a uniquely determined square (known as the Durfee square) that is maximal, as exemplified by the following diagram:

=

This decomposition is expressed in terms of partition GFs as [ 2 P∼ Z h × P (≤h) × P {1,...,h} , = h≥0

It gives automatically, via (44) and (46), a non-trivial identity, which is nothing but a formal rewriting of the geometric decomposition: ∞ Y

2 X zh 1 = 1 − zn h 2 h≥0 (1 − z) · · · (1 − z ) n=1

(h is the size of the Durfee square, known to manic bibliometricians as the “H-index”). Stack polyominoes. Here is a similar case illustrating the direct correspondence between geometric diagrams and generating functions, as afforded by the symbolic method. A stack polyomino is the diagram of a composition such that for some j, ℓ, one has 1 ≤ x1 ≤ x2 ≤

46

I. COMBINATORIAL STRUCTURES AND ORDINARY GENERATING FUNCTIONS

· · · ≤ x j ≥ x j+1 ≥ · · · ≥ xℓ ≥ 1 (see [552, §2.5] for further properties). The diagram representation of stack polyominoes

k

←→

P {1,...,k−1} × Z k × P {1,...,k}

translates immediately into the OGF S(z) =

X

1 zk , k 1 − z (1 − z)(1 − z 2 ) · · · (1 − z k−1 ) 2 k≥1

once use is made of the partition GFs P {1,...,k} (z) of (44). This last relation provides a bona fide algorithm for computing the initial values of the number of stack polyominoes (EIS A001523): S(z) = z + 2 z 2 + 4 z 3 + 8 z 4 + 15 z 5 + 27 z 6 + 47 z 7 + 79 z 8 + · · · . The book of van Rensburg [592] describes many such constructions and their relation to models of statistical physics, especially polyominoes. For instance, related “q–Bessel” functions appear in the enumeration of parallelogram polyominoes (Example IX.14, p. 660). . . . . . . . . . . . . . .

I.17. Systems of linear diophantine inequalities. Consider the class F of compositions of integers into four summands (x1 , x2 , x3 , x4 ) such that x1 ≥ 0,

x2 ≥ 2x1 ,

x3 ≥ 2x2 ,

x4 ≥ 2x3 ,

where the x j are in Z≥0 . The OGF is F(z) =

1 (1 − z)(1 − z 3 )(1 − z 7 )(1 − z 15 )

.

Generalize to r ≥ 4 summands (in Z≥0 ) and a similar system of inequalities. (Related GFs appear on p. 200.) Work out elementarily the OGFs corresponding to the following systems of inequalities: {x1 + x2 ≤ x3 },

{x1 + x2 ≥ x3 },

{x1 + x2 ≤ x3 + x4 },

{x1 ≤ x2 , x2 ≥ x3 , x3 ≤ x4 }.

More generally, the OGF of compositions into a fixed number of summands (in Z≥0 ), constrained to satisfy a linear system of equations and inequalities with coefficients in Z, is rational; its denominator is a product of factors of the form (1 − z j ). (Caution: this generalization is non-trivial: see Stanley’s treatment in [552, §4.6].)

Figure I.9 summarizes what has been learned regarding compositions and partitions. The way several combinatorial problems are solved effortlessly by the symbolic method is worth noting. I. 3.2. Related constructions. It is also natural to consider the two constructions of cycle and powerset when these are applied to the set of integers I.

I. 3. INTEGER COMPOSITIONS AND PARTITIONS

Specification

OGF

47

coefficients

Compositions: all

S EQ(S EQ≥1 (Z))

parts ≤ r

S EQ(S EQ1 . . r (Z))

k parts

S EQk (S EQ≥1 (Z))

cyclic

C YC(S EQ≥1 (Z))

1−z 2n−1 1 − 2z 1−z ∼ cr ρr−n 1 − 2z + zr +2 n k−1 zk ∼ k (k − 1)! (1 − z) 2n Eq. (48) ∼ n

Partitions: MS ET(S EQ≥1 (Z))

all parts ≤ r

MS ET(S EQ1 . . r (Z))

≤ k parts

∼ = MS ET(S EQ1 . . k (Z))

distinct parts PS ET(S EQ≥1 (Z))

∞ Y

m=1 r Y

m=1 k Y

m=1 ∞ Y

m=1

(p. 40) (pp. 42, 308) (p. 44) (p. 48) q

2n 3

(1 − z m )−1 ∼

1 π √ e 4n 3

(1 − z m )−1 ∼

nr −1 r !(r − 1)!

(pp. 43, 258)

(1 − z m )−1 ∼

n k−1 k!(k − 1)!

(pp. 44, 258)

(1 + z m )

∼

(pp. 41, 574)

33/4 π √n/3 (pp. 48, 579) e 12n 3/4

Figure I.9. Partitions and compositions: specifications, generating functions, and coefficients (in exact or asymptotic form).

Cyclic compositions (wheels). The class D = C YC(I) comprises compositions defined up to circular shift of the summands; so, for instance 2 + 3 + 1 + 2 + 5, 3 + 1 + 2 + 5 + 2, etc, are identified. Alternatively, we may view elements of D as “wheels” composed of circular arrangements of rows of balls (taken up to rotation):

a “wheel” (cyclic composition)

By the translation of the cycle construction, the OGF is

(47)

D(z) = =

∞ X ϕ(k) k=1

k

log 1 −

zk 1 − zk

−1

z + 2 z 2 + 3 z 3 + 5 z 4 + 7 z 5 + 13 z 6 + 19 z 7 + 35 z 8 + · · · .

48

I. COMBINATORIAL STRUCTURES AND ORDINARY GENERATING FUNCTIONS

The coefficients are thus (EIS A008965) 1X 2n 1X ϕ(k)(2n/k − 1) ≡ −1 + ϕ(k)2n/k ∼ , (48) Dn = n n n k|n

k|n

where the condition “k | n” indicates a sum over the integers k dividing n. Notice that Dn is of the same asymptotic order as n1 Cn , which is suggested by circular symmetry of wheels, but there is a factor: Dn ∼ 2Cn /n. Partitions into distinct summands. The class Q = PS ET(I) is the subclass of P = MS ET(I) corresponding to partitions determined as in Definition I.9, but with the strict inequalities xk > · · · > x1 , so that the OGF is Y (49) Q(z) = (1 + z n ) = 1 + z + z 2 + 2z 3 + 2z 4 + 3z 5 + 4z 6 + 5z 7 + · · · . n≥1

The coefficients (EIS A000009) are not expressible in closed form. However, the saddle-point method (Section VIII. 6, p. 574) yields the approximation: r n 33/4 (50) Qn ∼ , exp π 3 12n 3/4 which has a shape similar to that of Pn in (40), p. 41.

I.18. Odd versus distinct summands. The partitions of n into odd summands (On ) and the

ones into distinct summands (Qn ) are equinumerous. Indeed, one has Q(z) =

∞ Y

m=1

(1 + z m ),

O(z) =

∞ Y

(1 − z 2 j+1 )−1 .

j=0

Equality results from substituting (1 + a) = (1 − a 2 )/(1 − a) with a = z m , Q(z) =

1 1 1 − z2 1 − z4 1 − z6 1 − z8 1 − z10 1 ··· = ··· , 1 − z 1 − z2 1 − z 3 1 − z4 1 − z 5 1 − z 1 − z3 1 − z5

and simplification of the numerators with half of the denominators (in boldface).

Let I pow

Partitions into powers. = {1, 2, 4, 8, . . .} be the set of powers of 2. The corresponding P and Q partitions have OGFs P pow (z)

Q pow (z)

=

∞ Y

j=0

1 1 − z2

j

= 1 + z + 2z 2 + 2z 3 + 4z 4 + 4z 5 + 6z 6 + 6z 7 + 10z 8 + · · · ∞ Y j (1 + z 2 ) = j=0

= 1 + z + z2 + z3 + z4 + z5 + · · · .

The first sequence 1, 1, 2, 2, . . . is the “binary partition sequence” (EIS A018819); the difficult asymptotic analysis was performed by de Bruijn [141] who obtained an esti2 mate that involves subtle fluctuations and is of the global form e O(log n) . The function

I. 4. WORDS AND REGULAR LANGUAGES

49

Q pow (z) reduces to (1− z)−1 since every number has a unique additive decomposition into powers of 2. Accordingly, the identity ∞ Y 1 j (1 + z 2 ), = 1−z j=0

first observed by Euler is sometimes nicknamed the “computer scientist’s identity” as it reflects the property that every number admits a unique binary representation. There exists a rich set of identities satisfied by partition generating functions— this fact is down to deep connections with elliptic functions, modular forms, and q–analogues of special functions on the one hand, basic combinatorics and number theory on the other hand. See [14, 129] for introductions to this fascinating subject.

I.19. Euler’s pentagonal number theorem. This famous identity expresses 1/P(z) as Y

(1 − z n ) =

n≥1

X

(−1)k z k(3k+1)/2 .

k∈Z

It is proved formally and combinatorially in Comtet’s reference [129, p. 105] and it serves to illustrate “proofs from THE BOOK” in the splendid exposition √ of Aigner and Ziegler [7, §29]. Consequently, the numbers {P j } Nj=0 can be determined in O(N N ) integer operations.

I.20. A digital surprise. Define the constant 9 99 999 9999 ··· . 10 100 1000 10000 Is it a surprise that it evaluates numerically to . ϕ = 0.8900100999989990000001000099999999899999000000000010 · · · , ϕ :=

that is, its decimal representation involves only the digits 0, 1, 8, 9? [This is suggested by a note of S. Ramanujan, “Some definite integrals”, Messenger of Math. XLIV, 1915, pp. 10–18.]

I.21. Lattice points. The number of lattice points with integer coordinates that belong to the closed ball of radius n in d-dimensional Euclidean space is 2

[z n ]

1 (2(z))d 1−z

where

2(z) = 1 + 2

∞ X

2

zn .

n=1

Estimates may be obtained via the saddle-point method (Note VIII.35, p. 589).

I. 4. Words and regular languages Fix a finite alphabet A whose elements are called letters. Each letter is taken to have size 1; i.e., it is an atom. A word8 is any finite sequence of letters, usually written without separators. So, for us, with the choice of the Latin alphabet (A = {a,. . . ,z}), sequences such as ygololihp, philology, zgrmblglps are words. We denote the set of all words (often written as A⋆ in formal linguistics) by W. Following a well-established tradition in theoretical computer science and formal linguistics, any subset of W is called a language (or formal language, when the distinction with natural languages has to be made). 8An alternative to the term “word” sometimes preferred by computer scientists is “string”; biologists

often refer to words as “sequences”.

50

I. COMBINATORIAL STRUCTURES AND ORDINARY GENERATING FUNCTIONS

Words: a–runs < k exclude subseq. p exclude factor p circular

OGF

coefficients

1 1 − mz

mn

(p. 50)

∼ ck ρk−n

(pp. 51, 308)

≈ (m − 1)n n |p|−1

(p. 54)

∼ cpρp

(pp. 61, 271)

∼ m n /n

(p. 64)

1 − zk 1 − mz + (m − 1)z k+1 Eq. (55) cp(z) z |p| + (1 − mz)cp(z)

Eq. (64)

regular language

≈ C · An n k

[rational]

context-free lang.

−n

≈ C · An n p/q

[algebraic]

(pp. 56, 302, 342) (pp. 80, 501)

Figure I.10. Words over an m–ary alphabet: generating functions and coefficients.

From the definition of the set of words W, one has 1 , 1 − mz where m is the cardinality of the alphabet, i.e., the number of letters. The generating function gives us the counting result

(51)

W∼ = S EQ(A)

H⇒

W (z) =

Wn = m n . This result is elementary, but, as is usual with symbolic methods, many enumerative consequences result from a given construction. It is precisely the purpose of this section to examine some of them. We shall introduce separately two frameworks that each have great expressive power for describing languages. The first one is iterative (i.e., non-recursive) and it bases itself on “regular specifications” that only involve the constructions of sum, product, and sequence; the other one, which is recursive (but of a very simple form), is best conceived of in terms of finite automata and is equivalent to linear systems of equations. Both frameworks turn out to be logically equivalent in the sense that they determine the same family of languages, the regular languages, though the equivalence is non-trivial (Appendix A.7: Regular languages, p. 733), and each particular problem usually admits a preferred representation. The resulting OGFs are invariably rational functions, a fact to be systematically exploited from an asymptotic standpoint in Chapter V. Figure I.10 recapitulates some of the major word problems studied in this chapter, together with corresponding approximations9. 9 In this book, we reserve “∼” for the technical sense of “asymptotically equivalent” defined in Ap-

pendix A.2: Asymptotic notations, p. 722; we reserve the symbol “≈” to mean “approximately equal” in a vaguer sense, where formulae have been simplified by omitting constant factors or terms of secondary importance (in context).

I. 4. WORDS AND REGULAR LANGUAGES

51

I. 4.1. Regular specifications. Consider words (or strings) over the binary alphabet A = {a, b}. There is an alternative way to construct binary strings. It is based on the observation that, with a minor adjustment at the beginning, a string decomposes into a succession of “blocks” each formed with a single b followed by an arbitrary (possibly empty) sequence of as. For instance aaabaababaabbabbaaa decomposes as [aaa] baa | ba | baa | b | ba | b | baaa. Omitting redundant10 symbols, we have the alternative decomposition:

(52)

W∼ = S EQ(a) × S EQ(b S EQ(a))

H⇒

W (z) =

1 1 . 1 1 − z 1 − z 1−z

This last expression reduces to (1 − 2z)−1 as it should. Longest runs. The interest of the construction just seen is to take into account various meaningful properties, for example longest runs. Abbreviate by a n} = 1 − P {C ≤ n} = n![z n ] e z − e z/r − 1 .

An application of the Eulerian integral trick of (27) then provides a representation of the expectation of the time needed for a full collection as Z ∞ (31) E(C) = 1 − (1 − e−t/r )r dt. 0

A simple calculation (expand by the binomial theorem and integrate termwise) shows that r X r (−1) j−1 E(C) = r , j j j=1

which constitutes a first answer to the coupon collector problem in the form of an alternating sum. Alternatively, in (31), perform the change of variables v = 1 − e−t/r , then expand and integrate termwise; this process provides the more tractable form E(C) = r Hr ,

(32) where Hr is the harmonic number: (33)

Hr = 1 +

1 1 1 + + ··· + . 2 3 r

Formula (32) is by the way easy to interpret directly6: one needs on average 1 = r/r trials to get the first day, then r/(r − 1) to get a different day, etc. Regarding (32), one has available the well-known formula (by comparing sums with integrals or by Euler–Maclaurin summation), 1 . + O(r −2 ), γ = 0.57721 56649, Hr = log r + γ + 2r where γ is known as Euler’s constant. Thus, the expected time for a full collection satisfies 1 (34) E(C) = r log r + γ r + + O(r −1 ). 2 Here the “surprise” lies in the nonlinear growth of the expected time for a full collection. For a . year on Earth, r = 365, the exact expected value is = 2364.64602 whereas the approximation provided by the first three terms of (34) yields 2364.64625, representing a relative error of only one in ten million. As usual, the symbolic treatment adapts to a variety of situations, for instance, to multiple collections. One finds: the expected time till each item (birthday or coupon) is obtained b times is Z ∞ r J (r, b) = 1 − 1 − eb−1 (t/r )e−t/r dt. 0

6Such elementary derivations are very much problem specific: contrary to the symbolic method, they

do not usually generalize to more complex situations.

118

II. LABELLED STRUCTURES AND EGFS

This expression vastly generalizes the standard case (31), which corresponds to b = 1. From it, one finds [454] J (r, b) = r (log r + (b − 1) log log r + γ − log(b − 1)! + o(1)) , so that only a few more trials are needed in order to obtain additional collections. . . . . . . . . .

II.9. The little sister. The coupon collector has a little sister to whom he gives his duplicates. Foata, Lass, and Han [266] show that the little sister misses on average Hr coupons when her big brother first obtains a complete collection.

II.10. The probability distribution of time till a complete collection. The saddle-point method (Chapter VIII) may be used to prove that, in the regime n = r log r + tr , we have −t

lim P(C ≤ r log r + tr ) = e−e .

t→∞

This continuous probability distribution is known as a double exponential distribution. For the time C (b) till a collection of multiplicity b, one has lim P(C (b) < r log r + (b − 1)r log log r + tr ) = exp(−e−t /(b − 1)!),

t→∞

a property known as the Erd˝os–R´enyi law, which finds application in the study of random graphs [195].

Words as both labelled and unlabelled objects. What distinguishes a labelled structure from an unlabelled one? There is nothing intrinsic there, and everything is in the eye of the beholder—or rather in the type of construction adopted when modelling a specific problem. Take the class of words W over an alphabet of cardinality r . The two generating functions (an OGF and an EGF respectively), X X 1 zn b (z) ≡ W Wn z n = and W (z) ≡ = er z , Wn 1 − r z n! n n

leading in both cases to Wn = r n , correspond to two different ways of constructing words: the first one directly as an unlabelled sequence, the other as a labelled power of letter positions. A similar situation arises for r –partitions, for which we find as OGF and EGF, (e z − 1)r zr b and S (r ) (z) = , S (r ) (z) = (1 − z)(1 − 2z) · · · (1 − r z) r! by viewing these either as unlabelled structures (an encoding via words of a regular language in Section I. 4.3, p. 62) or directly as labelled structures (this chapter, p. 108).

II.11. Balls switching chambers: the Ehrenfest2 model. Consider a system of two chambers A and B (also classically called “urns”). There are N distinguishable balls, and, initially, chamber A contains them all. At any instant 21 , 32 , . . ., one ball is allowed to change from one

chamber to the other. Let E n[ℓ] be the number of possible evolutions that lead to chamber A containing ℓ balls at instant n and E [ℓ] (z) the corresponding EGF. Then N E [ℓ] (z) = (cosh z)ℓ (sinh z) N −ℓ , E [N ] (z) = (cosh z) N ≡ 2−N (e z + e−z ) N . ℓ

[Hint: the EGF E [N ] enumerates mappings where each preimage has an even cardinality.] In particular the probability that urn A is again full at time 2n is N X 1 N (N − 2k)2n . k 2 N N 2n k=0

II. 4. ALIGNMENTS, PERMUTATIONS, AND RELATED STRUCTURES

119

This famous model was introduced by Paul and Tatiana Ehrenfest [188] in 1907, as a simplified model of heat transfer. It helped resolve the apparent contradiction between irreversibility in thermodynamics (the case N → ∞) and recurrence of systems undergoing ergodic transformations (the case N < ∞). See especially Mark Kac’s discussion [361]. The analysis can also be carried out by combinatorial methods akin to those of weighted lattice paths: see Note V.25, p. 336 and [304].

II. 4. Alignments, permutations, and related structures In this section, we start by considering specifications built by piling up two constructions, sequences-of-cycles and sets-of-cycles respectively. They define a new class of objects, alignments, while serving to specify permutations in a novel way. (These specifications otherwise parallel surjections and set partitions.) In this context, permutations are examined under their cycle decomposition, the corresponding enumeration results being the most important ones combinatorially (Subsection II. 4.1 and Figure II.8, p. 123). In Subsection II. 4.2, we recapitulate the meaning of classes that can be defined iteratively by a combination of any two nested labelled constructions. II. 4.1. Alignments and permutations. The two specifications under consideration now are (35)

O = S EQ(C YC(Z)),

and

P = S ET(C YC(Z)),

specifying new objects called alignments (O) as well as an important decomposition of permutations (P). Alignments. An alignment is a well-labelled sequence of cycles. Let O be the class of all alignments. Schematically, one can visualize an alignment as a collection of directed cycles arranged in a linear order, somewhat like slices of a sausage fastened on a skewer:

The symbolic method provides, O = S EQ(C YC(Z))

H⇒

O(z) =

1 , 1 − log(1 − z)−1

and the expansion starts as O(z) = 1 + z + 3

z2 z3 z4 z5 + 14 + 88 + 694 + · · · , 2! 3! 4! 5!

but the coefficients (see EIS A007840: “ordered factorizations of permutations into cycles”) appear to admit no simple form.

120

II. LABELLED STRUCTURES AND EGFS

4 10

17

5

14

15

16

6

12

7

2

13

8

1 11

9 3

A permutation may be viewed as a set of cycles that are labelled circular digraphs. The diagram shows the decomposition of the permutation 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 σ = 11 12 13 17 10 15 14 9 3 4 6 2 7 8 1 5 16 .

(Cycles here read clockwise and i is connected to σi by an edge in the graph.) Figure II.7. The cycle decomposition of permutations.

Permutations and cycles. From elementary mathematics, it is known that a permutation admits a unique decomposition into cycles. Let σ be a permutation. Start with any element, say 1, and draw a directed edge from 1 to σ (1), then continue connecting to σ 2 (1), σ 3 (1), and so on; a cycle containing 1 is obtained after at most n steps. If one repeats the construction, taking at each stage an element not yet connected to earlier ones, the cycle decomposition of the permutation σ is obtained; see Figure II.7. This argument shows that the class of sets-of-cycles (corresponding to P in (35)) is isomorphic to the class of permutations as defined in Example II.2, p. 98: (36) P∼ = S EQ(Z). = S ET(C YC(Z)) ∼ This combinatorial isomorphism is reflected by the obvious series identity 1 1 P(z) = exp log = . 1−z 1−z The property that exp and log are inverse of one another is nothing but an analytic reflex of the combinatorial fact that permutations uniquely decompose into cycles! As regards combinatorial applications, what is especially fruitful is the variety of special results derived from the decomposition of permutations into cycles. By a use of restricted construction that entirely parallels Proposition II.2, p. 110, we obtain the following statement. Proposition II.4. The class P (A,B) of permutations with cycle lengths in A ⊆ Z>0 and with cycle number that belongs to B ⊆ Z≥0 has EGF X zb X za , β(z) = . P (A,B) (z) = β(α(z)) where α(z) = a b! a∈A

b∈B

II.12. What about alignments? With similar notations, one has for alignments O (A,B) (z) = β(α(z))

where

corresponding to O(A,B) = S EQ B (C YC A (Z)).

α(z) =

X za X zb , , β(z) = a

a∈A

b∈B

II. 4. ALIGNMENTS, PERMUTATIONS, AND RELATED STRUCTURES

121

Example II.12. Stirling cycle numbers. The class P (r ) of permutations that decompose into r cycles, satisfies r 1 1 (37) P (r ) = S ETr (C YC(Z)) H⇒ P (r ) (z) = log . r! 1−z The number of such permutations of size n is then r 1 n! n (r ) [z ] log . (38) Pn = r! 1−z These numbers are fundamental quantities of combinatorial analysis. They are known as the Stirling numbers of the first kind, or better, according to a proposal of Knuth, the Stirling cycle numbers. Together with the Stirling partition numbers, the properties of the Stirling cycle numbers are explored in the book by Graham, Knuth, and Patashnik [307] where they are denoted by nr . See Appendix A.8: Stirling numbers, p. 735. (Note that the number of alignments formed with r cycles is r ! nr .) As we shall see shortly (p. 140) Stirling numbers also surface in the enumeration of permutations by their number of records. It is also of interest to determine what happens regarding cycles in a random permutation of size n. Clearly, when the uniform distribution is placed over all elements of Pn , each particular permutation has probability exactly 1/n!. Since the probability of an event is the quotient of the number of favorable cases over the total number of cases, the quantity 1 n pn,k := n! k is the probability that a random element of Pn has k cycles. This probabilities can be effectively determined for moderate values of n from (38) by means of a computer algebra system. Here are for instance selected values for n = 100: k pn,k

1 0.01

2 0.05

3 0.12

4 0.19

5 0.21

6 0.17

7 0.11

8 0.06

9 0.03

10 0.01

For this value n = 100, we expect in a vast majority of cases the number of cycles to be in the interval [1, 10]. (The residual probability is only about 0.005.) Under this probabilistic model, the mean is found to be about 5.18. Thus: A random permutation of size 100 has on average a little more than 5 cycles; it rarely has more than 10 cycles. Such procedures demonstrate a direct exploitation of symbolic methods. They do not however tell us how the number of cycles could depend on n, as n increases unboundedly. Such questions are to be investigated systematically in Chapters III and IX. Here, we shall content ourselves with a brief sketch. First, form the bivariate generating function, P(z, u) :=

∞ X

P (r ) (z)u r ,

r =0

and observe that P(z, u) =

r ∞ r X u 1 1 log = (1 − z)−u . = exp u log r! 1−z 1−z

r =0

Newton’s binomial theorem then provides

−u . [z n ](1 − z)−u = (−1)n n

122

II. LABELLED STRUCTURES AND EGFS

In other words, a simple formula n X n k (39) u = u(u + 1)(u + 2) · · · (u + n − 1) k k=0

encodes precisely all the Stirling cycle numbers corresponding to a fixed value of n. From here, P the expected number of cycles, µn := k kpn,k is easily found to be expressed in terms of harmonic numbers (use logarithmic differentiation of (39)): µn = Hn ≡ 1 +

1 1 + ··· + . 2 n

. In particular, one has µ100 ≡ H100 = 5.18738. In general: The mean number of cycles in a random permutation of size n grows logarithmically with n, µn ∼ log n. . . . . . . . . . . . . . . . . . Example II.13. Involutions and permutations without long cycles. A permutation σ is an involution if σ 2 = Id, with Id the identity permutation. Clearly, an involution can have only cycles of sizes 1 and 2. The class I of all involutions thus satisfies ! z2 . (40) I = S ET(C YC1,2 (Z)) H⇒ I (z) = exp z + 2 The explicit form of the EGF lends itself to expansion, In =

⌊n/2⌋ X k=0

n! , (n − 2k)!2k k!

which solves the counting problem explicitly. A pairing is an involution without a fixed point. In other words, only cycles of length 2 are allowed, so that J = S ET(C YC2 (Z))

H⇒

2 J (z) = e z /2 ,

J2n = 1 · 3 · 5 · · · (2n − 1).

(The formula for Jn , hence that of In , can be checked by a direct reasoning.) Generally, the EGF of permutations, all of whose cycles (in particular the largest one) have length at most equal to r , satisfies r j X z (r ) . B (z) = exp j j=1

(r )

The numbers bn = [z n ]B (r ) (z) satisfy the recurrence (r )

(r )

(r )

(n + 1)bn+1 = (n + 1)bn − bn−r , by which they can be computed quickly, while they can be analysed asymptotically by means of the saddle-point method (Chapter VIII, p. 568). This gives access to the statistics of the longest cycle in a permutation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Example II.14. Derangements and permutations without short cycles. Classically, a derangement is defined as a permutation without fixed points, i.e., σi 6= i for all i. Given an integer r , an r –derangement is a permutation all of whose cycles (in particular the shortest one) have length larger than r . Let D(r ) be the class of all r –derangements. A specification is (41)

D(r ) = S ET(C YC>r (Z)),

II. 4. ALIGNMENTS, PERMUTATIONS, AND RELATED STRUCTURES

Specification

EGF 1 1 −z r 1 1 log r! 1−z

Permutations: S EQ(Z) r cycles

S ETr (C YC(Z))

involutions

S ET(C YC1 . . 2 (Z)) e z+z /2 zr z + ··· + S ET(C YC1 . . r (Z)) exp 1 r e−z S ET(C YC>1 (Z)) 1−z r exp − 1z − · · · − zr S ET(C YC>r (Z)) 1−z

all cycles ≤ r derangements

all cycles > r

2

123

coefficient n! n r

(p. 104) (p. 121)

≈ n n/2

(pp. 122, 558)

≈ n 1−1/r

(pp. 122, 568)

∼ n!e−1

(pp. 122, 261)

∼ n!e− Hr (pp. 123, 261)

Figure II.8. A summary of permutation enumerations.

the corresponding EGF then being (42)

D (r ) (z) = exp

P j exp(− rj=1 zj ) X zj = . j 1−z j>r

For instance, when r = 1, a direct expansion yields (1)

1 1 (−1)n Dn =1− + − ··· + , n! 1! 2! n! a truncation of the series expansion of exp(−1) that converges rapidly to e−1 . Phrased differently, this becomes a famous combinatorial problem with a pleasantly quaint nineteenth-century formulation [129]: “A number n of people go to the opera, leave their hats on hooks in the cloakroom and grab them at random when leaving; the probability that nobody gets back his own hat is asymptotic to 1/e, which is nearly 37%.” The usual proof uses inclusion–exclusion; see Section III. 7, p. 198 for both the classical and symbolic arguments. (It is a sign of changing times that Motwani and Raghavan [451, p. 11] describe the problem as one of sailors that return to their ship in a state of inebriation and choose random cabins to sleep in.) For the generalized derangement problem, we have, for any fixed r (with Hr a harmonic number, p. 117), (r )

(43)

Dn ∼ e− Hr , n!

which is proved easily by complex asymptotic methods (Chapter IV, p. 261). . . . . . . . . . . . . .

Similar to several other structures that we have been considering previously, permutation allow for transparent connections between structural constraints and the forms of generating functions. The major counting results encountered in this section are summarized in Figure II.8.

124

II. LABELLED STRUCTURES AND EGFS

II.13. Permutations such that σ f = Id. Such permutations are “roots of unity” in the symmetric group. Their EGF is X zd , exp d d| f

where the sum extends to all divisors d of f .

II.14. Parity constraints in permutations. The EGFs of permutations having only even-size cycles or odd-size cycles (O(z)) are, respectively, r 1 1 1+z 1 1+z 1 p , O(z) = exp = = log log . E(z) = exp 2 2 2 1 − z 1−z 1 − z2 1−z

One finds E 2n = (1 · 3 · 5 · · · (2n − 1))2 and O2n = E 2n , O2n+1 = (2n + 1)E 2n . The EGFs of permutations having an even number of cycles (E ∗ (z)) and an odd number of cycles (O ∗ (z)) are, respectively, 1 1 1 1 1−z 1 z−1 1 = = + , O ∗ (z) = sinh log + , E ∗ (z) = cosh log 1−z 21−z 2 1−z 21−z 2 so that parity of the number of cycles is evenly distributed among permutations of size n as soon as n ≥ 2. The generating functions obtained in this way are analogous to the ones appearing in the discussion of “Comtet’s square”, p. 111.

II.15. A hundred prisoners I. This puzzle originates with a paper of G´al and Miltersen [275, 612]. A hundred prisoners, each uniquely identified by a number between 1 and 100, have been sentenced to death. The director of the prison gives them a last chance. He has a cabinet with 100 drawers (numbered 1 to 100). In each, he’ll place at random a card with a prisoner’s number (all numbers different). Prisoners will be allowed to enter the room one after the other and open, then close again, 50 drawers of their own choosing, but will not in any way be allowed to communicate with one another afterwards. The goal of each prisoner is to locate the drawer that contains his own number. If all prisoners succeed, then they will all be spared; if at least one fails, they will all be executed. There are two mathematicians among the prisoners. The first one, a pessimist, declares . that their overall chances of success are only of the order of 1/2100 = 8 · 10−31 . The second one, a combinatorialist, claims he has a strategy for the prisoners, which has a greater than 30% chance of success. Who is right? [Note III.10, p. 176 provides a solution, but our gentle reader is advised to reflect on the problem for a few moments, before she jumps there.]

II. 4.2. Second-level structures. Consider the three basic constructors of labelled sequences (S EQ), sets (S ET), and cycles (C YC). We can play the formal game of examining what the various combinations produce as combinatorial objects. Restricting attention to superpositions of two constructors (an external one applied to an internal one) gives nine possibilities summarized by the table of Figure II.9. The classes of surjections, alignments, set partitions, and permutations appear naturally as S EQ ◦ S ET, S EQ ◦ C YC, S ET ◦ S ET, and S ET ◦ C YC (top right corner). The others represent essentially non-classical objects. The case of the class L = S EQ(S EQ≥1 (Z)) describes objects that are (ordered) sequences of linear graphs; this can be interpreted as permutations with separators inserted, e.g, 53|264|1, or alternatively as integer compositions with a labelling superimposed, so that L n = n! 2n−1 . The class F = S ET(S EQ≥1 (Z)) corresponds to unordered collections of permutations; in other words, “fragments” are obtained by breaking a permutation into pieces

II. 5. LABELLED TREES, MAPPINGS, AND GRAPHS

ext.\int.

S EQ

S EQ≥1

S ET≥1 Surjections (R)

Alignments (O)

S EQ ◦ S EQ 1−z 1 − 2z

S EQ ◦ S ET 1 2 − ez

S EQ ◦ C YC 1 1 − log(1 − z)−1 Permutations (P)

S ET ◦ S EQ e z/(1−z)

C YC

C YC

Labelled compositions (L)

Fragmented permutations (F ) Set partitions (S) S ET

125

S ET ◦ S ET e

e z −1

Supernecklaces (S I )

Supernecklaces (S I I )

C YC ◦ S EQ 1−z log 1 − 2z

C YC ◦ S ET

log(2 − e z )−1

S ET ◦ C YC 1 1−z Supernecklaces (S I I I ) C YC ◦ C YC 1 log 1 − log(1 − z)−1

Figure II.9. The nine second-level structures.

(pieces must be non-empty for definiteness). The interesting EGF is z3 z4 z2 + 13 + 73 + · · · , 2! 3! 4! (EIS A000262: “sets of lists”). The corresponding asymptotic analysis serves to illustrate an important aspect of the saddle-point method in Chapter VIII (p. 562). What we termed “supernecklaces” in the last row represents cyclic arrangements of composite objects existing in three brands. All sorts of refinements, of which Figures II.8 and II.9 may give an idea, are clearly possible. We leave to the reader’s imagination the task of determining which among the level 3 structures may be of combinatorial interest. . . F(z) = e z/(1−z) = 1 + z + 3

II.16. A meta-exercise: Counting specifications of level n. The algebra of constructions satisfies the combinatorial isomorphism S ET(C YC(X )) ∼ = S EQ(X ) for all X . How many different terms involving n constructions can be built from three symbols C YC, S ET, S EQ satisfying a semi-group law (“◦”) together with the relation S ET ◦ C YC = S EQ? This determines the number of specifications of level n. [Hint: the OGF is rational as normal forms correspond to words with an excluded pattern.] II. 5. Labelled trees, mappings, and graphs In this section, we consider labelled trees as well as other important structures that are naturally associated with them. As in the unlabelled case considered in Section I. 6, p. 83, the corresponding combinatorial classes are inherently recursive, since a tree is obtained by appending a root to a collection (set or sequence) of subtrees. From here, it is possible to build the “functional graphs” associated to mappings from a finite set to itself—these decompose as sets of connected components that are cycles of trees. Variations of these construction finally open up the way to the enumeration of graphs having a fixed excess of the number of edges over the number of vertices.

126

II. LABELLED STRUCTURES AND EGFS

3 2

5

&

( 3, 2, 5, 1, 7, 4, 6)

1 7

4

6

Figure II.10. A labelled plane tree is determined by an unlabelled tree (the “shape”) and a permutation of the labels 1, . . . , n.

II. 5.1. Trees. The trees to be studied here are labelled, meaning that nodes bear distinct integer labels. Unless otherwise specified, they are rooted, meaning as usual that one node is distinguished as the root. Labelled trees, like their unlabelled counterparts, exist in two varieties: (i) plane trees where an embedding in the plane is understood (or, equivalently, subtrees dangling from a node are ordered, say, from left to right); (ii) non-plane trees where no such embedding is imposed (such trees are then nothing but connected undirected acyclic graphs with a distinguished root). Trees may be further restricted by the additional constraint that the nodes’ outdegrees should belong to a fixed set ⊆ Z≥0 where ∋ 0. Plane labelled trees. We first dispose of the plane variety of labelled trees. Let A be the set of (rooted labelled) plane trees constrained by . This family is A = Z ⋆ S EQ (A),

where Z represents the atomic class consisting of a single labelled node: Z = {1}. The sequence construction appearing here reflects the planar embedding of trees, as subtrees stemming from a common root are ordered between themselves. Accordingly, the EGF A(z) satisfies X A(z) = zφ(A(z)) where φ(u) = uω. ω∈

This is exactly the same equation as the one satisfied by the ordinary GF of – 1 An is the number restricted unlabelled plane trees (see Proposition I.5, p. 66). Thus, n! of unlabelled trees. In other words: in the plane rooted case, the number of labelled trees equals n! times the corresponding number of unlabelled trees. As illustrated by Figure II.10, this is easily understood combinatorially: each labelled tree can be defined by its “shape” that is an unlabelled tree and by the sequence of node labels where nodes are traversed in some fixed order (preorder, say). In a way similar to Proposition I.5, p. 66, one has, by Lagrange inversion (Appendix A.6: Lagrange Inversion, p. 732): An = n![z n ]A(z) = (n − 1)![u n−1 ]φ(u)n .

II. 5. LABELLED TREES, MAPPINGS, AND GRAPHS

1

1 2

1

1

2

2 3 3

2

3

1

3 2 1

3

2

3

1 1 2

1 2

127

2 3

1

3 3

1

2

2 1

Figure II.11. There are T1 = 1, T2 = 2, T3 = 9, and in general Tn = n n−1 Cayley trees of size n.

This simple analytic–combinatorial relation enables us to transpose all of the enumeration results of Subsection I. 5.1, p. 65, to plane labelled trees, upon multiplying the evaluations by n!, of course. In particular, the total number of “general” plane labelled trees (with no degree restriction imposed, i.e., = Z≥0 ) is (2n − 2)! 1 2n − 2 = = 2n−1 (1 · 3 · · · (2n − 3)) . n! × n n−1 (n − 1)! The corresponding sequence starts as 1, 2, 12, 120, 1680 and is EIS A001813. Non-plane labelled trees. We next turn to non-plane labelled trees (Figure II.11) to which the rest of this section will be devoted. The class T of all such trees is definable by a symbolic equation, which provides an implicit equation satisfied by the EGF: (44)

T = Z ⋆ S ET(T )

H⇒

T (z) = ze T (z) .

There the set construction translates the fact that subtrees stemming from the root are not ordered between themselves. From the specification (44), the EGF T (z) is defined implicitly by the “functional equation” (45)

T (z) = ze T (z) .

The first few values are easily found, for instance by the method of indeterminate coefficients: z2 z3 z4 z5 T (z) = z + 2 + 9 + 64 + 625 + · · · . 2! 3! 4! 5! As suggested by the first few coefficients(9 = 32 , 64 = 43 , 625 = 54 ), the general formula is (46)

Tn = n n−1

which is established (as in the case of plane unlabelled trees) by Lagrange inversion: 1 n−1 u n n [u ](e ) = n n−1 . (47) Tn = n! [z ]T (z) = n! n The enumeration result Tn = n n−1 is a famous one, attributed to the prolific British mathematician Arthur Cayley (1821–1895) who had keen interest in combinatorial mathematics and published altogether over 900 papers and notes. Consequently, formula (46) given by Cayley in 1889 is often referred to as “Cayley’s formula” and unrestricted non-plane labelled trees are often called “Cayley trees”. See [67, p. 51] for a historical discussion. The function T (z) is also known as the

128

II. LABELLED STRUCTURES AND EGFS

(Cayley) “tree function”; it is a close relative of the W -function [131] defined implicitly by W e W = z, which was introduced by the Swiss mathematician Johann Lambert (1728–1777) otherwise famous for first proving the irrationality of the number π . A similar process gives the number of (non-plane rooted) trees where all outdegrees of nodes are restricted to lie in a set . This corresponds to the specification X uω T () = Z ⋆ S ET (T () ) H⇒ T () (z) = zφ(T () (z)), φ(u) := . ω! ω∈

What the last formula involves is the “exponential characteristic” of the degree sequence (as opposed to the ordinary characteristic, in the planar case). It is once more amenable to Lagrange inversion. In summary: Proposition II.5. The number of rooted non-plane trees, where all nodes have outdegree in , is X uω Tn() = (n − 1)![u n−1 ](φ(u))n where φ(u) = . ω! ω∈

In particular, when all node degrees are allowed, i.e., when ≡ Z≥0 , the number of trees is Tn = n n−1 and its EGF is the Cayley tree function satisfying T (z) = ze T (z) .

As in the unlabelled case (p. 66), we refer to a class of labelled trees defined by degree restrictions as a simple variety of trees: its EGF satisfies an equation of the form y = zφ(y).

II.17. Pr¨ufer’s bijective proofs of Cayley’s formula. The simplicity of Cayley’s formula calls for a combinatorial explanation. The most famous one is due to Pr¨ufer (in 1918). It establishes as follows a bijective correspondence between unrooted Cayley trees whose number is n n−2 for size n and sequences (a1 , . . . , an−2 ) with 1 ≤ a j ≤ n for each j. Given an unrooted tree τ , remove the endnode (and its incident edge) with the smallest label; let a1 denote the label of the node that was joined to the removed node. Continue with the pruned tree τ ′ to get a2 in a similar way. Repeat the construction of the sequence until the tree obtained only consists of a single edge. For instance: 3 1

4

2 8

7

5

−→

(4, 8, 4, 8, 8, 4).

6

It can be checked that the correspondence is bijective; see [67, p. 53] or [445, p. 5].

II.18. Forests. The number of unordered k–forests (i.e., k–sets of trees) is (k)

Fn

= n![z n ]

T (z)k (n − 1)! n−k u n n − 1 n−k = [u ](e ) = n , k! (k − 1)! k−1

as follows from B¨urmann’s form of Lagrange inversion, relative to powers (p. 66).

II.19. Labelled hierarchies. The class L of labelled hierarchies is formed of trees whose internal nodes are unlabelled and are constrained to have outdegree larger than 1, while their leaves have labels attached to them. As for other labelled structures, size is the number of labels (internal nodes do not contribute). Hierarchies satisfy the specification (compare with p. 72) L = Z + S ET≥2 (L),

H⇒

L = z + eL − 1 − L .

II. 5. LABELLED TREES, MAPPINGS, AND GRAPHS 13

4 12

23

7

22 15 24

21 14 16

20

6

10

5

17 19

26

9

1

129

8 11 3

25 2

18

Figure II.12. A functional graph of size n = 26 associated to the mapping ϕ such that ϕ(1) = 16, ϕ(2) = ϕ(3) = 11, ϕ(4) = 23, and so on. This happens to be solvable in terms of the Cayley function: L(z) = T ( 12 e z/2−1/2 ) + 2z − 1 2 . The first few values are 0, 1, 4, 26, 236, 2752 (EIS A000311): these numbers count phylogenetic trees, used to describe the evolution of a genetically-related group of organisms, and they correspond to Schr¨oder’s “fourth problem” [129, p. 224]. The asymptotic analysis is done in Example VII.12, p. 472. The class of binary (labelled) hierarchies defined by the additional fact that internal nodes can have degree 2 only is expressed by √ M = Z + S ET2 (M) H⇒ M(z) = 1 − 1 − 2z and Mn = 1 · 3 · · · (2n − 3), where the counting numbers are now, surprisingly perhaps, the odd factorials.

II. 5.2. Mappings and functional graphs. Let F be the class of mappings (or “functions”) from [1 . . n] to itself. A mapping f ∈ [1 . . n] 7→ [1 . . n] can be represented by a directed graph over the set of vertices [1 . . n] with an edge connecting x to f (x), for all x ∈ [1 . . n]. The graphs so obtained are called functional graphs and they have the characteristic property that the outdegree of each vertex is exactly equal to 1. Mappings and associated graphs. Given a mapping (or function) f , upon starting from any point x0 , the succession of (directed) edges in the graph traverses the vertices corresponding to iterated values of the mapping, x0 ,

f (x0 ),

f ( f (x0 )), . . . .

Since the domain is finite, each such sequence must eventually loop back on itself. When the operation is repeated, starting each time from an element not previously hit, the vertices group themselves into (weakly connected) components. This leads to a valuable characterization of functional graphs (Figure II.12): a functional graph is a set of connected functional graphs; a connected functional graph is a collection of rooted trees arranged in a cycle. (This decomposition is seen to extend the decomposition of permutations into cycles, p. 120.)

130

II. LABELLED STRUCTURES AND EGFS

Thus, with T being as before the class of all Cayley trees, and with K the class of all connected functional graphs, we have the specification: F(z) = e K (z) F = S ET (K) 1 K (z) = log H⇒ (48) K = C YC(T ) 1 − T (z) T (z) T = Z ⋆ S ET(T ) T (z) = ze . What is especially interesting here is a specification binding three types of related structures. From (48), the EGF F(z) is found to satisfy F = (1 − T )−1 . It can be checked from this, by Lagrange inversion once again (p. 733), that we have Fn = n n ,

(49)

as was to be expected (!) from the origin of the problem. More interestingly, Lagrange inversion also gives the number of connected functional graphs (expand log(1 − T )−1 and recover coefficients by B¨urmann’s form, p. 66): n − 1 (n − 1)(n − 2) + + ··· . n n2 The quantity Q(n) that appears in (50) is a famous one that surfaces in many problems of discrete mathematics (including the birthday paradox, Equation (27), p. 115). Knuth has proposed naming it “Ramanujan’s Q-function” as it already appears in the first letter of Ramanujan to Hardy in 1913. The asymptotic analysis is elementary and involves developing a continuous approximation of the general term and approximating the resulting Riemann sum by an integral: this is an instance of the Laplace method for sums briefly explained in Appendix B.6: Laplace’s method, p. 755 (see also [377, Sec. 1.2.11.3] and [538, Sec. 4.7]). In fact, very precise estimates come out naturally from an analysis of the singularities of the EGF K (z), as we shall see in Chapters VI (p. 416) and VII (p. 449). The net result is r π , Kn ∼ nn 2n √ so that a fraction about 1/ n of all the graphs consist of a single component. (50)

K n = n n−1 Q(n)

where

Q(n) := 1 +

Constrained mappings. As is customary with the symbolic method, basic constructions open the way to a large number of related counting results (Figure II.13). First, by an adaptation of (48), the mappings without fixed points, (∀x : f (x) 6= x) and those without 1, 2–cycles, (additionally, ∀x : f ( f (x)) 6= x), have EGFs, respectively, 2

e−T (z)−T (z)/2 . 1 − T (z)

e−T (z) , 1 − T (z)

The first term is consistent with what a direct count yields, namely (n − 1)n , which is asymptotic to e−1 n n , so that the fraction of mappings without fixed point is asymptotic to e−1 . The second one lends itself easily to complex asymptotic methods that give 2

e−T −T /2 n![z ] ∼ e−3/2 n n , 1−T n

II. 5. LABELLED TREES, MAPPINGS, AND GRAPHS

Mappings:

EGF 1 1−T

connected

log

no fixed-point

coefficient nn

1 1−T

∼ nn

e−T

1−T z

idempotent

e ze

partial

eT 1−T

131

r

(p. 130) π 2n

(pp. 130, 449)

∼ e−1 n n

(p. 130)

≈

(pp. 131, 571)

nn

(log n)n

∼ e nn

(p. 132)

Figure II.13. A summary of various counting results relative to mappings, with T ≡ T (z) the Cayley tree function. (Bijections, surjections, involutions, and injections are covered by previous constructions.)

and the proportion is asymptotic to e−3/2 . These two particular estimates are of the same form as that found for permutations (the generalized derangements, Equation (43)). Such facts are not quite obvious by elementary probabilistic arguments, but they are neatly explained by the singular theory of combinatorial schemas developed in Part B of this book. Next, idempotent mappings, i.e., ones satisfying f ( f (x)) = f (x) for all x, correspond to I ∼ = S ET(Z ⋆ S ET(Z)), so that n X n n−k z k . I (z) = e ze and In = k k=0

(The specification translates the fact that idempotent mappings can have only cycles of length 1 on which are grafted sets of direct antecedents.) The latter sequence is EIS A000248, which starts as 1,1,3,10,41,196,1057. An asymptotic estimate can be derived either from the Laplace method or, better, from the saddle-point method expounded in Chapter VIII (p. 571). Several analyses of this type are of relevance to cryptography and the study of random number generators. For √ instance, the fact that a random mapping over [1 . . n] tends to reach a cycle in O( n) steps (Subsection VII. 3.3, p. 462) led Pollard to design a surprising Monte Carlo integer factorization algorithm; see [378, p. 371] and [538, Sec 8.8], as well as our discussion in Example VII.11, p. 465. This algorithm, once suitably optimized, first led to the factorization of the Fermat number 8 F8 = 22 + 1 obtained by Brent in 1980.

II.20. Binary mappings. The class BF of binary mappings, where each point has either 0 or 2 preimages, is specified by BF = S ET(K), K = C YC(P), P = Z ⋆ B, B = Z ⋆ S ET0,2 (B) (planted trees P and binary trees B are needed), so that B F(z) = p

1 1 − 2z 2

,

B F2n =

((2n)!)2 . 2n (n!)2

132

II. LABELLED STRUCTURES AND EGFS

The class BF is an approximate model of the behaviour of (modular) quadratic functions under iteration. See [18, 247] for a general enumerative theory of random mappings including degreerestricted ones.

II.21. Partial mappings. A partial mapping may be undefined at some points, and at those we consider it takes a special value, ⊥. The iterated preimages of ⊥ form a forest, while the remaining values organize themselves into a standard mapping. The class PF of partial mappings is thus specified by PF = S ET(T ) ⋆ F , so that

e T (z) and P Fn = (n + 1)n . 1 − T (z) This construction lends itself to all sorts of variations. For instance, the class P F I of injective partial maps is described as sets of chains of linear and circular graphs, P F I = S ET(C YC(Z)+ S EQ≥1 (Z)), so that 2 n X n 1 z/(1−z) e , P F In = i! . P F I (z) = 1−z i P F(z) =

i=0

(This is a symbolic rewriting of part of the paper [78]; see Example VIII.13, p. 596, for asymptotics.)

II. 5.3. Labelled graphs. Random graphs form a major chapter of the theory of random discrete structures [76, 355]. We examine here enumerative results concerning graphs of low “complexity”, that is, graphs which are very nearly trees. (Such graphs for instance play an essential rˆole in the analysis of early stages of the evolution of a random graph, when edges are successively added, as shown in [241, 354].) Unrooted trees and acyclic graphs. The simplest of all connected graphs are certainly the ones that are acyclic. These are trees, but contrary to the case of Cayley trees, no root is specified. Let U be the class of all unrooted trees. Since a rooted tree (rooted trees are, as we know, counted by Tn = n n−1 ) is an unrooted tree combined with a choice of a distinguished node (there are n such possible choices for trees of size n), one has Tn = nUn implying Un = n n−2 . At generating function level, this combinatorial equality translates into Z z dw , U (z) = T (w) w 0 which integrates to give (take T as the independent variable) 1 U (z) = T (z) − T (z)2 . 2 Since U (z) is the EGF of acyclic connected graphs, the quantity A(z) = eU (z) = e T (z)−T (z)

2 /2

is the EGF of all acyclic graphs. (Equivalently, these are unordered forests of unrooted trees; the sequence is EIS A001858: 1, 1, 2, 7, 38, 291, . . . ) Singularity analysis methods (Note VI.14, p. 406) imply the estimate An ∼ e1/2 n n−2 . Surprisingly, perhaps, there are barely more acyclic graphs than unrooted trees—such phenomena are easily explained by singularity analysis.

II. 5. LABELLED TREES, MAPPINGS, AND GRAPHS

133

Unicyclic graphs. The excess of a graph is defined as the difference between the number of edges and the number of vertices. For a connected graph, this quantity must be at least −1, this minimal value being precisely attained by unrooted trees. The class Wk is the class of connected graphs of excess equal to k; in particular U = W−1 . The successive classes W−1 , W0 , W1 , . . ., may be viewed as describing connected graphs of increasing complexity. The class W0 comprises all connected graphs with the number of edges equal to the number of vertices. Equivalently, a graph in W0 is a connected graph with exactly one cycle (a sort of “eye”), and for that reason, elements of W0 are sometimes referred to as “unicyclic components” or “unicycles”. In a way, such a graph looks very much like an undirected version of a connected functional graph. In precise terms, a graph of W0 consists of a cycle of length at least 3 (by definition, graphs have neither loops nor multiple edges) that is undirected (the orientation present in the usual cycle construction is killed by identifying cycles isomorphic up to reflection) and on which are grafted trees (these are implicitly rooted by the point at which they are attached to the cycle). With UC YC representing the (new) undirected cycle construction, one thus has W0 ∼ = UC YC≥3 (T ). We claim that this construction is reflected by the EGF equation

1 1 1 1 log − T (z) − T (z)2 . 2 1 − T (z) 2 4 Indeed one has the isomorphism W0 + W0 ∼ = C YC≥3 (T ), (51)

W0 (z) =

since we may regard the two disjoint copies on the left as instantiating two possible orientations of the undirected cycle. The result of (51) then follows from the usual translation of the cycle construction—it is originally due to the Hungarian probabilist R´enyi in 1959. Asymptotically, one finds (using methods of Chapter VI, p. 406): 1√ 2π n n−1/2 . (52) n![z n ]W0 ∼ 4 (The sequence starts as 0, 0, 1, 15, 222, 3660, 68295 and is EIS A057500.) Finally, the number of graphs made only of trees and unicyclic components has EGF 2 e T /2−3T /4 , e W−1 (z)+W0 (z) = √ 1−T which asymptotically yields n![z n ]e W−1 +W0 ∼ Ŵ(3/4)(2e)−1/4 π −1/2 n n−1/4 . Such graphs stand just next to acyclic graphs in order of structural complexity. They are the undirected counterparts of functional graphs encountered in the previous subsection.

II.22. 2–Regular graphs. This is based on Comtet’s account [129, Sec. 7.3]. A 2-regular graph is an undirected graph in which each vertex has degree exactly 2. Connected 2–regular graphs are thus undirected cycles of length n ≥ 3, so that their class R satisfies 2

(53)

R = S ET(UC YC≥3 (Z))

H⇒

e−z/2−z /4 R(z) = √ . 1−z

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II. LABELLED STRUCTURES AND EGFS

EGF

coefficient 2n(n−1)/2

Graphs: acyclic, connected acyclic (forest) unicycle

U ≡ W−1 = T − T 2 /2 A=e W0 =

T2

1 1 T log − − 2 1−T 2 4 2

e T /2−3T /4 √ 1−T Pk (T ) Wk = (1 − T )3k

set of trees & unicycles B = connected, excess k

n n−2

T −T 2 /2

∼ e1/2 n n−2 √ ∼ 14 2πn n−1/2 (2e)−1/4 n−1/4 n √ π √ Pk (1) 2π n+(3k−1)/2 ∼ 3k/2 n 2 Ŵ(3k/2)

∼ Ŵ(3/4)

Figure II.14. A summary of major enumeration results relative to labelled graphs. The asymptotic estimates result from singularity analysis (Note VI.14, p. 406).

Given n straight lines in general position in the plane, a cloud is defined to be a set of n intersection points, no three being collinear. Clouds and 2–regular graphs are equinumerous. [Hint: Use duality.] The asymptotic analysis will serve as a prime example of the singularity analysis process (Examples VI.1, p. 379 and VI.2, p. 395). The general enumeration of r –regular graphs becomes somewhat more difficult as soon as r > 2. Algebraic aspects are discussed in [289, 303] while Bender and Canfield [39] have determined the asymptotic formula (for r n even) √ (r 2 −1)/4 r r/2 r n/2 n , 2e er/2 r ! for the number of r –regular graphs of size n. (See also Example VIII.9, p. 583, for regular multigraphs.) (54)

(r )

Rn ∼

Graphs of fixed excess. The previous discussion suggests considering more generally the enumeration of connected graphs according to excess. E. M. Wright made important contributions in this area [620, 621, 622] that are revisited in the famous “giant paper on the giant component” by Janson, Knuth, Łuczak, and Pittel [354]. Wright’s result are summarized by the following proposition. Proposition II.6. The EGF Wk (z) of connected graphs with excess (of edges over vertices) equal to k is, for k ≥ 1, of the form (55)

Wk (z) =

Pk (T ) , (1 − T )3k

T ≡ T (z),

where Pk is a polynomial of degree 3k + 2. For any fixed k, as n → ∞, one has √ Pk (1) 2π n+(3k−1)/2 n (56) Wk,n = n![z ]Wk (z) = 3k/2 1 + O(n −1/2 ) . n 2 Ŵ (3k/2)

The combinatorial part of the proof (see Note II.23 below) is an interesting exercise in graph surgery and symbolic methods. The analytic part of the statement follows straightforwardly from singularity analysis. The polynomials P(T ) and the

II. 5. LABELLED TREES, MAPPINGS, AND GRAPHS

135

constants Pk (1) are determined by an explicit nonlinear recurrence; one finds for instance: W1 =

1 T 4 (6 − T ) , 24 (1 − T )3

W2 =

1 T 4 (2 + 28T − 23T 2 + 9T 3 − T 4 ) . 48 (1 − T )6

II.23. Wright’s surgery. The full proof of Proposition II.6 by symbolic methods requires the notion of pointing in conjunction with multivariate generating function techniques of Chapter III. It is convenient to define wk (z, y) := y k Wk (zy), which is a bivariate generating function with y marking the number of edges. Pick up an edge in a connected graph of excess k + 1, then remove it. This results either in a connected graph of excess k with two pointed vertices (and no edge in between) or in two connected components of respective excess h and k − h, each with a pointed vertex. Graphically (with connected components in grey): 11111111111 00000000000 00000000000 11111111111 00000000000 11111111111 00000000000 11111111111 00000000000 11111111111 00000000000 11111111111 00000000000 11111111111 00000000000 11111111111 00000000000 11111111111 00000000000 11111111111 00000000000 11111111111

+

=

This translates into the differential recurrence on the wk (∂x := ∂∂x ), k+1 X 2∂ y wk+1 = z 2 ∂z2 wk − 2y∂ y wk + (z∂z wh ) · z∂z wk−h , h=−1

and similarly for Wk (z) = wk (z, 1). From here, it can be verified by induction that each Wk is a rational function of T ≡ W−1 . (See Wright’s original papers [620, 621, 622] or [354] for details; constants related to the Pk (1) occur in Subsection VII. 10.1, p. 532.)

As explained in the giant paper [354], such results combined with complex analytic techniques provide, with great detail, information about a random graph Ŵ(n, m) with n nodes and m edges. In the sparse case where m is of the order of n, one finds the following properties to hold “with high probability” (w.h.p.)7; that is, with probability tending to 1 as n → ∞ .

• For m = µn, with µ < 21 , the random graph Ŵ(m, n) has w.h.p. only tree and unicycle components; the largest component is w.h.p. of size O(log n). • For m = 12 n + O(n 2/3 ), w.h.p. there appear one or several semi-giant components that have size O(n 2/3 ). • For m = µn, with µ > 21 , there is w.h.p. a unique giant component of size proportional to n.

In each case, refined estimates follow from a detailed analysis of corresponding generating functions, which is a main theme of [241] and especially [354]. Raw forms of these results were first obtained by Erd˝os and R´enyi who launched the subject in a famous series of papers dating from 1959–60; see the books [76, 355] for a probabilistic context and the paper [40] for the finest counting estimates available. In contrast, the enumeration of all connected graphs (irrespective of the number of edges, that is, without excess being taken into account) is a relatively easy problem treated in the 7 Synonymous expressions are “asymptotically almost surely” (a.a.s) and “in probability”. The term “almost surely” is sometimes used, though it lends itself to confusion with properties of continuous measures.

136

II. LABELLED STRUCTURES AND EGFS

next section. Many other classical aspects of the enumerative theory of graphs are covered in the book Graphical Enumeration by Harary and Palmer [319].

II.24. Graphs are not specifiable. The class of all graphs does not admit a specification that starts from single atoms and involves only sums, products, sets and cycles. Indeed, the growth of G n is such that the EGF G(z) has radius of convergence 0, whereas EGFs of constructible classes must have a non-zero radius of convergence. (Section IV. 4, p. 249, provides a detailed proof of this fact for iterative structures; for recursively specified classes, this is a consequence of the analysis of inverse functions, p. 402, and systems, p. 489, with suitable adaptations based on the technique of majorant series. p. 250.) II. 6. Additional constructions As in the unlabelled case, pointing and substitution are available in the world of labelled structures (Subsection II. 6.1), and implicit definitions enlarge the scope of the symbolic method (Subsection II. 6.2). The inversion process needed to enumerate implicit structures is even simpler, since in the labelled universe sets and cycles have more concise translations as operators over EGF. Finally, and this departs significantly from Chapter I, the fact that integer labels are naturally ordered makes it possible to take into account certain order properties of combinatorial structures (Subsection II. 6.3). II. 6.1. Pointing and substitution. The pointing operation is entirely similar to its unlabelled counterpart since it consists in distinguishing one atom among all the ones that compose an object of size n. The definition of composition for labelled structures is however a bit more subtle as it requires singling out “leaders” in components. Pointing. The pointing of a class B is defined by A = 2B

iff

An = [1 . . n] × Bn .

In other words, in order to generate an element of A, select one of the n labels and point at it. Clearly d An = n · Bn H⇒ A(z) = z B(z). dz Substitution (composition). Composition or substitution can be introduced so that it corresponds a priori to composition of generating functions. It is formally defined as ∞ X Bk × S ETk (C), B◦C = k=0

so that its EGF is

∞ X k=0

Bk

(C(z))k = B(C(z)). k!

A combinatorial way of realizing this definition and forming an arbitrary object of B ◦ C, is as follows. First select an element of β ∈ B called the “base” and let k = |β| be its size; then pick up a k–set of elements of C; the elements of the k–set are naturally ordered by the value of their “leader” (the leader of an object being by convention the value of its smallest label); the element with leader of rank r is then substituted to the node labelled by value r of β. Gathering the above, we obtain:

II. 6. ADDITIONAL CONSTRUCTIONS

137

Theorem II.3. The combinatorial constructions of pointing and substitution are admissible d A = 2B H⇒ A(z) = z∂z B(z), ∂z ≡ dz A = B ◦ C H⇒ A(z) = B(C(z)). For instance, the EGF of (relabelled) pairings of elements drawn from C is eC(z)

2 /2

,

since the EGF of involutions without fixed points is e z

2 /2

.

II.25. Standard constructions based on substitutions. The sequence class of A may be defined by composition as P ◦ A where P is the set of all permutations. The set class of A may be defined as U ◦ A where U is the class of all urns. Similarly, cycles are obtained by substitution into circular graphs. Thus, ∼ U ◦ A, ∼ P ◦ A, C YC(A) ∼ S ET(A) = S EQ(A) = = C ◦ A.

In this way, permutation, urns and circle graphs appear as archetypal classes in a development of combinatorial analysis based on composition. (Joyal’s “theory of species” [359] and the book by Bergeron, Labelle, and Leroux [50] show that a far-reaching theory of combinatorial enumeration can be based on the concept of substitution.)

II.26. Distinct component sizes. The EGFs of permutations with cycles of distinct lengths and of set partitions with parts of distinct sizes are ∞ ∞ Y Y zn zn , . 1+ 1+ n n! n=1

n=1

The probability that a permutation of Pn has distinct cycle sizes tends to e−γ ; see [309, Sec. 4.1.6] for a Tauberian argument and [495] for precise asymptotics. The corresponding analysis for set partitions is treated in the seven-author paper [368].

II. 6.2. Implicit structures. Let X be a labelled class implicitly characterized by either of the combinatorial equations A = B + X,

A = B ⋆ X.

Then, solving the corresponding EGF equations leads to X (z) = A(z) − B(z),

X (z) =

A(z) , B(z)

respectively. For the composite labelled constructions S EQ, S ET, C YC, the algebra is equally easy. Theorem II.4 (Implicit specifications). The generating functions associated with the implicit equations in X A = S EQ(X ),

A = S ET(X ),

A = C YC(X ),

are, respectively, X (z) = 1 −

1 , A(z)

X (z) = log A(z),

X (z) = 1 − e−A(z) .

138

II. LABELLED STRUCTURES AND EGFS

Example II.15. Connected graphs. In the context of graphical enumerations, the labelled set construction takes the form of an enumerative formula relating a class G of graphs and the subclass K ⊂ G of its connected graphs: G = S ET(K)

H⇒

G(z) = e K (z) .

This basic formula is known in graph theory [319] as the exponential formula. Consider the class G of all (undirected) labelled graphs, the size of a graph being the number of its nodes. Since a graph is determined by the choice of its set of edges, there are n2 n potential edges each of which may be taken in or out, so that G = 2(2) . Let K ⊂ G be the n

subclass of all connected graphs. The exponential formula determines K (z) implicitly: X n zn K (z) = log 1 + 2(2) n! n≥1 (57) 3 2 z z4 z5 z + 4 + 38 + 728 + · · · , = z+ 2! 3! 4! 5! where the sequence is EIS A001187. The series is divergent, that is, it has radius of convergence 0. It can nonetheless be manipulated as a formal series (Appendix A.5: Formal power series, p. 730). Expanding by means of log(1 + u) = u − u 2 /2 + · · · , yields a complicated convolution expression for K n : n3 n2 n1 n1 n n 1X 1X n n +( 22 ) ( ) ( ) 2 2 Kn = 2 − 2 + 2( 2 )+( 2 )+( 2 ) − · · · . 2 3 n1, n2 n1, n2, n3

(The kth term is a sum over n 1 + · · · + n k = n, with 0 < n j < n.) Given the very fast increase of G n with n, for instance n+1 n 2( 2 ) = 2n 2(2) ,

a detailed analysis of the various terms of the expression of K n shows predominance of the first sum, and, in that sum itself, the extreme terms corresponding to n 1 = n − 1 or n 2 = n − 1 predominate, so that n (58) K n = 2(2) 1 − 2n2−n + o(2−n ) . Thus: almost all labelled graphs of size n are connected. In addition, the error term decreases very quickly: for instance, for n = 18, an exact computation based on the generating function formula reveals that a proportion only 0.0001373291074 of all the graphs are not connected— this is extremely close to the value 0.0001373291016 predicted by the main terms in the asymptotic formula (58). Notice that good use could be made here of a purely divergent generating function for asymptotic enumeration purposes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

II.27. Bipartite graphs. A plane bipartite graph is a pair (G, ω) where G is a labelled graph, ω = (ωW , ω E ) is a bipartition of the nodes (into West and East categories), and the edges are such that they only connect nodes from ωW to nodes of ω E . A direct count shows that the EGF of plane bipartite graphs is X zn X n γn Ŵ(z) = 2k(n−k) . with γn = k n! n k

The EGF of plane bipartite graphs that are connected is log Ŵ(z). A bipartite graph is a labelled graph whose nodes can be partitioned into two groups so that edges only connect nodes of different groups. The EGF of bipartite graphs is p 1 log Ŵ(z) = Ŵ(z). exp 2

II. 6. ADDITIONAL CONSTRUCTIONS

139

[Hint. The EGF of a connected bipartite graph is 21 log Ŵ(z), since a factor of 21 kills the East– West orientation present in a connected plane bipartite graph. See Wilf’s book [608, p. 78] for details.]

II.28. Do two permutations generate the symmetric group? To two permutations σ, τ of the same size, associate a graph Ŵσ,τ whose set vertices is V = [1 . . n], if n = |σ | = |τ |, and set of edges is formed of all the pairs (x, σ (x)), (x, τ (x)), for x ∈ V . The probability that a random Ŵσ,τ is connected is X 1 n n n!z . πn = [z ] log n! n≥0

This represents the probability that two permutations generate a transitive group (that is for all x, y ∈ [0 . . n], there exists a composition of σ, σ −1 , τ, τ −1 that maps x to y). One has 4 23 171 1542 1 1 (59) πn ∼ 1 − − 2 − 3 − 4 − 5 − 6 − · · · , n n n n n n Surprisingly, the coefficients 1, 1, 4, 23, . . . (EIS A084357) in the asymptotic formula (59) enumerate a “third-level” structure (Subsection II. 4.2, p. 124 and Note VIII.15, p. 571), namely: S ET(S ET≥1 (S EQ≥1 (Z))). In addition, one has n!2 πn = (n − 1)!In , where In+1 is the number of indecomposable permutations (Example I.19, p. 89). Let πn⋆ be the probability that two random permutations generate the whole symmetric group. Then, by a result of Babai based on the classification of groups, the quantity πn − πn⋆ is exponentially small, so that (59) also applies to πn⋆ ; see Dixon [167].

II. 6.3. Order constraints. A construction well-suited to dealing with many of the order properties of combinatorial structures is the modified labelled product: A = (B 2 ⋆ C). This denotes the subset of the product B⋆C formed with elements such that the smallest label is constrained to lie in the B component. (To make this definition consistent, it must be assumed that B0 = 0.) We call this binary operation on structures the boxed product. Theorem II.5. The boxed product is admissible: Z z d 2 (60) A = (B ⋆ C) H⇒ A(z) = ∂t ≡ . (∂t B(t)) · C(t) dt, dt 0 Proof. The definition of boxed products implies the coefficient relation n X n−1 Bk Cn−k . An = k−1 k=1

The binomial coefficient that appears in the standard convolution, Equation (2), p. 100, is to be modified since only n −1 labels need to be distributed between the two components: k − 1 go to the B component (that is already constrained to contain the label 1) and n − k to the C component. From the equivalent form n 1X n An = (k Bk ) Cn−k , k n k=0

the result follows by taking EGFs, via A(z) = (∂z B(z)) · C(z).

140

II. LABELLED STRUCTURES AND EGFS

2.5

2

1.5

1

0.5 0

20

40

60

80

100

Figure II.15. A numerical sequence of size 100 with records marked by circles: there are 7 records that occur at times 1, 3, 5, 11, 60, 86, 88.

A useful special case is the min-rooting operation, A = Z2 ⋆ C ,

for which a variant definition goes as follows: take in all possible ways elements γ ∈ C, prepend an atom with a label, for instance 0, smaller than the labels of γ , and relabel in the canonical way over [1 . . (n +1)] by shifting all label values by 1. Clearly An+1 = Cn , which yields Z z

A(z) =

C(t) dt,

0

a result which is also consistent with the general formula (60) of boxed products. For some applications, it is convenient to impose constraints on the maximal label rather than the minimum. The max-boxed product written A = (B ⋆ C),

is then defined by the fact the maximum is constrained to lie in the B–component of the labelled product. Naturally, translation by an integral in (60) remains valid for this trivially modified boxed product.

II.29. Combinatorics of integration. In the perspective of this book, integration by parts has an immediate interpretation. Indeed, the equality Z z Z z A′ (t) · B(t) dt + A(t) · B ′ (t) dt = A(z) · B(z) 0

0

reads: “The smallest label in an ordered pair appears either on the left or on the right.”

Example II.16. Records in permutations. Given a sequence of numbers x = (x1 , . . . , xn ), assumed all distinct, a record is defined to be an element x j such that xk < x j for all k < j. (A record is an element “better” than its predecessors!) Figure II.15 displays a numerical sequence of length n = 100 that has 7 records. Confronted by such data, a statistician will typically want to determine whether the data obey purely random fluctuations or if there could be some indications of a “trend” or of a “bias” [139, Ch. 10]. (Think of the data as reflecting share prices or athletic records, say.) In particular, if the x j are independently drawn from a continuous distribution, then the number of records obeys the same laws as in a random permutation of

II. 6. ADDITIONAL CONSTRUCTIONS

141

[1 . . n]. This statistical preamble then invites the question: How many permutations of n have k records? First, we start with a special brand of permutations, the ones that have their maximum at the beginning. Such permutations are defined as (“” indicates the boxed product based on the maximum label) Q = (Z ⋆ P), where P is the class of all permutations. Observe that this gives the EGF Z z d 1 1 Q(z) = t · dt = log , dt 1−t 1−z 0 implying the obvious result Q n = (n − 1)! for all n ≥ 1. These are exactly the permutations with one record. Next, consider the class P (k) = S ETk (Q). The elements of P (k) are unordered sets of cardinality k with elements of type Q. Define the max–leader (“el lider m´aximo”) of any component of P (k) as the value of its maximal element. Then, if we place the components in sequence, ordered by increasing values of their leaders, then read off the whole sequence, we obtain a permutation with exactly k records. The correspondence8 is clearly revertible. Here is an illustration, with leaders underlined:

(7, 2, 6, 1), (4, 3), (9, 8, 5)

∼ =

∼ =

(4, 3), (7, 2, 6, 1), (9, 8, 5) ]

4, 3, 7, 2, 6, 1, 9, 8, 5.

Thus, the number of permutations with k records is determined by 1 P (k) (z) =

k!

log

k 1 , 1−z

n (k) , Pn = k

where we recognize Stirling cycle numbers from Example II.12, p. 121. In other words: The number of permutations of size n having k records is counted by the Stirling “cycle” number nk .

Returning to our statistical problem, the treatment of Example II.12 p. 121 (to be revisited in Chapter III, p. 189) shows that the expected number of records in a random permutation of . size n equals Hn , the harmonic number. One has H100 = 5.18, so that for 100 data items, a little more than 5 records are expected on average. The probability of observing 7 records or more is still about 23%, an altogether not especially rare event. In contrast, observing twice as many records as we did, namely 14, would be a fairly strong indication of a bias—on random data, the event has probability very close to 10−4 . Altogether, the present discussion is consistent with the hypothesis for the data of Figure II.15 to have been generated independently at random (and indeed they were). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

8This correspondence can also be viewed as a transformation on permutations that maps the number

of records to the number of cycles—it is known as Foata’s fundamental correspondence [413, Sec. 10.2].

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II. LABELLED STRUCTURES AND EGFS

It is possible to base a fair part of the theory of labelled constructions on sums and products in conjunction with the boxed product. In effect, consider the three relations 1 , 1 − g(z)

F = S EQ(G)

H⇒

f (z) =

F = S ET(G)

H⇒

f (z) = e g(z) ,

F = C YC(G) H⇒

f (z) = log

1 , 1 − g(z)

f = 1 + gf Z f = 1 + g′ f Z 1 f = g′ . 1−g

The last column is easily checked, by standard calculus, to provide an alternative form of the standard operator corresponding to sequences, sets, and cycles. Each case can in fact be deduced directly from Theorem II.5 and the labelled product rule as follows. (i) Sequences: they obey the recursive definition F = S EQ(G)

H⇒

F∼ = {ǫ) + (G ⋆ F).

(ii) Sets: we have F = S ET(G)

H⇒

F∼ = {ǫ} + (G ⋆ F),

which means that, in a set, one can always single out the component with the largest label, the rest of the components forming a set. In other words, when this construction is repeated, the elements of a set can be canonically arranged according to increasing values of their largest labels, the “leaders”. (We recognize here a generalization of the construction used for records in permutations.) (iii) Cycles: The element of a cycle that contains the largest label can be taken canonically as the cycle “starter”, which is then followed by an arbitrary sequence of elements upon traversing the cycle in cyclic order. Thus F = C YC(G)

H⇒

F∼ = (G ⋆ S EQ(G)).

Greene [308] has developed a complete framework of labelled grammars based on standard and boxed labelled products. In its basic form, its expressive power is essentially equivalent to ours, because of the above relations. More complicated order constraints, dealing simultaneously with a collection of larger and smaller elements, can be furthermore taken into account within this framework.

II.30. Higher order constraints, after Greene. Let the symbols , ⊡, represent smallest, d ) second smallest, and largest labels, respectively. One has the correspondences (with ∂z = dz A = B2 ⋆ C ∂z2 A(z) = (∂z B(z)) · (∂z C(z)) A = B2 ⋆ C ∂z2 A(z) = ∂z2 B(z) · C(z) A = B2 ⋆ C ⊡ ⋆ D ∂ 3 A(z) = (∂ B(z)) · (∂ C(z)) · (∂ D(z)) , z

z

z

z

and so on. These can be transformed into (iterated) integral representations. (See [308] for more.)

The next three examples demonstrate the utility of min/max-rooting used in conjunction with recursion. Examples II.17 and II.18 introduce two important classes of

II. 6. ADDITIONAL CONSTRUCTIONS

143

1 3

5

2

6

4

7

5

7

3

4

1

6

2

Figure II.16. A permutation of size 7 and its increasing binary tree lifting.

trees that are tightly linked to permutations. Example II.19 provides a simple symbolic solution to a famous parking problem, on which many analyses can be built. Example II.17. Increasing binary trees and alternating permutations. To each permutation, one can associate bijectively a binary tree of a special type called an increasing binary tree and sometimes a heap-ordered tree or a tournament tree. This is a plane rooted binary tree in which internal nodes bear labels in the usual way, but with the additional constraint that node labels increase along any branch stemming from the root. Such trees are closely related to many classical data structures of computer science, such as heaps and binomial queues. The correspondence (Figure II.16) is as follows: Given a permutation written as a word, σ = σ1 σ2 . . . σn , factor it into the form σ = σ L · min(σ ) · σ R , with min(σ ) the smallest label value in the permutation, and σ L , σ R the factors left and right of min(σ ). Then the binary tree β(σ ) is defined recursively in the format hroot, left, righti by β(σ ) = hmin(σ ), β(σ L ), β(σ R )i,

β(ǫ) = ǫ.

The empty tree (consisting of a unique external node of size 0) goes with the empty permutation ǫ. Conversely, reading the labels of the tree in symmetric (infix) order gives back the original permutation. (The correspondence is described for instance in Stanley’s book [552, p. 23–25] who says that “it has been primarily developed by the French”, pointing at [267].) Thus, the family I of binary increasing trees satisfies the recursive definition (61) I = {ǫ} + Z 2 ⋆ I ⋆ I , which implies the nonlinear integral equation for the EGF Z z I (z) = 1 + I (t)2 dt.

0 ′ 2 This equation reduces to I (z) = I (z) and, under the initial condition I (0) = 1, it admits the solution I (z) = (1 − z)−1 . Thus In = n!, which is consistent with the fact that there are as

many increasing binary trees as there are permutations. The construction of increasing trees is instrumental in deriving EGFs relative to various local order patterns in permutations. We illustrate its use here by counting the number of up-and-down (or zig-zag) permutations, also known as alternating permutations. The result,

144

II. LABELLED STRUCTURES AND EGFS

already mentioned in our Invitation chapter (p. 2) was first derived by D´esir´e Andr´e in 1881 by means of a direct recurrence argument. A permutation σ = σ1 σ2 · · · σn is an alternating permutation if σ1 > σ2 < σ3 > σ4 < · · · ,

(62)

so that pairs of consecutive elements form a succession of ups and downs; for instance, 7

6

2 6

2

4

3

5

1 3

1

7

4

5

Consider first the case of an alternating permutation of odd size. It can be checked that the corresponding increasing trees have no one-way branching nodes, so that they consist solely of binary nodes and leaves. Thus, the corresponding specification is J = Z + Z2 ⋆ J ⋆ J , so that

Z z

d J (z) = 1 + J (z)2 . dz The equation admits separation of variables, which implies, since J (0) = 0, that arctan(J (z)) = z, hence: z5 z7 z3 J (z) = tan(z) = z + 2 + 16 + 272 + · · · . 3! 5! 7! The coefficients J2n+1 are known as the tangent numbers or the Euler numbers of odd index (EIS A000182). Alternating permutations of even size defined by the constraint (62) and denoted by K can be determined from K = {ǫ} + Z 2 ⋆ J ⋆ K , J (z) = z +

J (t)2 dt

and

0

since now all internal nodes of the tree representation are binary, except for the right-most one that only branches on the left. Thus, K ′ (z) = tan(z)K (z), and the EGF is K (z) =

1 z2 z4 z6 z8 = 1 + 1 + 5 + 61 + 1385 + · · · , cos(z) 2! 4! 6! 8!

where the coefficients K 2n are the secant numbers also known as Euler numbers of even index (EIS A000364). Use will be made later in this book (Chapter III, p. 202) of this important tree representation of permutations as it opens access to parameters such as the number of descents, runs, and (once more!) records in permutations. Analyses of increasing trees also inform us of crucial performance issues regarding binary search trees, quicksort, and heap-like priority queue structures [429, 538, 598, 600]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . z , tan tan z, tan(e z − 1) as EGFs of II.31. Combinatorics of trigonometrics. Interpret tan 1−z

combinatorial classes.

II. 6. ADDITIONAL CONSTRUCTIONS

145

8

1 x 5

2

6

3

5

x x

4 8

4

x

3

x 7

2

6 x

x

1

x

−

x

9

7

1

2

3

4

5

6

7 8

9

Figure II.17. An increasing Cayley tree (left) and its associated regressive mapping (right).

Example II.18. Increasing Cayley trees and regressive mappings. An increasing Cayley tree is a Cayley tree (i.e., it is labelled, non-plane, and rooted) whose labels along any branch stemming from the root form an increasing sequence. In particular, the minimum must occur at the root, and no plane embedding is implied. Let L be the class of such trees. The recursive specification is now L = Z 2 ⋆ S ET(L) .

The generating function thus satisfies the functional relations Z z L(z) = e L(t) dt, L ′ (z) = e L(z) ,

0 ′ −L with L(0) = 0. Integration of L e = 1 shows that e−L = 1 − z, hence

1 and L n = (n − 1)!. 1−z Thus the number of increasing Cayley trees is (n−1)!, which is also the number of permutations of size n − 1. These trees have been studied by Meir and Moon [435] under the name of “recursive trees”, a terminology that we do not, however, retain here. The simplicity of the formula L n = (n − 1)! certainly calls for a combinatorial interpretation. In fact, an increasing Cayley tree is fully determined by its child–parent relationship (Figure II.17). In other words, to each increasing Cayley tree τ , we associate a partial map φ = φτ such that φ(i) = j iff the label of the parent of i is j. Since the root of tree is an orphan, the value of φ(1) is undefined, φ(1) =⊥; since the tree is increasing, one has φ(i) < i for all i ≥ 2. A function satisfying these last two conditions is called a regressive mapping. The correspondence between trees and regressive mappings is then easily seen to be bijective. Thus regressive mappings on the domain [1 . . n] and increasing Cayley trees are equinumerous, so that we may as well use L to denote the class of regressive mappings. Now, a regressive mapping of size n is evidently determined by a single choice for φ(2) (since φ(2) = 1), two possible choices for φ(3) (either of 1, 2), and so on. Hence the formula L(z) = log

L n = 1 × 2 × 3 × · · · × (n − 1) receives a natural interpretation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

II.32. Regressive mappings and permutations. Regressive mappings can be related directly to permutations. The construction that associates a regressive mapping to a permutation is

146

II. LABELLED STRUCTURES AND EGFS

called the “inversion table” construction; see [378, 538]. Given a permutation σ = σ1 , . . . , σn , associate to it a function ψ = ψσ from [1 . . n] to [0 . . n − 1] by the rule ψ( j) = card k < j σk > σ j . The function ψ is a trivial variant of a regressive mapping.

II.33. Rotations and increasing trees. An increasing Cayley tree can be canonically drawn by ordering descendants of each node from left to right according to their label values. The rotation correspondence (p. 73) then gives rise to a binary increasing tree. Hence, increasing Cayley trees and increasing binary trees are also directly related. Summarizing this note and the previous one, we have a quadruple combinatorial connection, ∼ Regressive mappings = ∼ Permutations ∼ Increasing Cayley trees = = Increasing binary trees, which opens the way to yet more permutation enumerations. Example II.19. A parking problem. Here is Knuth’s introduction to the problem, dating back from 1973 (see [378, p. 545]), which nowadays might be regarded by some as politically incorrect: “A certain one-way street has m parking spaces in a row numbered 1 to m. A man and his dozing wife drive by, and suddenly, she wakes up and orders him to park immediately. He dutifully parks at the first available space [. . . ].”

Consider n = m − 1 cars and condition by the fact that everybody eventually finds a parking space and the last space remains empty. There are m n = (n + 1)n possible sequences of “wishes”, among which only a certain number Fn satisfy the condition—this number is to be determined. (An important motivation for this problem is the analysis of hashing algorithms examined in Note III.11, p. 178, under the “linear probing” strategy.) A sequence satisfying the condition called an almost-full allocation, its size n being the number of cars involved. Let F represent the class of almost-full allocations. We claim the decomposition: h i (63) F = (2F + F ) ⋆ Z ⋆ F .

Indeed, consider the car that arrived last, before it will eventually land in some position k + 1 from the left. Then, there are two islands, which are themselves almost-full allocations (of respective sizes k and n − k − 1). This last car’s intended parking wish must have been either one of the first k occupied cells on the left (the factor 2F in (63)) or the last empty cell of the first island (the term F in the left factor); the right island is not affected (the factor F on the right). Finally, the last car is inserted into the street (the factor Z ). Pictorially, we have a sort of binary tree decomposition of almost-full allocations:

Analytically, the translation of (63) into EGF is Z z (64) F(z) = (w F ′ (w) + F(w))F(w) dw, 0

which, through differentiation gives (65)

F ′ (z) = (z F(z))′ · F(z).

II. 7. PERSPECTIVE

147

Simple manipulations do the rest: we have F ′ /F = (z F)′ , which by integration gives log F = (z F) and F = e z F . Thus F(z) satisfies a functional equation strangely similar to that of the Cayley tree function T (z); indeed, it is not hard to see that one has 1 (66) F(z) = T (z) and Fn = (n + 1)n−1 , z which solves the original counting problem. The derivation above is based on articles by Flajolet, Poblete, Viola, and Knuth [249, 380], who show that probabilistic properties of parking allocations can be precisely analysed (for instance, total displacement, examined in Note VII.54, p. 534, is found to be governed by an Airy distribution). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

II. 7. Perspective Together with the previous chapter and Figure I.18, this chapter and Figure II.18 provide the basis for the symbolic method that is at the core of analytic combinatorics. The translations of the basic constructions for labelled classes to EGFs could hardly be simpler, but, as we have seen, they are sufficiently powerful to embrace numerous classical results in combinatorics, ranging from the birthday and coupon collector problems to tree and graph enumeration. The examples that we have considered for second-level structures, trees, mappings, and graphs lead to EGFs that are simple to express and natural to generalize. (Often, the simple form is misleading—direct derivations of many of these EGFs that do not appeal to the symbolic method can be rather intricate.) Indeed, the symbolic method provides a framework that allows us to understand the nature of many of these combinatorial classes. From here, numerous seemingly scattered counting problems can be organized into broad structural categories and solved in an almost mechanical manner. Again, the symbolic method is only half of the story (the “combinatorics” in analytic combinatorics), leading to EGFs for the counting sequences of numerous interesting combinatorial classes. While some of these EGFs lead immediately to explicit counting results, others require classical techniques in complex analysis and asymptotic analysis that are covered in Part B (the “analytic” part of analytic combinatorics) to deliver asymptotic estimates. Together with these techniques, the basic constructions, translations, and applications that we have discussed in this chapter reinforce the overall message that the symbolic method is a systematic approach that is successful for addressing classical and new problems in combinatorics, generalizations, and applications. We have been focusing on enumeration problems—counting the number of objects of a given size in a combinatorial class. In the next chapter, we shall consider how to extend the symbolic method to help analyse other properties of combinatorial classes. Bibliographic notes. The labelled set construction and the exponential formula were recognized early by researchers working in the area of graphical enumerations [319]. Foata [265] proposed a detailed formalization in 1974 of labelled constructions, especially sequences and sets, under the names of partitional complex; a brief account is also given by Stanley in his survey [550]. This is parallel to the concept of “prefab” due to Bender and Goldman [42]. The

148

II. LABELLED STRUCTURES AND EGFS

1. The main constructions of union, and product, sequence, set, and cycle for labelled structures together with their translation into exponential generating functions. Construction

EGF

Union

A=B+C

A(z) = B(z) + C(z)

Product

A=B⋆C

Sequence

A = S EQ(B)

Set

A = S ET(B)

Cycle

A = C YC(B)

A(z) = B(z) · C(z) 1 A(z) = 1 − B(z) A(z) = exp(B(z)) 1 A(z) = log 1 − B(z)

2. Sets, multisets, and cycles of fixed cardinality. Construction

EGF

Sequence

A = S EQk (B)

Set

A = S ETk (B)

Cycle

A = C YCk (B)

A(z) = B(z)k 1 A(z) = B(z)k k! 1 A(z) = B(z)k k

3. The additional constructions of pointing and substitution. Construction

EGF

Pointing

A = 2B

d B(z) A(z) = z dz

Substitution

A=B◦C

A(z) = B(C(z))

4. The “boxed” product. A = (B2 ⋆ C) H⇒ A(z) =

Z z d B(t) · C(t) dt. dt 0

Figure II.18. A “dictionary” of labelled constructions together with their translation into exponential generating functions (EGFs). The first constructions are counterparts of the unlabelled constructions of the previous chapter (the multiset construction is not meaningful here). Translation for composite constructions of bounded cardinality appears to be simple. Finally, the boxed product is specific to labelled structures. (Compare with the unlabelled counterpart, Figure I.18, p. 18.)

books by Comtet [129], Wilf [608], Stanley [552], or Goulden and Jackson [303] have many examples of the use of labelled constructions in combinatorial analysis. Greene [308] has introduced in his 1983 dissertation a general framework of “labelled grammars” largely based on the boxed product with implications for the random generation of combinatorial structures. Joyal’s theory of species dating from 1981 (see [359] for the original

II. 7. PERSPECTIVE

149

article and the book by Bergeron, Labelle, and Leroux [50] for a rich exposition) is based on category theory; it presents the advantage of uniting in a common framework the unlabelled and the labelled worlds. Flajolet, Salvy, and Zimmermann have developed a specification language closely related to the system expounded here. They show in [255] how to compile automatically specifications into generating functions; this is complemented by a calculus that produces fast random generation algorithms [264].

I can see looming ahead one of those terrible exercises in probability where six men have white hats and six men have black hats and you have to work it out by mathematics how likely it is that the hats will get mixed up and in what proportion. If you start thinking about things like that, you would go round the bend. Let me assure you of that! —AGATHA C HRISTIE (The Mirror Crack’d. Toronto, Bantam Books, 1962.)

III

Combinatorial Parameters and Multivariate Generating Functions Generating functions find averages, etc. — H ERBERT W ILF [608]

III. 1. III. 2. III. 3. III. 4. III. 5. III. 6. III. 7. III. 8. III. 9.

An introduction to bivariate generating functions (BGFs) 152 Bivariate generating functions and probability distributions 156 Inherited parameters and ordinary MGFs 163 Inherited parameters and exponential MGFs 174 Recursive parameters 181 Complete generating functions and discrete models 186 Additional constructions 198 Extremal parameters 214 Perspective 218

Many scientific endeavours demand precise quantitative information on probabilistic properties of parameters of combinatorial objects. For instance, when designing, analysing, and optimizing a sorting algorithm, it is of interest to determine the typical disorder of data obeying a given model of randomness, and to do so in the mean, or even in distribution, either exactly or asymptotically. Similar situations arise in a broad variety of fields, including probability theory and statistics, computer science, information theory, statistical physics, and computational biology. The exact problem is then a refined counting problem with two parameters, namely, size and an additional characteristic: this is the subject addressed in this chapter and treated by a natural extension of the generating function framework. The asymptotic problem can be viewed as one of characterizing in the limit a family of probability laws indexed by the values of the possible sizes: this is a topic to be discussed in Chapter IX. As demonstrated here, the symbolic methods initially developed for counting combinatorial objects adapt gracefully to the analysis of various sorts of parameters of constructible classes, unlabelled and labelled alike. Multivariate generating functions (MGFs)—ordinary or exponential—can keep track of a collection of parameters defined over combinatorial objects. From the knowledge of such generating functions, there result either explicit probability distributions or, at least, mean and variance evaluations. For inherited parameters, all the combinatorial classes discussed so far are amenable to such a treatment. Technically, the translation schemes that relate combinatorial constructions and multivariate generating functions present no major difficulty—they appear to be natural (notational, even) refinements of the paradigm developed in Chapters I and II for the univariate case. Typical applications from classical combinatorics are the number of summands 151

152

III. PARAMETERS AND MULTIVARIATE GFS

in a composition, the number of blocks in a set partition, the number of cycles in a permutation, the root degree or path length of a tree, the number of fixed points in a permutation, the number of singleton blocks in a set partition, the number of leaves in trees of various sorts, and so on. Beyond its technical aspects anchored in symbolic methods, this chapter also serves as a first encounter with the general area of random combinatorial structures. The general question is: What does a random object of large size look like? Multivariate generating functions first provide an easy access to moments of combinatorial parameters—typically the mean and variance. In addition, when combined with basic probabilistic inequalities, moment estimates often lead to precise characterizations of properties of large random structures that hold with high probability. For instance, a large integer partition conforms with high probability to a deterministic profile, a large random permutation almost surely has at least one long cycle and a few short ones, and so on. Such a highly constrained behaviour of large objects may in turn serve to design dedicated algorithms and optimize data structures; or it may serve to build statistical tests—when does one depart from randomness and detect a “signal” in large sets of observed data? Randomness forms a recurrent theme of the book: it will be developed much further in Chapter IX, where the complex asymptotic methods of Part B are grafted on the exact modelling by multivariate generating functions presented in this chapter. This chapter is organized as follows. First a few pragmatic developments related to bivariate generating functions are presented in Section III. 1. Next, Section III. 2 presents the notion of bivariate enumeration and its relation to discrete probabilistic models, including the determination of moments, since the language of elementary probability theory does indeed provide an intuitively appealing way to conceive of bivariate counting data. The symbolic method per se, declined in its general multivariate version, is centrally developed in Sections III. 3 and III. 4: with suitable multi-index notations, the extension of the symbolic method to the multivariate case is almost immediate. Recursive parameters that often arise in particular from tree statistics form the subject of Section III. 5, while complete generating functions and associated combinatorial models are discussed in Section III. 6. Additional constructions such as pointing, substitution, and order constraints lead to interesting developments, in particular, an original treatment of the inclusion–exclusion principle in Section III. 7. The chapter concludes, in Section III. 8, with a brief abstract discussion of extremal parameters like height in trees or smallest and largest components in composite structures— such parameters are best treated via families of univariate generating functions. III. 1. An introduction to bivariate generating functions (BGFs) We have seen in Chapters I and II that a number sequence ( f n ) can be encoded by means of a generating function in one variable, either ordinary or exponential: X f n z n (ordinary GF) n ( fn ) ; f (z) = X zn (exponential GF). fn n! n

III. 1. AN INTRODUCTION TO BIVARIATE GENERATING FUNCTIONS (BGFS)

f 00 f 10

f 11

f 20 .. .

f 21 .. .

f 22 .. .

↓

↓

↓

f h0i (z)

f h1i (z)

−→

f 0 (u)

−→

f 1 (u)

−→

f 2 (u)

153

f h2i (z)

Figure III.1. An array of numbers and its associated horizontal and vertical GFs.

This encoding is powerful, since many combinatorial constructions admit a translation as operations over such generating functions. In this way, one gains access to many useful counting formulae. Similarly, consider a sequence of numbers ( f n,k ) depending on two integer-valued indices, n and k. Usually, in this book, ( f n,k ) will be an array of numbers (often a triangular array), where f n,k is the number of objects ϕ in some class F, such that |ϕ| = n and some parameter χ (ϕ) is equal to k. We can encode this sequence by means of a bivariate generating function (BGF) involving two variables: a primary variable z attached to n and a secondary u attached to k. Definition III.1. The bivariate generating functions (BGFs), either ordinary or exponential, of an array ( f n,k ) are the formal power series in two variables defined by X f n,k z n u k n,k f (z, u) = X zn k u f n,k n!

(ordinary BGF) (exponential BGF).

n,k

n

k

(The “double exponential” GF corresponding to zn! uk! is not used in the book.) As we shall see shortly, parameters of constructible classes become accessible through such BGFs. According to the point of view adopted for the moment, one starts with an array of numbers and forms a BGF by a double summation process. We present here two examples related to binomial coefficients and Stirling cycle numbers illustrating how such BGFs can be determined, then manipulated. In what follows it is convenient to refer to the horizontal and vertical generating functions (Figure III.1) that are each a one-parameter family of GFs in a single variable defined by horizontal GF: vertical GF:

f n (u)

:=

f hki (z) := f

hki

(z) :=

P

P

P

k n

f n,k u k ; f n,k z n

zn n f n,k n!

(ordinary case) (exponential case).

154

III. PARAMETERS AND MULTIVARIATE GFS

(0) (1)

(2)

(3)

(4) (5)

Figure III.2. The set W5 of the 32 binary words over the alphabet {, } enumerated according to the number of occurrences of the letter ‘’ gives rise to the bivariate counting sequence {W5, j } = 1, 5, 10, 10, 5, 1.

The terminology is transparently explained if the elements ( f n,k ) are arranged as an infinite matrix, with f n,k placed in row n and column k, since the horizontal and vertical GFs appear as the GFs of the rows and columns respectively. Naturally, one has X f n (u)z n (ordinary BGF) X n f (z, u) = u k f hki (z) = X zn f n (u) (exponential BGF). k n! n Example III.1. The ordinary BGF of binomial coefficients. The binomial coefficient nk counts binary words of length n having k occurrences of a designated letter; see Figure III.2. In order to compose the bivariate GF, start from the simplest case of Newton’s binomial theorem and directly form the horizontal GFs corresponding to a fixed n: n X n k (1) Wn (u) := u = (1 + u)n , k k=0

Then a summation over all values of n gives the ordinary BGF X n X 1 . (2) W (z, u) = uk zn = (1 + u)n z n = 1 − z(1 + u) k k,n≥0

n≥0

Such calculations are typical of BGF manipulations. What we have done amounts to starting from a sequence of numbers, Wn,k , determining the horizontal GFs Wn (u) in (1), then the bivariate GF W (z, u) in (2), according to the scheme: Wn,k

;

Wn (u)

;

W (z, u).

The BGF in (2) reduces to the OGF (1 − 2z)−1 of all words, as it should, upon setting u = 1. In addition, one can deduce from (2) the vertical GFs of the binomial coefficients corresponding to a fixed value of k X n zk W hki (z) = zn = , k (1 − z)k+1 n≥0

III. 1. AN INTRODUCTION TO BIVARIATE GENERATING FUNCTIONS (BGFS)

155

from an expansion of the BGF with respect to u W (z, u) =

(3)

X zk 1 1 = uk , z 1 − z 1 − u 1−z (1 − z)k+1 k≥0

and the result naturally matches what a direct calculation would give. . . . . . . . . . . . . . . . . . . . .

III.1. The exponential BGF of binomial coefficients. This is (4)

e (z, u) = W

X n k,n

k

uk

X zn zn = (1 + u)n = e z(1+u) . n! n!

The vertical GFs are e z z k /k!. The horizontal GFs are (1 + u)n , as in the ordinary case.

Example III.2. The exponential BGF of Stirling cycle numbers. As seen Example II.12, p. 121, the number Pn,k of permutations of size n having k cycles equals the Stirling cycle number nk , a vertical EGF being X n z n L(z)k 1 = , L(z) := log . P hki (z) := k n! k! 1 − z n From this, the exponential BGF is formed as follows (this revisits the calculations on p. 121): (5)

P(z, u) :=

X k

P hki (z)u k =

X uk L(z)k = eu L(z) = (1 − z)−u . k! k

The simplification is quite remarkable but altogether quite typical, as we shall see shortly, in the context of a labelled set construction. The starting point is thus a collection of vertical EGFs and the scheme is now hki

Pn

;

P hki (z)

;

P(z, u).

The BGF in (5) reduces to the EGF (1 − z)−1 of all permutations, upon setting u = 1. Furthermore, an expansion of the BGF in terms of the variable z provides useful information; namely, the horizontal GF is obtained by Newton’s binomial theorem: X n + u − 1 X zn P(z, u) = zn = Pn (u) , n! n (6) n≥0 n≥0 where

Pn (u)

=

u(u + 1) · · · (u + n − 1).

This last polynomial is called the Stirling cycle polynomial of index n and it describes completely the distribution of the number of cycles in all permutations of size n. In addition, the relation Pn (u) = Pn−1 (u)(u + (n − 1)), is equivalent to the recurrence n n−1 n−1 = (n − 1) + , k k k−1 by which Stirling numbers are often defined and easily evaluated numerically; see also Appendix A.8: Stirling numbers, p. 735. (The recurrence is susceptible to a direct combinatorial interpretation—add n either to an existing cycle or as a “new” singleton.) . . . . . . . . . . . . . . . .

156

III. PARAMETERS AND MULTIVARIATE GFS

Numbers n k

Horizontal GFs

Vertical OGFs zk (1 − z)k+1

Ordinary BGF 1 1 − z(1 + u)

(1 + u)n

Numbers n k Vertical EGFs k 1 1 log k! 1−z

Horizontal GFs u(u + 1) · · · (u + n − 1) Exponential BGF (1 − z)−u

Figure III.3. The various GFs associated with binomial coefficients (left) and Stirling cycle numbers (right).

Concise expressions for BGFs, like (2), (3), (5), or (18), are summarized in Figure III.3; they are invaluable for deriving moments, variance, and even finer characteristics of distributions, as we see next. The determination of such BGFs can be covered by a simple extension of the symbolic method, as will be detailed in Sections III. 3 and III. 4. III. 2. Bivariate generating functions and probability distributions Our purpose in this book is to analyse characteristics of a broad range of combinatorial types. The eventual goal of multivariate enumeration is the quantification of properties present with high regularity in large random structures. We shall be principally interested in enumeration according to size and an auxiliary parameter, the corresponding problems being naturally treated by means of BGFs. In order to avoid redundant definitions, it proves convenient to introduce the sequence of fundamental factors (ωn )n≥0 , defined by (7)

ωn = 1

for ordinary GFs,

ωn = n! for exponential GFs.

Then, the OGF and EGF of a sequence ( f n ) are jointly represented as X zn f (z) = fn and f n = ωn [z n ] f (z). ωn

Definition III.2. Given a combinatorial class A, a (scalar) parameter is a function from A to Z≥0 that associates to any object α ∈ A an integer value χ (α). The sequence An,k = card {α ∈ A |α| = n, χ (α) = k} , is called the counting sequence of the pair A, χ . The bivariate generating function (BGF) of A, χ is defined as X zn A(z, u) := An,k u k , ωn n,k≥0

and is ordinary if ωn ≡ 1 and exponential if ωn ≡ n!. One says that the variable z marks size and the variable u marks the parameter χ .

III. 2. BIVARIATE GENERATING FUNCTIONS AND PROBABILITY DISTRIBUTIONS

157

Naturally A(z, 1) reduces to the usual counting generating function A(z) associated with A, and the cardinality of An is expressible as An = ωn [z n ]A(z, 1). III. 2.1. Distributions and moments. Within this subsection, we examine the relationship between probabilistic models needed to interpret bivariate counting sequences and bivariate generating functions. The elementary notions needed are recalled in Appendix A.3: Combinatorial probability, p. 727. Consider a combinatorial class A. The uniform probability distribution over An assigns to any α ∈ An a probability equal to 1/An . We shall use the symbol P to denote probability and occasionally subscript it with an indication of the probabilistic model used, whenever this model needs to be stressed: we shall then write PAn (or simply Pn if A is understood) to indicate probability relative to the uniform distribution over An . Probability generating functions. Consider a parameter χ . It determines over each An a discrete random variable defined over the discrete probability space An : (8)

PAn (χ = k) =

An,k An,k . =P An k An,k

Given a discrete random variable X , typically, a parameter χ taken over a subclass An , we recall that its probability generating function (PGF) is by definition the quantity X (9) p(u) = P(X = k)u k . k

From (8) and (9), one has immediately: Proposition III.1 (PGFs from BGFs). Let A(z, u) be the bivariate generating function of a parameter χ defined over a combinatorial class A. The probability generating function of χ over An is given by X [z n ]A(z, u) , PAn (χ = k)u k = n [z ]A(z, 1) k

and is thus a normalized version of a horizontal generating function. The translation into the language of probability enables us to make use of whichever intuition might be available in any particular case, while allowing for a natural interpretation of data (Figure III.4). Indeed, instead of noting that the quantity 381922055502195 represents the number of permutations of size 20 that have 10 cycles, it is perhaps more informative to state the probability of the event, which is 0.00015, i.e., about 1.5 per 10 000. Discrete distributions are conveniently represented by histograms or “bar charts”, where the height of the bar at abscissa k indicates the value of P{X = k}. Figure III.4 displays two classical combinatorial distributions in this way. Given the uniform probabilistic model that we have been adopting, such histograms are eventually nothing but a condensed form of the “stacks” corresponding to exhaustive listings, like the one displayed in Figure III.2.

158

III. PARAMETERS AND MULTIVARIATE GFS

0.1

0.2

0.08 0.15 0.06 0.1 0.04 0.05

0.02 0

10

20

30

40

50

0

10

20

30

40

50

Figure III.4. Histograms of two combinatorial distributions. Left: the number of occurrences of a designated letter in a random binary word of length 50 (binomial distribution). Right: the number of cycles in a random permutation of size 50 (Stirling cycle distribution).

Moments. Important information is conveyed by moments. Given a discrete random variable X , the expectation of f (X ) is by definition the linear functional X E( f (X )) := P{X = k} · f (k). k

The (power) moments are E(X r ) :=

X k

P{X = k} · k r .

Then the expectation (or average, mean) of X , its variance, and its standard deviation, respectively, are expressed as p E(X ), V(X ) = E(X 2 ) − E(X )2 , σ (X ) = V(X ).

The expectation corresponds to what is typically seen when forming the arithmetic mean value of a large number of observations: this property is the weak law of large numbers [205, Ch X]. The standard deviation then measures the dispersion of values observed from the expectation and it does so in a mean-quadratic sense. The factorial moment defined for order r as (10)

E (X (X − 1) · · · (X − r + 1))

is also of interest for computational purposes, since it is obtained plainly by differentiation of PGFs (Appendix A.3: Combinatorial probability, p. 727). Power moments are then easily recovered as linear combinations of factorial moments, see Note III.9 of Appendix A. In summary: Proposition III.2 (Moments from BGFs). The factorial moment of order r of a parameter χ is determined from the BGF A(z, u) by r -fold differentiation followed by evaluation at 1: [z n ]∂ur A(z, u) u=1 EAn (χ (χ − 1) · · · (χ − r + 1)) = . [z n ]A(z, 1)

III. 2. BIVARIATE GENERATING FUNCTIONS AND PROBABILITY DISTRIBUTIONS

159

In particular, the first two moments satisfy EAn (χ )

[z n ]∂u A(z, u)|u=1 [z n ]A(z, 1) [z n ]∂ 2 A(z, u)

=

[z n ]∂u A(z, u)|u=1 , [z n ]A(z, 1) the variance and standard deviation being determined by EAn (χ 2 )

u u=1 [z n ]A(z, 1)

=

+

V(χ ) = σ (χ )2 = E(χ 2 ) − E(χ )2 . Proof. The PGF pn (u) of χ over An is given by Proposition III.1. On the other hand, factorial moments are on general grounds obtained by differentiation and evaluation at u = 1. The result follows. In other words, the quantities

n k (k) := ω · [z ] ∂ A(z, u) n n u

u=1

give, after a simple normalization (by ωn · [z n ]A(z, 1)), the factorial moments: 1 (k) E (χ (χ − 1) · · · (χ − k + 1)) = . An n Most notably, (1) n is the cumulated value of χ over all objects of An : X (1) n ≡ ωn · [z n ] ∂u A(z, u)|u=1 = χ (α) ≡ An · EAn (χ ). α∈An

(1)

Accordingly, the GF (ordinary or exponential) of the n is sometimes named the cumulative generating function. It can be viewed as an unnormalized generating function of the sequence of expected values. These considerations explain Wilf’s suggestive motto quoted on p. 151: “Generating functions find averages, etc”. (The “etc” can be interpreted as a token for higher moments and probability distributions.)

III.2. A combinatorial form of cumulative GFs. One has (1) (z) ≡

X

EAn (χ )An

n

X z |α| zn = , χ (α) ωn ω|α| α∈A

where ωn = 1 (ordinary case) or ωn = n! (exponential case).

Example III.3. Moments of the binomial distribution. The binomial distribution of index n can be defined as the distribution of the number of as in a random word of length n over the binary alphabet {a, b}. The determination of moments results easily from the ordinary BGF, W (z, u) = By differentiation, one finds

1 . 1 − z − zu

r !zr ∂r = W (z, u) . r ∂u (1 − 2z)r +1 u=1

Coefficient extraction then gives the form of the factorial moments of orders 1, 2, 3, . . . , r as n(n − 1) n(n − 1)(n − 2) r! n n . , , ,..., 2 4 8 2r r

160

III. PARAMETERS AND MULTIVARIATE GFS

√ In particular, the mean and the variance are 12 n and 14 n. The standard deviation is thus 21 n which is of a smaller order than the mean: this indicates that the distribution is somehow concentrated around its mean value, as suggested by Figure III.4. . . . . . . . . . . . . . . . . . . . . . . . . . . .

III.3. De Moivre’s approximation of the binomial coefficients. The fact that the mean and √ the standard deviation of the binomial distribution are respectively 12 n and 21 n suggests we examine what goes on at a distance of x standard deviations from the mean. Consider for simplicity the case of n = 2ν even. From the ratio 2ν (1 − ν1 )(1 − ν2 ) · · · (1 − k−1 ν+ℓ ν ), r (ν, ℓ) := 2ν = 1 2 (1 + ν )(1 + ν ) · · · (1 + νk ) ν the approximation log(1 + x) = x + O(x 2 ) shows that, for any fixed y ∈ R, 2ν lim √ n→∞, ℓ=ν+y ν/2

2 ν+ℓ = e−y /2 . 2ν ν

(Alternatively, Stirling’s formula can be employed.) This Gaussian approximation for the binomial distribution was discovered by Abraham de Moivre (1667–1754), a close friend of Newton. General methods for establishing such approximations are developed in Chapter IX. Example III.4. Moments of the Stirling cycle distribution. Let us return to the example of cycles in permutations which is of interest in connection with certain sorting algorithms like bubble sort or insertion sort, maximum finding, and in situ rearrangement [374]. We are dealing with labelled objects, hence exponential generating functions. As seen earlier on p. 155, the BGF of permutations counted according to cycles is P(z, u) = (1 − z)−u . By differentiating the BGF with respect to u, then setting u = 1, we next get the expected number of cycles in a random permutation of size n as a Taylor coefficient: 1 1 1 1 log = 1 + + ··· + , 1−z 1−z 2 n which is the harmonic number Hn . Thus, on average, a random permutation of size n has about log n + γ cycles, a well-known fact of discrete probability theory, derived on p. 122 by means of horizontal generating functions. For the variance, a further differentiation of the bivariate EGF gives 2 X 1 1 n log . (12) En (χ (χ − 1))z = 1−z 1−z

(11)

En (χ ) = [z n ]

n≥0

From this expression and Note III.4 (or directly from the Stirling cycle polynomials of p. 155), a calculation shows that n n X X 1 π2 1 1 2 (13) σn = = log n + γ − − . +O k 6 n k2 k=1

k=1

Thus, asymptotically,

σn ∼

p log n.

The standard deviation is of an order smaller than the mean, and therefore large deviations from the mean have an asymptotically negligible probability of occurrence (see below the discussion of moment inequalities). Furthermore, the distribution is asymptotically Gaussian, as we shall see in Chapter IX, p. 644. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

III. 2. BIVARIATE GENERATING FUNCTIONS AND PROBABILITY DISTRIBUTIONS

161

III.4. Stirling cycle numbers and harmonic numbers. By the “exp–log trick” of Chapter I, p. 29, the PGF of the Stirling cycle distribution satisfies ! 1 v 2 (2) v 3 (3) u =1+v u(u + 1) · · · (u + n − 1) = exp v Hn − Hn + Hn + · · · , n! 2 3 P (r ) where Hn is the generalized harmonic number nj=1 j −r . Consequently, any moment of the distribution is a polynomial in generalized harmonic numbers; compare (11) and (13). Furthermore, the kth moment satisfies EPn (χ k ) ∼ (log n)k . (The same technique expresses the (r ) Stirling cycle number nk as a polynomial in generalized harmonic numbers Hn−1 .) Alternatively, start from the expansion of (1 − z)−α and differentiate repeatedly with respect to α; for instance, one has X1 1 1 n+α−1 n 1 z , = + + ··· + (1 − z)−α log 1−z α α+1 n−1+α n n≥0

which provides (11) upon setting α = 1, while the next differentiation gives (13).

The situation encountered with cycles in permutations is typical of iterative (nonrecursive) structures. In many other cases, especially when dealing with recursive structures, the bivariate GF may satisfy complicated functional equations in two variables (see the example of path length in trees, Section III. 5 below), which means we do not know them explicitly. However, asymptotic laws can be determined in a large number of cases (Chapter IX). In all cases, the BGFs are the central tool in obtaining mean and variance estimates, since their derivatives evaluated at u = 1 become univariate GFs that usually satisfy much simpler relations than the BGFs themselves. III. 2.2. Moment inequalities and concentration of distributions. Qualitatively speaking, families of distributions can be classified into two categories: (i) distributions that are spread, i.e., the standard deviation is of order at least as large as the mean (e.g.the uniform distributions over [0 . . n], which have totally flat histograms); (ii) distributions for which the standard deviation is of an asymptotic order smaller than the mean (e.g., the Stirling cycle distribution, Figure III.4, and the binomial distribution, Figure III.5.) Such informal observations are indeed supported by the Markov– Chebyshev inequalities, which take advantage of information provided by the first two moments. (A proof is found in Appendix A.3: Combinatorial probability, p. 727.) Markov–Chebyshev inequalities. Let X be a non-negative random variable and Y an arbitrary real variable. One has for any t > 0: P {X ≥ tE(X )}

≤

P {|Y − E(Y )| ≥ tσ (Y )}

≤

1 t 1 t2

(Markov inequality) (Chebyshev inequality).

This result informs us that the probability of being much larger than the mean must decay (Markov) and that an upper bound on the decay is measured in units given by the standard deviation (Chebyshev). The next proposition formalizes a concentration property of distributions. It applies to a family of distributions indexed by the integers.

162

III. PARAMETERS AND MULTIVARIATE GFS

0.3 0.25 0.2 0.15 0.1 0.05 0

0.2

0.4

0.6

0.8

1

Figure III.5. Plots of the binomial distributions for n = 5, . . . , 50. The horizontal axis (by a factor of 1/n) and rescaled to 1, so that the curves display n is normalized o P( Xnn = x) , for x = 0, n1 , n2 , . . . .

Proposition III.3 (Concentration of distribution). Consider a family of random variables X n , typically, a scalar parameter χ on the subclass An . Assume that the means µn = E(X n ) and the standard deviations σn = σ (X n ) satisfy the condition lim

n→+∞

σn = 0. µn

Then the distribution of X n is concentrated in the sense that, for any ǫ > 0, there holds Xn ≤ 1 + ǫ = 1. (14) lim P 1 − ǫ ≤ n→+∞ µn Proof. The result is a direct consequence of Chebyshev’s inequality.

The concentration property (14) expresses the fact that values of X n tend to become closer and closer (in relative terms) to the mean µn as n increases. Another figurative way of describing concentration, much used in random combinatorics, is to say that “X n /µn tends to 1 in probability”; in symbols: Xn P −→ 1. µn When this property is satisfied, the expected value is in a strong sense a typical value— this fact is an extension of the weak law of large numbers of probability theory. Concentration properties of the binomial and Stirling cycle distributions. The binomial distribution is concentrated, since the mean of the distribution is n/2 and √ the standard deviation is n/4, a much smaller quantity. Figure III.5 illustrates concentration by displaying the graphs (as polygonal lines) associated to the binomial distributions for n = 5, . . . , 50. Concentration is also quite perceptible on simulations as n gets large: the table below describes the results of batches of ten (sorted)

III. 3. INHERITED PARAMETERS AND ORDINARY MGFS

simulations from the binomial distribution n n n n

= 100 = 1000 = 10 000 = 100 000

n

on n

1 2n k

k=0

163

:

39, 42, 43, 49, 50, 52, 54, 55, 55, 57 487, 492, 494, 494, 506, 508, 512, 516, 527, 545 4972, 4988, 5000, 5004, 5012, 5017, 5023, 5025, 5034, 5065 49798, 49873, 49968, 49980, 49999, 50017, 50029, 50080, 50101, 50284;

the maximal deviations from the mean observed on such samples are 22% (n = 102 ), 9% (n = 103 ), 1.3% (n = 104 ), and 0.6% (n = 105 ). Similarly, the mean and variance computations of (11) and (13) imply that the number of cycles in a random permutation of large size is concentrated. Finer estimates on distributions form the subject of our Chapter IX dedicated to limit laws. The reader may get a feeling of some of the phenomena at stake when examining Figure III.5 and Note III.3, p. 160: the visible emergence of a continuous curve (the bell-shaped curve) corresponds to a common asymptotic shape for the whole family of distributions—the Gaussian law. III. 3. Inherited parameters and ordinary MGFs In this section and the next, we address the question of determining BGFs directly from combinatorial specifications. The answer is provided by a simple extension of the symbolic method, which is formulated in terms of multivariate generating functions (MGFs). Such generating functions have the capability of taking into account a finite collection (equivalently, a vector) of combinatorial parameters. Bivariate generating functions discussed earlier appear as a special case. III. 3.1. Multivariate generating functions (MGFs). The theory is best developed in full generality for the joint analysis of a fixed finite collection of parameters. Definition III.3. Consider a combinatorial class A. A (multidimensional) parameter χ = (χ1 , . . . , χd ) on the class is a function from A to the set Zd≥0 of d–tuples of natural numbers. The counting sequence of A with respect to size and the parameter χ is then defined by An,k ,...,k = card α |α| = n, χ1 (α) = k1 , . . . , χd (α) = kd . 1

d

We sometimes refer to such a parameter as a “multiparameter” when d > 1, and a “simple” or “scalar” parameter otherwise. For instance, one may take the class P of all permutations σ , and for χ j ( j = 1, 2, 3) the number of cycles of length j in σ . Alternatively, we may consider the class W of all words w over an alphabet with four letters, {α1 , . . . , α4 } and take for χ j ( j = 1, . . . , 4) the number of occurrences of the letter α j in w, and so on. The multi-index convention employed in various branches of mathematics greatly simplifies notations: let x = (x1 , . . . , xd ) be a vector of d formal variables and k = (k1 , . . . , kd ) be a vector of integers of the same dimension; then, the multipower xk is defined as the monomial (15) With this notation, we have:

xk := x1k1 x2k2 · · · xdkd .

164

III. PARAMETERS AND MULTIVARIATE GFS

Definition III.4. Let An,k be a multi-index sequence of numbers, where k ∈ Nd . The multivariate generating function (MGF) of the sequence of either ordinary or exponential type is defined as the formal power series X A(z, u) = An,k uk z n (ordinary MGF) n,k

(16)

A(z, u)

=

X

An,k uk

n,k

zn n!

(exponential MGF).

Given a class A and a parameter χ , the MGF of the pair hA, χ i is the MGF of the corresponding counting sequence. In particular, one has the combinatorial forms: X A(z, u) = uχ (α) z |α| (ordinary MGF; unlabelled case) α∈A

(17)

A(z, u)

=

X

α∈A

uχ (α)

z |α| |α|!

(exponential MGF; labelled case).

One also says that A(z, u) is the MGF of the combinatorial class with the formal variable u j marking the parameter χ j and z marking size. From the very definition, with 1 a vector of all 1’s, the quantity A(z, 1) coincides with the generating function of A, either ordinary or exponential as the case may be. One can then view an MGF as a deformation of a univariate GF by way of a vector u, with the property that the multivariate GF reduces to the univariate GF at u = 1. If all but one of the u j are set to 1, then a BGF results; in this way, the symbolic calculus that we are going to develop gives full access to BGFs (and, from here, to moments).

III.5. Special cases of MGFs. The exponential MGF of permutations with u 1 , u 2 marking

the number of 1–cycles and 2–cycles respectively is 2 exp (u 1 − 1)z + (u 2 − 1) z2 (18) P(z, u 1 , u 2 ) = . 1−z (This will be proved later in this chapter, p. 187.) The formula is checked to be consistent with three already known special cases derived in Chapter II: (i) setting u 1 = u 2 = 1 gives back the counting of all permutations, P(z, 1, 1) = (1 − z)−1 , as it should; (ii) setting u 1 = 0 and u 2 = 1 gives back the EGF of derangements, namely e−z /(1 − z); (iii) setting u 1 = u 2 = 0 gives back the EGF of permutations with cycles all of length greater than 2, P(z, 0, 0) = 2 e−z−z /2 /(1 − z), a generalized derangement GF. In addition, the particular BGF

e(u−1)z , 1−z enumerates permutations according to singleton cycles. This last BGF interpolates between the EGF of derangements (u = 0) and the EGF of all permutations (u = 1). P(z, u, 1) =

III. 3.2. Inheritance and MGFs. Parameters that are inherited from substructures (definition below) can be taken into account by a direct extension of the symbolic method. With a suitable use of the multi-index conventions, it is even the case that the translation rules previously established in Chapters I and II can be copied verbatim. This approach provides a large quantity of multivariate enumeration results that follow automatically by the symbolic method.

III. 3. INHERITED PARAMETERS AND ORDINARY MGFS

165

Definition III.5. Let hA, χ i, hB, ξ i, hC, ζ i be three combinatorial classes endowed with parameters of the same dimension d. The parameter χ is said to be inherited in the following cases. • Disjoint union: when A = B + C, the parameter χ is inherited from ξ, ζ iff its value is determined by cases from ξ, ζ : ξ(ω) if ω ∈ B χ (ω) = ζ (ω) if ω ∈ C. • Cartesian product: when A = B × C, the parameter χ is inherited from ξ, ζ iff its value is obtained additively from the values of ξ, ζ : χ (β, γ ) = ξ(β) + ζ (γ ). • Composite constructions: when A = K{B}, where K is a metasymbol representing any of S EQ, MS ET, PS ET, C YC, the parameter χ is inherited from ξ iff its value is obtained additively from the values of ξ on components; for instance, for sequences: χ (β1 , . . . , βr ) = ξ(β1 ) + · · · + ξ(βr ). With a natural extension of the notation used for constructions, we shall write hA, χ i = hB, ξ i + hC, ζ i,

hA, χ i = hB, ξ i × hC, ζ i,

hA, χ i = K {hB, ξ i} .

This definition of inheritance is seen to be a natural extension of the axioms that size itself has to satisfy (Chapter I): size of a disjoint union is defined by cases; size of a pair, and similarly of a composite construction, is obtained by addition. Next, we need a bit of formality. Consider a pair hA, χ i, where A is a combinatorial class endowed with its usual size function | · | and χ = (χ1 , . . . , χd ) is a d-dimensional (multi)parameter. Write χ0 for size and z 0 for the variable marking size (previously denoted by z). The key point is to define an extended multiparameter χ = (χ0 , χ1 , . . . , χd ); that is, we treat size and parameters on an equal opportunity basis. Then the ordinary MGF in (16) assumes an extremely simple and symmetrical form: X X (19) A(z) = Ak zk = zχ (α) . α∈A

k

Here, the indeterminates are the vector z = (z 0 , z 1 , . . . , z d ), the indices are k = (k0 , k1 , . . . , kd ), where k0 indexes size (previously denoted by n) and the usual multiindex convention introduced in (15) is in force: (20)

k

zk := z 00 z 1k1 · · · z d kd ,

but it is now applied to (d + 1)-dimensional vectors. With this convention, we have: Theorem III.1 (Inherited parameters and ordinary MGFs). Let A be a combinatorial class constructed from B, C, and let χ be a parameter inherited from ξ defined on B and (as the case may be) from ζ on C. Then the translation rules of admissible constructions stated in Theorem I.1, p. 27, are applicable, provided the multi-index

166

III. PARAMETERS AND MULTIVARIATE GFS

convention (19) is used. The associated operators on ordinary MGFs are then (ϕ(k) is the Euler totient function, defined on p. 721): Union:

A=B+C

H⇒

A(z) = B(z) + C(z),

Product:

A=B×C

H⇒

Sequence:

A = S EQ(B)

H⇒

Powerset:

A = PS ET(B)

H⇒

Multiset:

A = MS ET(B) H⇒

Cycle:

A = C YC(B)

A(z) = B(z) · C(z), 1 A(z) = , 1 − B(z) X ∞ (−1)ℓ−1 B(zℓ ) . A(z) = exp ℓ ℓ=1 X ∞ 1 A(z) = exp B(zℓ ) , ℓ ℓ=1 ∞ X ϕ(ℓ) 1 A(z) = log , ℓ 1 − B(zℓ )

H⇒

ℓ=1

Proof. For disjoint unions, one has X X X A(z) = zχ (α) = zξ (β) + zζ (γ ) , α∈A

β∈B

γ ∈C

since inheritance is defined by cases on unions. For cartesian products, one has X X X zξ (β) × zζ (γ ) , A(z) = zχ (α) = α∈A

β∈B

γ ∈C

since inheritance corresponds to additivity on products. The translation of composite constructions in the case of sequences, powersets, and multisets is then built up from the union and product schemes, in exactly the same manner as in the proof of Theorem I.1. Cycles are dealt with by the methods of Appendix A.4: Cycle construction, p. 729. The multi-index notation is a crucial ingredient for developing the general theory of multivariate enumerations. When we work with only a small number of parameters, typically one or two, we will however often find it convenient to return to vectors of variables like (z, u) or (z, u, v). In this way, unnecessary subscripts are avoided. The reader is especially encouraged to study the treatment of integer compositions in Examples III.5 and III.6 below carefully, since it illustrates the power of the multivariate symbolic method, in its bare bones version. Example III.5. Integer compositions and MGFs I. The class C of all integer compositions (Chapter I) is specified by C = S EQ(I),

I = S EQ≥1 (Z),

where I is the set of all positive numbers. The corresponding OGFS are 1 z C(z) = , I (z) = , 1 − I (z) 1−z

so that Cn = 2n−1 (n ≥ 1). Say we want to enumerate compositions according to the number χ of summands. One way to proceed, in accordance with the formal definition of inheritance, is

III. 3. INHERITED PARAMETERS AND ORDINARY MGFS

167

as follows. Let ξ be the parameter that takes the constant value 1 on all elements of I. The parameter χ on compositions is inherited from the (almost trivial) parameter ξ ≡ 1 defined on summands. The ordinary MGF of hI, ξ i is I (z, u) = zu + z 2 u + z 3 u + · · · =

zu . 1−z

Let C(z, u) be the BGF of hC, χ i. By Theorem III.1, the schemes translating admissible constructions in the univariate case carry over to the multivariate case, so that (21)

C(z, u) =

1−z 1 1 = z = 1 − z(u + 1) . 1 − I (z, u) 1 − u 1−z

Et voil`a! . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Markers. There is an alternative way of arriving at MGFs, as in (21), which is important and will be of much use thoughout this book. A marker (or mark) in a specification 6 is a neutral object (i.e., an object of size 0) attached to a construction or an atom by a product. Such a marker does not modify size, so that the univariate counting sequence associated to 6 remains unaffected. On the other hand, the total number of markers that an object contains determines by design an inherited parameter, so that Theorem III.1 is automatically applicable. In this way, one may decorate specifications so as to keep track of “interesting” substructures and get BGFs automatically. The insertion of several markers similarly gives MGFs. For instance, say we are interested in the number of summands in compositions, as in Example III.5 above. Then, one has an enriched specification, and its translation into MGF, (22)

C = S EQ µ S EQ≥1 (Z)

H⇒

C(z, u) =

1 , 1 − u I (z)

based on the correspondence: Z 7→ z, µ 7→ u. Example III.6. Integer compositions and MGFs II. Consider the double parameter χ = (χ1 , χ2 ) where χ1 is the number of parts equal to 1 and χ2 the number of parts equal to 2. One can write down an extended specification, with µ1 a combinatorial mark for summands equal to 1 and µ2 for summands equal to 2, 2 C = S EQ µ1 Z + µ2 Z + S EQ≥3 (Z) (23) 1 , H⇒ C(z, u 1 , u 2 ) = 2 1 − (u 1 z + u 2 z + z 3 (1 − z)−1 ) where u j ( j = 1, 2) records the number of marks of type µ j . Similarly, let µ mark each summand and µ1 mark summands equal to 1. Then, one has, 1 (24) C = S EQ µµ1 Z + µ S EQ≥2 (Z) H⇒ C(z, u 1 , u) = , 1 − (uu 1 z + uz 2 (1 − z)−1 ) where u keeps track of the total number of summands and u 1 records the number of summands equal to 1.

168

III. PARAMETERS AND MULTIVARIATE GFS

MGFs obtained in this way via the multivariate extension of the symbolic method can then provide explicit counts, after suitable series expansions. For instance, the number of compositions of n with k parts is, by (21), 1−z n n−1 n−1 [z n u k ] = − = , 1 − (1 + u)z k k k−1 a result otherwise obtained in Chapter I by direct combinatorial reasoning (the balls-and-bars model). The number of compositions of n containing k parts equal to 1 is obtained from the special case u 2 = 1 in (23), 1

[z n u k ]

2

= [z n−k ]

(1 − z)k+1 , (1 − z − z 2 )k+1

z 1 − uz − (1−z) where the last OGF closely resembles a power of the OGF of Fibonacci numbers. Following the discussion of Section III. 2, such MGFs also carry complete information about moments. In particular, the cumulated value of the number of parts in all compositions of n has OGF z(1 − z) ∂u C(z, u)|u=1 = , (1 − 2z)2 since cumulated values are obtained via differentiation of a BGF. Therefore, the expected number of parts in a random composition of n is exactly (for n ≥ 1) 1 z(1 − z) 1 [z n ] = (n + 1). n−1 2 2 2 (1 − 2z) One further differentiation will give rise to the variance. The standard deviation is found to √ be 21 n − 1, which is of an order (much) smaller than the mean. Thus, the distribution of the number of summands in a random composition satisfies the concentration property as n → ∞. In the same vein, the number of parts equal to a fixed number r in compositions is determined by −1 z r r + (u − 1)z . C = S EQ µZ + S EQ6=r (Z) H⇒ C(z, u) = 1 − 1−z It is then easy to pull out the expected number of r -summands in a random composition of size n. The differentiated form

∂u C(z, u)|u=1 = gives, by partial fraction expansion,

zr (1 − z)2 (1 − 2z)2

2−r −1 − r 2−r −2 2−r −2 + + q(z), 2 1 − 2z (1 − 2z) for a polynomial q(z) that we do not need to make explicit. Extracting the nth coefficient of the cumulative GF ∂u C(z, 1) and dividing by 2n−1 yields the mean number of r –parts in a random composition. Another differentiation gives access to the second moment. One obtains the following proposition. Proposition III.4 (Summands in integer compositions). The total number of summands in a random composition of size n has mean 12 (n + 1) and a distribution that is concentrated around the mean. The number of r summands in a composition of size n has mean n + O(1); r 2 +1 √ and a standard deviation of order n, which also ensures concentration of distribution. ∂u C(z, u)|u=1 =

III. 3. INHERITED PARAMETERS AND ORDINARY MGFS

10 8 6 4 2 0

10

20

30

40

10 8 6 4 2 0

10

20

30

40

169

Figure III.6. A random composition of n = 100 represented as a ragged landscape (top); its associated profile 120 212 310 41 51 71 101 , defined as the partition obtained by sorting the summands (bottom).

Results of a simulation illustrating the proposition are displayed in Figure III.6 to which Note III.6 below adds further comments. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

III.6. The profile of integer compositions. From the point of view of random structures, Proposition III.4 shows that random compositions of large size tend to conform to a global “profile”. With high probability, a composition of size n should have about n/4 parts equal to 1, n/8 parts equal to 2, and so on. Naturally, there are statistically unavoidable fluctuations, and for any finite n, the regularity of this law cannot be perfect: it tends to fade away, especially with regard to largest summands that are log2 (n) + O(1) with high probability. (In this region mean and standard deviation both become of the same order and are O(1), so that concentration no longer holds.) However, such observations do tell us a great deal about what a typical random composition must (probably) look like—it should conform to a “geometric profile”, 1n/4 2n/8 3n/16 4n/32 · · · .

Here are for instance the profiles of two compositions of size n = 1024 drawn uniformly at random: 1250 2138 370 429 515 610 74 80 , 91

and

1253 2136 368 431 513 68 73 81 91 102 .

These are to be compared with the “ideal” profile 1256 2128 364 432 516 68 74 82 91 . It is a striking fact that samples of a very few elements or even just one element (this would be ridiculous by the usual standards of statistics) are often sufficient to illustrate asymptotic properties of large random structures. The reason is once more to be attributed to concentration of distributions whose effect is manifest here. Profiles of a similar nature present themselves among objects defined by the sequence construction, as we shall see throughout this book. (Establishing such general laws is usually not difficult but it requires the full power of complex analytic methods developed in Chapters IV–VIII.)

III.7. Largest summands in compositions. For any ǫ > 0, with probability tending to 1 as n → ∞, the largest summand in a random integer composition of size n is in the interval [(1 − ǫ) log2 n, (1 + ǫ) log2 n]. (Hint: use the first and second moment methods. More precise estimates are obtained by the methods of Example V.4, p. 308.)

170

III. PARAMETERS AND MULTIVARIATE GFS

K S EQ :

PS ET :

MS ET :

BGF (A(z, u))

cumulative GF ((z))

1 1 − u B(z) ∞ k X u (−1)k−1 B(z k ) exp k

A(z)2 · B(z) =

∞ Y

k=1

(1 + uz n ) Bn n=1 ∞ k X u exp B(z k ) k k=1

∞ Y (1 − uz n )−Bn n=1

C YC :

∞ X 1 ϕ(k) log k 1 − u k B(z k )

k=1

A(z) ·

A(z) · ∞ X

k=1

B(z) (1 − B(z))2

∞ X

(−1)k−1 B(z k )

∞ X

B(z k )

k=1

k=1

ϕ(k)

B(z k ) . 1 − B(z k )

Figure III.7. Ordinary GFs relative to the number of components in A = K(B).

Simplified notation for markers. It proves highly convenient to simplify notations, much in the spirit of our current practice, where the atom Z is reflected by the name of the variable z in GFs. The following convention will be systematically adopted: the same symbol (usually u, v, u 1 , u 2 . . .) is freely employed to designate a combinatorial marker (of size 0) and the corresponding marking variable in MGFs. For instance, we can write directly, for compositions, C = S EQ(u S EQ≥1 Z)),

C = S EQ(uu 1 Z + u S EQ≥2 Z)),

where u marks all summands and u 1 marks summands equal to 1, giving rise to (22) and (24) above. The symbolic scheme of Theorem III.1 invariably applies to enumeration according to the number of markers. III. 3.3. Number of components in abstract unlabelled schemas. Consider a construction A = K(B), where the metasymbol K designates any standard unlabelled constructor among S EQ, MS ET, PS ET, C YC. What is sought is the BGF A(z, u) of class A, with u marking each component. The specification is then of the form A = K(uB),

K = S EQ, MS ET, PS ET, C YC .

Theorem III.1 applies and yields immediately the BGF A(z, u). In addition, differentiating with respect to u then setting u = 1 provides the GF of cumulated values (hence, in a non-normalized form, the OGF of the sequence of mean values of the number of components): ∂ A(z, u) (z) = . ∂u u=1

III. 3. INHERITED PARAMETERS AND ORDINARY MGFS

171

20

15

10

5

0

2

4

6

8 10

Figure III.8. A random partition of size n = 100 has an aspect rather different from the profile of a random composition of the same size (Figure III.6).

In summary: Proposition III.5 (Components in unlabelled schemas). Given a construction, A = K(B), the BGF A(z, u) and the cumulated GF (z) associated to the number of components are given by the table of Figure III.7. Mean values are then recovered with the usual formula, EAn (# components) =

[z n ](z) . [z n ]A(z)

III.8. r –Components in abstract unlabelled schemas. Consider unlabelled structures. The BGF of the number of r –components in A = K{B} is given by −1 1 − zr Br A(z, u) = 1 − B(z) − (u − 1)Br zr , A(z, u) = A(z) · , 1 − uzr

in the case of sequences (K = S EQ) and multisets (K = MS ET), respectively. Similar formulae hold for the other basic constructions and for cumulative GFs.

III.9. Number of distinct components in a multiset. The specification and the BGF are Y

β∈B

1 + u S EQ≥1 (β)

H⇒

Bn Y uz n , 1+ 1 − zn

n≥1

as follows from first principles.

As an illustration of Proposition III.5, we discuss the profile of random partitions (Figure III.8). Example III.7. The profile of partitions. Let P = MS ET(I) be the class of all integer partitions, where I = S EQ≥1 (Z) represents integers in unary notation. The BGF of P with u marking the number χ of parts (or summands) is obtained from the specification ∞ k k X z u . P = MS ET(uI) H⇒ P(z, u) = exp k 1 − zk k=1

172

III. PARAMETERS AND MULTIVARIATE GFS

100 80 60 40 20 0

100

200

300

400

500

Figure III.9. The number of parts in random partitions of size 1, . . . , 500: exact values of the mean and simulations (circles, one for each value of n).

Equivalently, from first principles, P∼ =

∞ Y

S EQ (uIn )

n=1

H⇒

∞ Y

n=1

1 . 1 − uz n

The OGF of cumulated values then results from the second form of the BGF by logarithmic differentiation: ∞ X zk (25) (z) = P(z) · . 1 − zk k=1

Now, the factor on the right in (25) can be expanded as ∞ X

k=1

∞ X zk = d(n)z n , k 1−z n=1

with d(n) the number of divisors of n. Thus, the mean value of χ is (26)

En (χ ) =

n 1 X d( j)Pn− j . Pn j=1

The same technique applies to the number of parts equal to r . The form of the BGF is r Y e∼ e u) = 1 − z · P(z), P S EQ(In ) H⇒ P(z, = S EQ(uIr ) × 1 − uzr n6=r

which implies that the mean value of the number χ e of r –parts satisfies 1 1 n zr Pn−r + Pn−2r + Pn−3r + · · · . = En (e χ) = [z ] P(z) · Pn 1 − zr Pn From these formulae and a decent symbolic manipulation package, the means are calculated easily up to values of n well into the range of several thousand. . . . . . . . . . . . . . . . . . . . . . . . . .

The comparison between Figures III.6 and III.8 shows that different combinatorial models may well lead to rather different types of probabilistic behaviours. Figure III.9 displays the exact value of the mean number of parts in random partitions of size n = 1, . . . , 500, (as calculated from (26)) accompanied with the observed values of one

III. 3. INHERITED PARAMETERS AND ORDINARY MGFS

173

60

70 60

50

50

40

40

30

30 20 20 10

10 0

10

20

30

40

50

60

0

20

40

60

80

Figure III.10. Two partitions of P1000 drawn at random, compared to the limiting shape 9(x) defined by (27).

random sample for each value of n in the range. The mean number of parts is known to be asymptotic to √ n log n , √ π 2/3 √ and the distribution, though it admits a comparatively large standard deviation O( n), is still concentrated, in the technical sense of the term. We shall prove some of these assertions in Chapter VIII, p. 581. In recent years, Vershik and his collaborators [152, 595] have shown that most√integer partitions tend to conform to a definite profile given (after normalization by n) by the continuous plane curve y = 9(x) defined implicitly by π (27) y = 9(x) iff e−αx + e−αy = 1, α=√ . 6 This is illustrated in Figure III.10 by two randomly drawn elements of P1000 represented together with the “most likely” limit shape. The theoretical result explains the huge differences that are manifest on simulations between integer compositions and integer partitions. The last example of this section demonstrates the application of BGFs to estimates regarding the root degree of a tree drawn uniformly at random among the class Gn of general Catalan trees of size n. Tree parameters such as number of leaves and path length that are more global in nature and need a recursive definition will be discussed in Section III. 5 below. Example III.8. Root degree in general Catalan trees. Consider the parameter χ equal to the degree of the root in a tree, and take the class G of all plane unlabelled trees, i.e., general Catalan trees. The specification is obtained by first defining trees (G), then defining trees with a mark for subtrees (G ◦ ) dangling from the root: z G = Z × S EQ(G) G(z) = 1 − G(z) H⇒ z G ◦ = Z × S EQ(uG) G(z, u) = . 1 − uG(z)

174

III. PARAMETERS AND MULTIVARIATE GFS

This set of equations reveals that the probability that the root degree equals r is 1 n−1 r r 2n − 3 − r Pn {χ = r } = ∼ r +1 , [z ]G(z)r = Gn n−1 n−2 2 this by Lagrange inversion and elementary asymptotics. Furthermore, the cumulative GF is found to be zG(z) (z) = . (1 − G(z))2 The relation satisfied by G entails a further simplification, 1 1 − 1 G(z) − 1, (z) = G(z)3 = z z so that the mean root degree admits a closed form, n−1 1 G n+1 − G n = 3 , En (χ ) = Gn n+1 a quantity clearly asymptotic to 3. A random plane tree is thus usually composed of a small number of root subtrees, at least one of which should accordingly be fairly large. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

III. 4. Inherited parameters and exponential MGFs The theory of inheritance developed in the last section applies almost verbatim to labelled objects. The only difference is that the variable marking size must carry a factorial coefficient dictated by the needs of relabellings. Once more, with a suitable use of multi-index conventions, the translation mechanisms developed in the univariate case (Chapter II) remain in force, this in a way that parallels the unlabelled case. Let us consider a pair hA, χ i, where A is a labelled combinatorial class endowed with its size function | · | and χ = (χ1 , . . . , χd ) is a d-dimensional parameter. As before, the parameter χ is extended into χ by inserting size as zeroth coordinate and a vector z = (z 0 , . . . , z d ) of d + 1 indeterminates is introduced, with z 0 marking size and z j marking χ j . Once the multi-index convention of (20) defining zk has been brought into play, the exponential MGF of hA, χ i (see Definition III.4, p. 164) can be rephrased as X zχ (α) X zk = . Ak (28) A(z) = k0 ! |α|! k

α∈A

This MGF is exponential in z (alias z 0 ) but ordinary in the other variables; only the factorial k0 ! is needed to take into account relabelling induced by labelled products. We a priori restrict attention to parameters that do not depend on the absolute values of labels (but may well depend on the relative order of labels): a parameter is said to be compatible if, for any α, it assumes the same value on any labelled object α and all the order-consistent relabellings of α. A parameter is said to be inherited if it is compatible and it is defined by cases on disjoint unions and determined additively on labelled products—this is Definition III.5 (p. 165) with labelled products replacing cartesian products. In particular, for a compatible parameter, inheritance signifies additivity on components of labelled sequences, sets, and cycles. We can then cutand-paste (with minor adjustments) the statement of Theorem III.1, p. 165:

III. 4. INHERITED PARAMETERS AND EXPONENTIAL MGFS

175

Theorem III.2 (Inherited parameters and exponential MGFs). Let A be a labelled combinatorial class constructed from B, C, and let χ be a parameter inherited from ξ defined on B and (as the case may be) from ζ on C. Then the translation rules of admissible constructions stated in Theorem II.1, p. 103, are applicable, provided the multi-index convention (28) is used. The associated operators on exponential MGFs are then: Union: A=B+C H⇒ A(z) = B(z) + C(z) Product: A=B⋆C H⇒ A(z) = B(z) · C(z) 1 Sequence: A = S EQ(B) H⇒ A(z) = 1 − B(z) 1 Cycle: A = C YC(B) H⇒ A(z) = log . 1 − B(z) Set: A = S ET(B) H⇒ A(z) = exp B(z) .

Proof. Disjoint unions are treated in a similar manner to the unlabelled multivariate case. Labelled products result from X |β| + |γ | zξ (β) zζ (γ ) X zχ (α) = , A(z) = |β|, |γ | (|β| + |γ |)! |α|! α∈A

β∈B,γ ∈C

and the usual translation of binomial convolutions that reflect labellings by means of products of exponential generating functions (like in the univariate case detailed in Chapter II). The translation for composite constructions is then immediate. This theorem can be exploited to determine moments, in a way that entirely parallels its unlabelled counterpart. Example III.9. The profile of permutations. Let P be the class of all permutations and χ the number of components. Using the concept of marking, the specification and the exponential BGF are 1 = (1 − z)−u , P = S ET (u C YC(Z)) H⇒ P(z, u) = exp u log 1−z

as was already obtained by an ad hoc calculation in (5). We also know (p. 160) that the mean number of cycles is the harmonic number Hn and that the distribution is concentrated, since the standard deviation is much smaller than the mean. Regarding the number χ of cycles of length r , the specification and the exponential BGF are now P = S ET C YC6=r (Z) + u C YC=r (Z) r (29) e(u−1)z /r zr 1 = + (u − 1) . H⇒ P(z, u) = exp log 1−z r 1−z The EGF of cumulated values is then

zr 1 . r 1−z The result is a remarkably simple one: In a random permutation of size n, the mean number of r –cycles is equal to 1/r for any r ≤ n. Thus, the profile of a random permutation, where profile is defined as the ordered sequence of cycle lengths, departs significantly from what has been encountered for integer compositions (30)

(z) =

176

III. PARAMETERS AND MULTIVARIATE GFS

Figure III.11. The profile of permutations: a rendering of the cycle structure of six random permutations of size 500, where circle areas are drawn in proportion to cycle lengths. Permutations tend to have a few small cycles (of size O(1)), a few large ones (of size 2(n)), and altogether have Hn ∼ log n cycles on average. and partitions. Formula (30) also sheds a new light on the harmonic number formula for the mean number of cycles—each term 1/r in the harmonic number expresses the mean number of r –cycles. As formulae are so simple, one can extract more information. By (29) one has r

1 e−z /r , [z n−kr ] k 1−z k! r where the last factor counts permutations without cycles of length r . From this (and the asymptotics of generalized derangement numbers in Note IV.9, p. 261), one proves easily that the asymptotic law of the number of r –cycles is Poisson1 of rate 1/r ; in particular it is not concentrated. (This interesting property to be established in later chapters constitutes the starting point of an important study by Shepp and Lloyd [540].) Furthermore, the mean number of cycles whose size is between n/2 and n is Hn − H⌊n/2⌋ , a quantity that equals the probability of existence of such a long cycle and is approximately . log 2 = 0.69314. In other words, we expect a random permutation of size n to have one or a few large cycles. (See the article of Shepp and Lloyd [540] for the original discussion of largest and smallest cycles.) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . P{χ = k} =

III.10. A hundred prisoners II. This is the solution to the prisoners problem of Note II.15, p. 124 The better strategy goes as follows. Each prisoner will first open the drawer which corresponds to his number. If his number is not there, he’ll use the number he just found to access another drawer, then find a number there that points him to a third drawer, and so on, hoping to return to his original drawer in at most 50 trials. (The last opened drawer will then contain his number.) This strategy globally succeeds provided the initial permutation σ defined by σi (the number contained in drawer i) has all its cycles of length at most 50. The probability of the event is ! 100 X z z2 z 50 1 . 100 p = [z ] exp =1− + + ··· + = 0.31182 78206. 1 2 50 j j=51

1 The Poisson distribution of rate λ > 0 has the non-negative integers as support and is determined by

P{k} = e−λ

λk . k!

III. 4. INHERITED PARAMETERS AND EXPONENTIAL MGFS

177

Figure III.12. Two random allocations with m = 12, n = 48, corresponding to λ ≡ n/m = 4 (left). The right-most diagrams display the bins sorted by decreasing order of occupancy.

Do the prisoners stand a chance against a malicious director who would not place the numbers in drawers at random? For instance, the director might organize the numbers in a cyclic permutation. [Hint: randomize the problem by renumbering the drawers according to a randomly chosen permutation.] Example III.10. Allocations, balls-in-bins models, and the Poisson law. Random allocations and the balls-in-bins model were introduced in Chapter II in connection with the birthday paradox and the coupon collector problem. Under this model, there are n balls thrown into m bins in all possible ways, the total number of allocations being thus m n . By the labelled construction of words, the bivariate EGF with z marking the number of balls and u marking the number χ (s) of bins that contain s balls (s a fixed parameter) is given by zs m A = S EQm S ET6=s (Z) + u S ET=s (Z) H⇒ A(s) (z, u) = e z + (u − 1) . s! In particular, the distribution of the number of empty bins (χ (0) ) is expressible in terms of Stirling partition numbers: n! n (m − k)! m Pm,n (χ (0) = k) ≡ n [u k z n ]A(0) (z, u) = . k m−k m mn By differentiating the BGF, we get an exact expression for the mean (any s ≥ 0): 1 1 n−s n(n − 1) · · · (n − s + 1) 1 (31) 1− Em,n (χ (s) ) = . m s! m ms

Let m and n tend to infinity in such a way that n/m = λ is a fixed constant. This regime is extremely important in many applications, some of which are listed below. The average proportion of bins containing s elements is m1 Em,n (χ (s) ), and from (31), one obtains by straightforward calculations the asymptotic limit estimate, (32)

λs 1 Em,n (χ (s) ) = e−λ . s! n/m=λ, n→∞ m lim

(See Figure III.12 for two simulations corresponding to λ = 4.) In other words, a Poisson formula describes the average proportion of bins of a given size in a large random allocation. (Equivalently, the occupancy of a random bin in a random allocation satisfies a Poisson law in the limit.)

178

III. PARAMETERS AND MULTIVARIATE GFS

K

exponential BGF (A(z, u))

cumulative GF ((z))

S EQ :

1 1 − u B(z)

A(z)2 · B(z) =

S ET :

exp (u B(z))

A(z) · B(z) = B(z)e B(z)

C YC :

log

1 1 − u B(z)

B(z) (1 − B(z))2

B(z) . 1 − B(z)

Figure III.13. Exponential GFs relative to the number of components in A = K(B). The variance of each χ (s) (with fixed s) is estimated similarly via a second derivative and one finds: ! λs sλs−1 λs λs+1 (s) −2λ λ Vm,n (χ ) ∼ me . E(λ), E(λ) := e − − (1 − 2s) − s! (s − 1)! s! s! As a consequence, one has the convergence in probability, 1 (s) P −λ λs χ −→e , m s! valid for any fixed s ≥ 0. See Example VIII.14, p. 598 for an analysis of the most filled urn.

III.11. Hashing and random allocations. Random allocations of balls into bins are central in the understanding of a class of important algorithms of computer science known as hashing [378, 537, 538, 598]: given a universe U of data, set up a function (called a hashing function) h : U −→ [1 . . m] and arrange for an array of m bins; an element x ∈ U is placed in bin number h(x). If the hash function scrambles the data in a way that is suitably (pseudo)uniform, then the process of hashing a file of n records (keys, data items) into m bins is adequately modelled by a random allocation scheme. If λ = n/m, representing the “load”, is kept reasonably bounded (say, λ ≤ 10), the previous analysis implies that hashing allows for an almost direct access to data. (See also Example II.19, p. 146 for a strategy that folds colliding items into a table.)

Number of components in abstract labelled schemas. As in the unlabelled universe, a general formula gives the distribution of the number of components for the basic constructions. Proposition III.6. Consider labelled structures and the parameter χ equal to the number of components in a construction A = K{B}, where K is one of S EQ, S ET C YC. The exponential BGF A(z, u) and the exponential GF (z) of cumulated values are given by the table of Figure III.13. Mean values are then easily recovered, and one finds En (χ ) =

[z n ](z) n = n , An [z ]A(z)

by the same formula as in the unlabelled case.

III. 4. INHERITED PARAMETERS AND EXPONENTIAL MGFS

179

III.12. r –Components in abstract labelled schemas. The BGF A(z, u) and the cumulative EGF (z) are given by the following table, S EQ : S ET : C YC : in the labelled case.

1 zr

Br zr 1 · 2 r! (1 − B(z))

1 − B(z) + (u − 1) Brr ! Br zr exp B(z) + (u − 1) r! 1 log r 1 − B(z) + (u − 1) Brr !z

e B(z) ·

Br zr r!

1 Br zr · , (1 − B(z)) r!

Example III.11. Set partitions. Set partitions S are sets of blocks, themselves non-empty sets of elements. The enumeration of set partitions according to the number of blocks is then given by S = S ET(u S ET≥1 (Z))

H⇒

z S(z, u) = eu(e −1) .

Since set partitions are otherwise known to be enumerated by the Stirling partition numbers, one has the BGF and the vertical EGFs as a corollary, X n z n X n z n z 1 uk = eu(e −1) , = (e z − 1)k , k n! n! k! k n n,k

which is consistent with earlier calculations of Chapter II. The EGF of cumulated values, (z) is then almost a derivative of S(z): z d (z) = (e z − 1)ee −1 = S(z) − S(z). dz

Thus, the mean number of blocks in a random partition of size n equals S n = n+1 − 1, Sn Sn a quantity directly expressible in terms of Bell numbers. A delicate computation based on the asymptotic expansion of the Bell numbers reveals that the expected value and the standard deviation are asymptotic to √ n n , , log n log n respectively (Chapter VIII, p. 595). Similarly the exponential BGF of the number of blocks of size k is S = S ET(u S ET=k (Z) + S ET6=0,k (Z))

H⇒

z k S(z, u) = ee −1+(u−1)z /k! ,

out of which mean and variance can also be derived. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Example III.12. Root degree in Cayley trees. Consider the class T of Cayley trees (non-plane labelled trees) and the parameter “root-degree”. The basic specifications are T T (z) = Z ⋆ S ET(T ) = ze T (z) H⇒ T ◦ = Z ⋆ S ET(uT ) T (z, u) = zeuT (z) .

180

III. PARAMETERS AND MULTIVARIATE GFS

The set construction reflects the non-planar character of Cayley trees and the specification T ◦ is enriched by a mark associated to subtrees dangling from the root. Lagrange inversion provides the fraction of trees with root degree k, e−1 1 n! (n − 1)n−2−k ∼ , (k − 1)! (n − 1 − k)! (k − 1)! n n−1

k ≥ 1.

Similarly, the cumulative GF is found to be (z) = T (z)2 , so that the mean root degree satisfies 1 ∼ 2. ETn (root degree) = 2 1 − n Thus the law of root degree is asymptotically a Poisson law of rate 1, shifted by 1. Probabilistic phenomena qualitatively similar to those encountered in plane trees are observed here, since the mean root degree is asymptotic to a constant. However a Poisson law eventually reflecting the non-planarity condition replaces the modified geometric law (known as a negative binomial law) present in plane trees. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

III.13. Numbers of components in alignments. Alignments (O) are sequences of cycles (Chapter II, p. 119). The expected number of components in a random alignment of On is [z n ] log(1 − z)−1 (1 − log(1 − z)−1 )−2 . [z n ](1 − log(1 − z)−1 )−1

Methods of Chapter V imply that the number √ of components in a random alignment has expectation ∼ n/(e − 1) and standard deviation 2( n).

III.14. Image cardinality of a random surjection. The expected cardinality of the image of a random surjection in Rn (Chapter II, p. 106) is

[z n ]e z (2 − e z )−2 . [z n ](2 − e z )−1 The number of values whose preimages have cardinality k is obtained upon replacing the factor e z by z k /k!. By the methods of Chapters IV (p. 259) and V (p. 296), the √ image cardinality of a random surjection has expectation n/(2 log 2) and standard deviation 2( n).

III.15. Distinct component sizes in set partitions. Take the number of distinct block sizes and cycle sizes in set partitions and permutations. The bivariate EGFs are ∞ Y

n=1

n 1 − u + ue z /n! ,

as follows from first principles.

∞ Y

n=1

n 1 − u + ue z /n ,

Postscript: Towards a theory of schemas. Let us look back and recapitulate some of the information gathered in pages 167–180 regarding the number of components in composite structures. The classes considered in Figure III.14 are compositions of two constructions, either in the unlabelled or the labelled universe. Each entry contains the BGF for the number of components (e.g., cycles in permutations, parts in integer partitions, and so on), and the asymptotic orders of the mean and standard deviation of the number of components for objects of size n. Some obvious facts stand out from the data and call for explanation. First the outer construction appears to play the essential rˆole: outer sequence constructs (compare integer compositions, surjections and alignments) tend to dictate a number of

III. 5. RECURSIVE PARAMETERS

181

Unlabelled structures Integer partitions, MS ET ◦ S EQ

z u2 z2 exp u + + ··· 1−z 2 1 − z2 √ √ n log n , 2( n) ∼ √ π 2/3

!

Integer compositions, S EQ ◦ S EQ −1 z 1−u 1−z √ n ∼ , 2( n) 2

Labelled structures Set partitions, S ET ◦ S ET exp u e z − 1 √ n n ∼ ∼ log n log n

Surjections, S EQ ◦ S ET −1 1 − u ez − 1 √ n ∼ , 2( n) 2 log 2

Permutations, S ET ◦ C YC exp u log(1 − z)−1 p ∼ log n, ∼ log n

Alignments, S EQ ◦ C YC −1 1 − u log(1 − z)−1 √ n , 2( n) ∼ e−1

Figure III.14. Major properties of the number of components in six level-two structures. For each class, from top to bottom: (i) specification type; (ii) BGF; (iii) mean and standard deviation of the number of components.

components that is 2(n) on average, while outer set constructs (compare integer partitions, set partitions, and permutations) are associated with a greater variety of asymptotic regimes. Eventually, such facts can be organized into broad analytic schemas, as will be seen in Chapters V–IX.

III.16. Singularity and probability. The differences in behaviour are to be assigned to the rather different types of singularity involved (Chapters IV–VIII): on the one hand sets corresponding algebraically to an exp(·) operator induce an exponential blow-up of singularities; on the other hand sequences expressed algebraically by quasi-inverses (1 − ·)−1 are likely to induce polar singularities. Recursive structures such as trees lead to yet other types of phenomena with a number of components, e.g., the root degree, that is bounded in probability. III. 5. Recursive parameters In this section, we adapt the general methodology of previous sections in order to treat parameters that are defined by recursive rules over structures that are themselves recursively specified. Typical applications concern trees and tree-like structures. Regarding the number of leaves, or more generally, the number of nodes of some fixed degree, in a tree, the method of placing marks applies, as in the non-recursive case. It suffices to distinguish elements of interest and mark them by an auxiliary variable. For instance, in order to mark composite objects made of r components, where r is an integer and K designates any of S EQ, S ET (or MS ET, PS ET), C YC, one

182

III. PARAMETERS AND MULTIVARIATE GFS

should split a construction K(C) as follows: K(C) = uK=r (C) + K6=r (C) = (u − 1)Kr (C) + K(C). This technique gives rise to specifications decorated by marks to which Theorems III.1 and III.2 apply. For a recursively-defined structure, the outcome is a functional equation defining the BGF recursively. The situation is illustrated by Examples III.13 and III.14 below in the case of Catalan trees and the parameter number of leaves. Example III.13. Leaves in general Catalan trees. How many leaves does a random tree of some variety have? Can different varieties of trees be somehow distinguished by the proportion of their leaves? Beyond the botany of combinatorics, such considerations are for instance relevant to the analysis of algorithms since tree leaves, having no descendants, can be stored more economically; see [377, Sec. 2.3] for an algorithmic motivation for such questions. Consider once more the class G of plane unlabelled trees, G = Z × S EQ(G), enumerated ◦ by the Catalan numbers: G n = n1 2n−2 n−1 . The class G where each leaf is marked is G ◦ = Zu + Z × S EQ≥1 (G ◦ )

H⇒

G(z, u) = zu +

zG(z, u) . 1 − G(z, u)

The induced quadratic equation can be solved explicitly q 1 G(z, u) = 1 + (u − 1)z − 1 − 2(u + 1)z + (u − 1)2 z 2 . 2

It is however simpler to expand using the Lagrange inversion theorem which yields n 1 n−1 y k n k G n,k = [u ] [z ]G(z, u) = [u ] [y ] u+ n 1 − y n−k 1 n n−2 y 1 n n−1 = [y ] . = n k n k k−1 (1 − y)n−k

These numbers are known as Narayana numbers, see EIS A001263, and they surface repeatedly in connection with ballot problems. The mean number of leaves is derived from the cumulative GF, which is 1 z 1 , (z) = ∂u G(z, u)|u=1 = z + √ 2 2 1 − 4z so that the mean is n/2 exactly for n √ ≥ 2. The distribution is concentrated since the standard deviation is easily calculated to be O( n). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Example III.14. Leaves and node types in binary trees. The class B of binary plane trees, also 1 2n ) can be specified as enumerated by Catalan numbers (Bn = n+1 n

(33)

B = Z + (B × Z) + (Z × B) + (B × Z × B),

which stresses the distinction between four types of nodes: leaves, left branching, right branching, and binary. Let u 0 , u 1 , u 2 be variables that mark nodes of degree 0,1,2, respectively. Then the root decomposition (33) yields, for the MGF B = B(z, u 0 , u 1 , u 2 ), the functional equation B = zu 0 + 2zu 1 B + zu 2 B 2 ,

which, by Lagrange inversion, gives 2k1 n , Bn,k0 ,k1 ,k2 = n k0 , k1 , k2

III. 5. RECURSIVE PARAMETERS

183

subject to the natural conditions: k0 + k1 + k2 = n and k0 = k2 + 1. Moments can be easily calculated using this approach [499]. In particular, the mean number of nodes of each type is asymptotically: n n n leaves: ∼ , 1–nodes : ∼ , 2–nodes : ∼ . 4 2 4 There is an equal asymptotic proportion of leaves, double nodes, left branching, and right √ branching nodes. Furthermore, the standard deviation is in each case O( n), so that all the corresponding distributions are concentrated. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

III.17. Leaves and node-degree profile in Cayley trees. For Cayley trees, the bivariate EGF with u marking the number of leaves is the solution to T (z, u) = uz + z(e T (z,u) − 1). (By Lagrange inversion, the distribution is expressible in terms of Stirling partition numbers.) The mean number of leaves in a random Cayley tree is asymptotic to ne−1 . More generally, the mean number of nodes of outdegree k in a random Cayley tree of size n is asymptotic to 1 . k! Degrees are thus approximately described by a Poisson law of rate 1. n · e−1

III.18. Node-degree profile in simple varieties of trees. For a family of trees generated by T (z) = zφ(T (z)) with φ a power series, the BGF of the number of nodes of degree k satisfies T (z, u) = z φ(T (z, u)) + φk (u − 1)T (z, u)k ,

where φk = [u k ]φ(u). The cumulative GF is (z) = z

φk T (z)k = φk z 2 T (z)k−1 T ′ (z), 1 − zφ ′ (T (z))

from which expectations can be determined.

III.19. Marking in functional graphs. Consider the class F of finite mappings discussed in Chapter II: F = S ET(K), K = C YC(T ), T = Z ⋆ S ET(T ). The translation into EGFs is 1 , T (z) = ze T (z) . F(z) = e K (z) , K (z) = log 1 − T (z) Here are the bivariate EGFs for (i) the number of components, (ii) the number of maximal trees, (iii) the number of leaves: (i) eu K (z) , (iii)

(ii)

1 1 − T (z, u)

1 , 1 − uT (z)

with

T (z, u) = (u − 1)z + ze T (z,u) .

The trivariate EGF F(u 1 , u 2 , z) of functional graphs with u 1 marking components and u 2 marking trees is F(z, u 1 , u 2 ) = exp(u 1 log(1 − u 2 T (z))−1 ) =

1 . (1 − u 2 T (z))u 1

An explicit expression for the coefficients involves the Stirling cycle numbers.

184

III. PARAMETERS AND MULTIVARIATE GFS

We shall now stop supplying examples that could be multiplied ad libitum, since such calculations greatly simplify when interpreted in the light of asymptotic analysis, as developed in Part B. The phenomena observed asymptotically are, for good reasons, especially close to what the classical theory of branching processes provides (see the books by Athreya–Ney [21] and Harris [324], as well as our discussion in the context of “complete” GFs on p. 196). Linear transformations on parameters and path length in trees. We have so far been dealing with a parameter defined directly by recursion. Next, we turn to other parameters such as path length. As a preamble, one needs a simple linear transformation on combinatorial parameters. Let A be a class equipped with two scalar parameters, χ and ξ , related by χ (α) = |α| + ξ(α). Then, the combinatorial form of BGFs yields X X X z |α| u χ (α) = z |α| u |α|+ξ(α) = (zu)|α| u ξ(α) ; α∈A

α∈A

α∈A

that is, (34)

Aχ (z, u) = Aξ (zu, u).

This is clearly a general mechanism: Linear transformations and MGFs: A linear transformation on parameters induces a monomial substitution on the corresponding marking variables in MGFs. We now put this mechanism to use in the recursive analysis of path length in trees. Example III.15. Path length in trees. The path length of a tree is defined as the sum of distances of all nodes to the root of the tree, where distances are measured by the number of edges on the minimal connecting path of a node to the root. Path length is an important characteristic of trees. For instance, when a tree is used as a data structure with nodes containing additional information, path length represents the total cost of accessing all data items when a search is started from the root. For this reason, path length surfaces, under various models, in the analysis of algorithms, in particular, in the area of algorithms and data structures for searching and sorting (e.g., tree-sort, quicksort, radix-sort [377, 538]). The formal definition of path length of a tree is X dist(ν, root(τ )), (35) λ(τ ) := ν∈τ

where the sum is over all nodes of the tree and the distance between two nodes is measured by the number of connecting edges. The definition implies an inductive rule X (36) λ(τ ) = (λ(υ) + |υ|) , υ≺τ

in which υ ≺ τ indicates a summation over all the root subtrees υ of τ . (To verify the equivalence of (35) and (36), observe that path length also equals the sum of all subtree sizes.) From this point on, we focus the discussion on general Catalan trees (see Note III.20 for other cases): G = Z × S EQ(G). Introduce momentarily the parameter µ(τ ) = |τ |+λ(τ ). Then,

III. 5. RECURSIVE PARAMETERS

185

one has from the inductive definition (36) and the general transformation rule (34): z (37) G λ (z, u) = and G µ (z, u) = G λ (zu, u). 1 − G µ (z, u)

In other words, G(z, u) ≡ G λ (z, u) satisfies a nonlinear functional equation of the difference type: z G(z, u) = . 1 − G(uz, u) (This functional equation will be revisited in connection with area under Dyck paths in Chapter V, p. 330.) The generating function (z) of cumulated values of λ is then obtained by differentiation with respect to u, then setting u = 1. We find in this way that the cumulative GF (z) := ∂u G(z, u)|u=1 satisfies z zG ′ (z) + (z) , (z) = 2 (1 − G(z))

which is a linear equation that solves to (z) = z 2

z G ′ (z) z . − √ = 2(1 − 4z) 2 1 − 4z (1 − G(z))2 − z

Consequently, one has (n ≥ 1)

n = 22n−3 −

1 2n − 2 , 2 n−1

where the sequence starting 1, 5, 22, 93, 386 for n ≥ 2 constitutes EIS A000346. By elementary asymptotic analysis, we get: √ The mean path length of a random Catalan tree of size n is asymptotic to 21 π n 3 ; in short: a branch from the root to a√random node in a random Catalan tree of size n has expected length of the order of n. Random Catalan trees thus tend to be somewhat imbalanced—by comparison, a fully balanced binary tree has all paths of length at most log2 n + O(1). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

The imbalance in random Catalan trees is a general phenomenon—it holds for binary Catalan and more generally for all simple varieties of trees. Note III.20 √ below and Example VII.9 (p. 461) imply that path √ length is invariably of order n n on average in such cases. Height is of typical order n as shown by R´enyi and Szekeres [507], de Bruijn, Knuth, and Rice [145], Kolchin [386], as well as Flajolet and Odlyzko [246]: see Subsection VII. 10.2, p. 535 for the outline of a proof. Figure III.15 borrowed from [538] illustrates this on a simulation. (The contour of the histogram of nodes by levels, once normalized, has been proved to converge to the process known as Brownian excursion.)

III.20. Path length in simple varieties of trees. The BGF of path length in a variety of trees generated by T (z) = zφ(T (z)) satisfies In particular, the cumulative GF is

T (z, u) = zφ(T (zu, u)).

(z) ≡ ∂u (T (z, u))u=1 = from which coefficients can be extracted.

φ ′ (T (z)) (zT ′ (z))2 , φ(T (z))

186

III. PARAMETERS AND MULTIVARIATE GFS

Figure III.15. A random pruned binary tree of size 256 and its associated level profile: the histogram on the left displays the number of nodes at each level in the tree.

III. 6. Complete generating functions and discrete models By a complete generating function, we mean, loosely speaking, a generating function in a (possibly large, and even infinite in the limit) number of variables that mark a homogeneous collection of characteristics of a combinatorial class2 . For instance one might be interested in the joint distribution of all the different letters composing words, the number of cycles of all lengths in permutations, and so on. A complete MGF naturally entails detailed knowledge on the enumerative properties of structures to which it is relative. Complete generating functions, given their expressive power, also make weighted models amenable to calculation, a situation that covers in particular Bernoulli trials (p. 190) and branching processes from classical probability theory (p. 196). Complete GFs for words. As a basic example, consider the class of all words W = S EQ{A} over some finite alphabet A = {a1 , . . . , ar }. Let χ = (χ1 , . . . , χr ), where χ j (w) is the number of occurrences of the letter a j in word w. The MGF of A with respect to χ is A = u 1 a1 + u 2 a2 + · · · u r ar

H⇒

A(z, u) = zu 1 + zu 2 + · · · + zu r ,

and χ on W is clearly inherited from χ on A. Thus, by the sequence rule, one has (38)

W = S EQ(A)

H⇒

W (z, u) =

1 , 1 − z(u 1 + u 2 + · · · + u r )

which describes all words according to their compositions into letters.PIn particular, the number of words with n j occurrences of letter a j and with n = n j is in this

2Complete GFs are not new objects. They are simply an avatar of multivariate GFs. Thus the term is only meant to be suggestive of a particular usage of MGFs, and essentially no new theory is needed in order to cope with them.

III. 6. COMPLETE GENERATING FUNCTIONS AND DISCRETE MODELS

framework obtained as [u n1 1 u n2 2

· · · u rnr ] (u 1

n + u 2 + · · · + ur ) = n 1 , n 2 , . . . , nr n

We are back to the usual multinomial coefficients.

=

187

n! . n 1 !n 2 ! · · · nr

III.21. After Bhaskara Acharya (circa 1150AD). Consider all the numbers formed in decimal with digit 1 used once, with digit 2 used twice,. . . , with digit 9 used nine times. Such numbers all have 45 digits. Compute their sum S and discover, much to your amazement that S equals 45875559600006153219084769286399999999999999954124440399993846780915230713600000.

This number has a long run of nines (and further nines are hidden!). Is there a simple explanation? This exercise is inspired by the Indian mathematician Bhaskara Acharya who discovered multinomial coefficients near 1150AD; see [377, pp. 23–24] for a brief historical note.

Complete GFs for permutations and set partitions. Consider permutations and the various lengths of their cycles. The MGF where u k marks cycles of length k for k = 1, 2, . . . can be written as an MGF in infinitely many variables: ! z2 z3 z (39) P(z, u) = exp u 1 + u 2 + u 3 + · · · . 1 2 3 This MGF expression has the neat feature that, upon restricting all but a finite number of u j to 1, we derive all the particular cases of interest with respect to any finite collection of cycles lengths. Observe also that one can calculate in the usual way any coefficient [z n ]P as it only involves the variables u 1 , . . . , u n .

III.22. The theory of formal power series in infinitely many variables. (This note is for formalists.) Mathematically, an object like P in (39) is perfectly well defined. Let U = {u 1 , u 2 , . . .} be an infinite collection of indeterminates. First, the ring of polynomials R = C[U ] is well defined and a given element of R involves only finitely many indeterminates. Then, from R, one can define the ring of formal power series in z, namely R[[z]]. (Note that, if f ∈ R[[z]], then each [z n ] f involves only finitely many of the variables u j .) The basic operations and the notion of convergence, as described in Appendix A.5: Formal power series, p. 730, apply in a standard way. For instance, in the case of (39), the complete GF P(z, u) is obtainable as the formal limit ! z z k+1 zk P(z, u) = lim exp u 1 + · · · + u k + + ··· 1 k k+1 k→∞ in R[[z]] equipped with the formal topology. (In contrast, the quantity evocative of a generating function of words over an infinite alphabet −1 ∞ X ! W = 1−z u j j=1

cannot be soundly defined as an element of the formal domain R[[z]].)

Henceforth, we shall keep in mind that verifications of formal correctness regarding power series in infinitely many indeterminates are always possible by returning to basic definitions. Complete generating functions are often surprisingly simple to expand. For instance, the equivalent form of (39) P(z, u) = eu 1 z/1 · eu 2 z

2 /2

· eu 3 z

3 /3

···

188

III. PARAMETERS AND MULTIVARIATE GFS

implies immediately that the number of permutations with k1 cycles of size 1, k2 of size 2, and so on, is n! , k1 ! k2 ! · · · kn ! 1k1 2k2 · · · n kn

(40)

P provided jk j = n. This is a result originally due to Cauchy. Similarly, the EGF of set partitions with u j marking the number of blocks of size j is ! z2 z3 z S(z, u) = exp u 1 + u 2 + u 3 + · · · . 1! 2! 3! A formula analogous to (40) follows: the number of partitions with k1 blocks of size 1, k2 of size 2, and so on, is n! . k1 ! k2 ! · · · kn ! 1!k1 2!k2 · · · n!kn

Several examples of such complete generating functions are presented in Comtet’s book; see [129], pages 225 and 233.

III.23. Complete GFs for compositions and surjections.

The complete GFs of integer compositions and surjections with u j marking the number of components of size j are 1−

1 P∞

j j=1 u j z

,

1−

1 P∞

zj j=1 u j j!

.

P The associated counts with n = j jk j are given by n! k1 + k2 + · · · k1 + k2 + · · · , . k1 , k2 , . . . k1 , k2 , . . . 1!k1 2!k2 · · ·

These factored forms follow directly from the multinomial expansion. The symbolic form of the multinomial expansion of powers of a generating function is sometimes expressed in terms of Bell polynomials, themselves nothing but a rephrasing of the multinomial expansion; see Comtet’s book [129, Sec. 3.3] for a fair treatment of such polynomials.

III.24. Fa`a di Bruno’s formula. The formulae for the successive derivatives of a functional composition h(z) = f (g(z)) ∂z h(z) = f ′ (g(z))g ′ (z),

∂z2 h(z) = f ′′ (g(z))g ′ (z)2 + f ′ (z)g ′′ (z), . . . ,

are clearly equivalent to the expansion of a formal power series composition. Indeed, assume without loss of generality that z = 0 and g(0) = 0; set f n := ∂zn f (0), and similarly for g, h. Then k X fk X zn g g1 z + 2 z 2 + · · · . = hn h(z) ≡ n! k! 2! n k

Thus in one direct application of the multinomial expansion, one finds g ℓ k X fk X hn g1 ℓ1 g2 ℓ2 k k ··· , = n! k! 1! 2! k! ℓ1 , ℓ2 , . . . , ℓk k

C

where the summation condition C is: 1ℓ1 + 2ℓ2 + · · · + kℓk = n, ℓ1 + ℓ2 + · · · + ℓk = k. This shallow identity is known as Fa`a di Bruno’s formula [129, p. 137]. (Fa`a di Bruno (1825– 1888) was canonized by the Catholic Church in 1988, presumably for reasons unrelated to his formula.)

III. 6. COMPLETE GENERATING FUNCTIONS AND DISCRETE MODELS

189

III.25. Relations between symmetric functions. Symmetric functions may be manipulated by mechanisms that are often reminiscent of the set and multiset construction. They appear in many areas of combinatorial enumeration. Let X = {xi }ri=1 be a collection of formal variables. Define the symmetric functions Y X Y X xi z X X 1 an z n , (1 + xi z) = bn z n , cn z n . = = 1 − x z 1 − x z i i n n n i

i

i

The an , bn , cn , called, respectively, elementary, monomial, and power symmetric functions, are expressible as an =

X

i 1 σi+1

unary left-branching (u ′1 )

valley:

σi−1 > σi < σi+1

binary node (u 2 )

Figure III.17. Local order patterns in a permutation and the four types of nodes in the corresponding increasing binary tree.

inherited and the corresponding exponential MGFs are related by Z z A(z, u) = (∂t B(t, u)) · C(t, u) dt. 0

To illustrate this multivariate extension, we shall consider a quadrivariate statistic on permutations. Example III.23. Local order patterns in permutations. An element σi of a permutation written σ = σ1 , . . . , σn when compared to its immediate neighbours can be categorized into one of four types4 summarized in the first two columns of Figure III.17. The correspondence with binary increasing trees described in Example II.17 and Figure II.16 (p. 143) then shows the following: peaks and valleys correspond to leaves and binary nodes, respectively, while double rises and double falls are associated with right-branching and left-branching unary nodes. Consider the class b I of non-empty increasing binary trees (so that b I = I \ {ǫ} in the notations of p. 143) and let u 0 , u 1 , u ′1 , u 2 be markers for the number of nodes of each type, as summarized in Figure III.17. Then the exponential MGF of non-empty increasing trees under this statistic is given by b I = u 0 Z + u 1 (Z 2 ⋆ b I) + u ′1 (b I ⋆ Z 2 ) + u 2 (b I ⋆ Z2 ⋆ b I) Z z H⇒ b I (z) = u 0 z + I (w)2 dw, (u 1 + u 1 )b I (w) + u 2 b 0

which gives rise to the differential equation:

∂ b I (z, u) = u 0 + (u 1 + u ′1 )b I (z, u) + u 2 b I (z, u)2 . ∂z This is solved by separation of variables as v δ v 1 + δ tan(zδ) b − 1, I (z, u) = u 2 δ − v 1 tan(zδ) u 2 where the following abbreviations are used: q 1 v 1 = (u 1 + u ′1 ), δ = u 0 u 2 − v 12 . 2 One finds

(66)

z3 z2 b I = u 0 z + u 0 (u 1 + u ′1 ) + u 0 ((u 1 + u ′1 )2 + 2u 0 u 2 ) + · · · , 2! 3!

4Here, for |σ | = n, we regard σ as bordered by (−∞, −∞), i.e., we set σ = σ 0 n+1 = −∞ and let

the index i in Figure III.17 vary in [1 . . n]. Alternative bordering conventions prove occasionally useful.

III. 7. ADDITIONAL CONSTRUCTIONS

203

Figure III.18. The level profile of a random increasing binary tree of size 256. (Compare with Figure III.15, p. 186, for binary trees drawn under the uniform Catalan statistics.)

which agrees with the small cases. This calculation is consistent with what has been found in Chapter II regarding the EGF of all non-empty permutations and of alternating permutations, z , tan(z), 1−z

that follow from the substitutions {u 0 = u 1 = u ′1 = u 2 = 1} and {u 0 = u 2 = 1, u 1 = u ′1 = 0}, respectively. The substitution {u 0 = u 1 = u, u ′1 = u 2 = 1} gives a simple variant (without the empty permutation) of the BGF of Eulerian numbers (75) on p. 209. From the quadrivariate GF, there results that, in a tree of size n the mean number of nodes of nullary, unary, or binary type is asymptotic to n/3, with a variance that is O(n), thereby ensuring concentration of distribution. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

A similar analysis yields path length. It is found that a random increasing binary tree of size n has mean path length 2n log n + O(n). Contrary to what the uniform combinatorial model gives, such trees tend to be rather well balanced, and a typical branch is only about 38.6% longer than in a perfect binary . tree (since 2/ log 2 = 1.386): see Figure III.18 for an illustration. This fact applies to binary search trees (Note III.33) and it justifies the fact that the performance of such trees is quite good, when they are applied to random data [378, 429, 538] or subjected to randomization [451, 520]. See Subsection VI. 10.3 (p. 427) dedicated to tree recurrences for a general analysis of additive functionals on such trees and Example IX.28, p. 684, for a distributional analysis of depth.

III.33. Binary search trees (BSTs). BST(τ )

Given a permutation τ , one defines inductively a tree

by

BST(ǫ) = ∅; BST(τ ) = hτ1 , BST(τ |τ1 )i. (Here, τ | P represents the subword of τ consisting of those elements that satisfy predicate P.) Let IBT(σ ) be the increasing binary tree canonically associated to σ . Then one has the fundamental Equivalence Principle, IBT(σ )

shape

shape

≡

BST(σ

−1 ),

where A ≡ B means that A and B have identical tree shapes. (Hint: relate the trees to the cartesian representation of permutations [538, 600], as in Example II.17, p. 143.)

III. 7.3. Implicit structures. For implicit structures defined by a relation of the form A = K[X ], we note that equations involving sums and products, either labelled

204

III. PARAMETERS AND MULTIVARIATE GFS

or not, are easily solved just as in the univariate case. The same remark applies for sequence and set constructions: refer to the corresponding sections of Chapters I (p. 88) and II (p. 137). Again, the process is best understood by examples. Suppose for instance one wants to enumerate connected labelled graphs by the number of nodes (marked by z) and the number of edges (marked by u). The class K of connected graphs and the class G of all graphs are related by the set construction, G = S ET(K), meaning that every graph decomposes uniquely into connected components. The corresponding exponential BGFs then satisfy G(z, u) = e K (z,u)

implying

K (z, u) = log G(z, u),

since the number of edges in a graph is inherited (additively) from the corresponding numbers in connected components. Now, the number of graphs of size n having k , so that edges is n(n−1)/2 k ! ∞ n X n(n−1)/2 z (1 + u) (67) K (z, u) = log 1 + . n! n=1

This formula, which appears as a refinement of the univariate formula of Chapter II (p. 138), then simply reads: connected graphs are obtained as components (the log operator) of general graphs, where a general graph is determined by the presence or absence of an edge (corresponding to (1+u)) between any pair of nodes (the exponent n(n − 1)/2). To pull information out of the formula (67) is, however, not obvious due to the alternation of signs in the expansion of log(1 + w) and due to the strongly divergent character of the involved series. As an aside, we note here that the quantity b(z, u) = K z , u K u enumerates connected graphs according to size (marked by z) and excess (marked by u) of the number of edges over the number of nodes. This means that the results of Note II.23 (p. 135), obtained by Wright’s decomposition, can be rephrased as the expansion (within C(u)[[z]]): ! ∞ n −n X 1 n(n−1)/2 z u log 1 + (1 + u) W−1 (z) + W0 (z) + · · · = n! u n=1 (68) 1 1 1 1 1 1 T − T2 + log − T − T2 + ··· , = u 2 2 1−T 2 4 with T ≡ T (z). See Temperley’s early works [573, 574] as well as the “giant paper on the giant component” [354] and the paper [254] for direct derivations that eventually constitute analytic alternatives to Wright’s combinatorial approach.

Example III.24. Smirnov words. Following the treatment of Goulden and Jackson [303], we define a Smirnov word to be any word that has no consecutive equal letters. Let W = S EQ(A) be the set of words over the alphabet A = {a1 , . . . , ar } of cardinality r , and S be the set of

III. 7. ADDITIONAL CONSTRUCTIONS

205

Smirnov words. Let also v j mark the number of occurrences of the jth letter in a word. One has5 1 W (v 1 , . . . , vr ) = 1 − (v 1 + · · · + vr ) Start from a Smirnov word and substitute for any letter a j that appears in it an arbitrary nonempty sequence of letters a j . When this operation is done at all places of a Smirnov word, it gives rise to an unconstrained word. Conversely, any word can be associated to a unique Smirnov word by collapsing into single letters maximal groups of contiguous equal letters. In other terms, arbitrary words are derived from Smirnov words by a simultaneous substitution: W = S a1 7→ S EQ≥1 {a1 }, . . . , ar 7→ S EQ≥1 {ar } . This leads to the relation (69)

W (v 1 , . . . , vr ) = S

vr v1 , ... , 1 − v1 1 − vr

.

This relation determines the MGF S(v 1 , . . . , vr ) implicitly. Now, since the inverse function of v/(1 − v) is v/(1 + v), one finds the solution: −1 r X vj v1 vr . (70) S(v 1 , . . . , vr ) = W , ... , = 1 − 1 + v1 1 + vr 1+vj j=1

For instance, if we set v j = z, that is, we “forget” the composition of the words into letters, we obtain the OGF of Smirnov words counted according to length as X 1 1+z =1+ r (r − 1)n−1 z n . = z 1 − r 1+z 1 − (r − 1)z n≥1

This is consistent with elementary combinatorics since a Smirnov word of length n is determined by the choice of its first letter (r possibilities) followed by a sequence of n − 1 choices constrained to avoid one letter among r (and corresponding to r − 1 possibilities for each position). The interest of (70) is to apply equally well to the Bernoulli model where letters may receive unequal probabilities and where a direct combinatorial argument does not appear to be easy: it suffices to perform the substitution v j 7→ p j z in this case: see Example IV.10, p. 262 and Note V.11, p. 311, for applications to asymptotics. From these developments, one can next build the GF of words that never contain more than m consecutive equal letters. It suffices to effect in (70) the substitution v j 7→ v j + · · · + vm j . In particular for the univariate problem (or, equivalently, the case where letters are equiprobable), one finds the OGF 1 1−z m

1−r

z 1−z m 1 + z 1−z 1−z

=

1 − z m+1 . 1 − r z + (r − 1)z m+1

This extends to an arbitrary alphabet the analysis of single runs and double runs in binary words that was performed in Subsection I. 4.1, p. 51. Naturally, the present approach applies equally well to non-uniform letter probabilities and to a collection of run-length upper-bounds and lower-bounds dependent on each particular letter. This topic is in particular pursued by different methods in several works of Karlin and coauthors (see, e.g., [446]), themselves motivated by applications to life sciences. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5The variable z marking length, being redundant, is best omitted in this calculation.

206

III. PARAMETERS AND MULTIVARIATE GFS

III.34. Enumeration in free groups. Consider the composite alphabet B = A ∪ A, where

A = {a1 , . . . , ar } and A = {a1 , . . . , ar }. A word over alphabet B is said to be reduced if it arises from a word over B by a maximal application of the reductions a j a j 7→ ǫ and a j a j 7→ ǫ (with ǫ the empty word). A reduced word thus has no factor of the form a j a j or a j a j . Such a reduced word serves as a canonical representation of an element in the free group Fr generated by A, upon identifying a j = a −1 j . The GF of the class R of reduced words, with u j and u j marking the number of occurrences of letter a j and a j , respectively, is ur u1 u1 ur + , ..., + , R(u 1 , . . . , u r , u 1 , . . . , u r ) = S 1 − u1 1 − u1 1 − ur 1 − ur where S is the GF of Smirnov words, as in (70). In particular this gives the OGF of reduced words with z marking length as R(z) = (1 + z)/(1 − (2r − 1)z); this implies Rn = 2r (2r − 1)n , which matches the result given by elementary combinatorics. The Abelian image λ(w) of an element w of the free group Fk is obtained by letting all letters commute and applying the reductions a j · a −1 j = 1. It can then be put under the form m

a1 1 · · · arm r , with each m j in Z, so that it can be identified with an element of Zr . Let x = m (x1 , . . . , xr ) be a vector of indeterminates and define xλ(w) to be the monomial x1 1 · · · xrm r . Of interest in certain group-theoretic investigations is the MGF of reduced words ! X zx1−1 zx1 zxr zxr−1 |w| λ(w) Q(z; x) := z x =S , , ..., + + 1 − zx1 1 − zxr 1 − zx −1 1 − zxr−1 w∈R

1

which is found to simplify to Q(z; x) =

1−z

Pr

1 − z2

−1 2 j=1 (x j + x j ) + (2r − 1)z

.

This last form appears in a paper of Rivin [514], where it is obtained by matrix techniques. Methods developed in Chapter IX can then be used to establish central and local limit laws for the asymptotic distribution of λ(w) over Rn , providing an alternative to the methods of Rivin [514] and Sharp [539]. (This note is based on an unpublished memo of Flajolet, Noy, and Ventura, 2006.)

III.35. Carlitz compositions II. Here is an alternative derivation of the OGF of Carlitz compositions (Note III.32, p. 201). Carlitz compositions with largest summand ≤ r are obtained from the OGF of Smirnov words by the substitution v j 7→ z j : −1 r j X z , (71) K [r ] (z) = 1 − 1+zj j=1

The OGF of all Carlitz compositions then results from letting r → ∞: −1 ∞ j X z . (72) K (z) = 1 − 1+zj j=1

The asymptotic form of the coefficients is derived in Chapter IV, p. 263.

III. 7.4. Inclusion–exclusion. Inclusion–exclusion is a familiar type of reasoning rooted in elementary mathematics. Its principle, in order to count exactly, consists in grossly overcounting, then performing a simple correction of the overcounting, then correcting the correction, and so on. Characteristically, enumerative results provided by inclusion exclusion involve an alternating sum. We revisit this process here in the

III. 7. ADDITIONAL CONSTRUCTIONS

207

perspective of multivariate generating functions, where it essentially reduces to a combined use of substitution and implicit definitions. Our approach follows Goulden and Jackson’s encyclopaedic treatise [303]. Let E be a set endowed with a real- or complex-valued measure | · | in such a way that, for A, B ⊂ E, there holds |A ∪ B| = |A| + |B|

whenever

A ∩ B = ∅.

Thus, | · | is an additive measure, typically taken as set cardinality (i.e., |e| = 1 for e ∈ E) or a discrete probability measure on E (i.e., |e| = pe for e ∈ E). The general formula |A ∪ B| = |A| + |B| − |AB| where AB := A ∩ B, follows immediately from basic set-theoretic principles: X X X X |c| = |a| + |b| − |i|. c∈A∪B

a∈A

b∈B

i∈A∩B

What is called the inclusion–exclusion principle or sieve formula is the following multivariate generalization, for an arbitrary family A1 , . . . , Ar ⊂ E: |A1 ∪ · · · ∪ Ar | ≡ E \ (A1 A2 · · · Ar ) X X (73) = |Ai | − |Ai1 Ai2 | + · · · + (−1)r −1 |A1 A2 · · · Ar |, 1≤i 1 0 and s = 0, respectively, W hsi (z) =

z k N (z)s−1 , D(z)s+1

W h0i (z) =

c(z) , D(z)

with N (z) and D(z) given by N (z) = (1 − r z)(c(z) − 1) + z k ,

D(z) = (1 − r z)c(z) + z k .

The expression of W h0i is in agreement with Chapter I, Equation (62), p. 61.

III.39. Patterns in Bernoulli sequences. Let A be an alphabet where letter α has probability πα and consider the Bernoulli model where letters in words are chosen independently. Fix a pattern p = p1 · · · pk and define the finite language of protrusions as [ Ŵ= {pi+1 pi+2 · · · pk }, i : ci 6=0

where the union is over all correlation positions of the pattern. Define now the correlation polynomial γ (z) (relative to p and the πα ) as the generating polynomial of the finite language of protrusions weighted by (πα ). For instance, p = ababa gives rise to Ŵ = {ǫ, ba, baba} and γ (z) = 1 + πa πb z 2 + πa2 πb2 z 4 .

The BGF of words with z marking length and u marking the number of occurrences of p is (u − 1)γ (z) − u , W (z, u) = (1 − z)((u − 1)γ (z) − u) + (u − 1)π [p]z k where π [p] is the product of the probabilities of letters of p.

III.40. Patterns in trees I. Consider the class B of pruned binary trees. An occurrence of pattern t in a tree τ is defined by a node of τ whose dangling subtree is isomorphic to t. We seek the BGF B(z, u) of class B where √ u marks the number of occurrences of t. The OGF of B is B(z) = (1− 1 − 4z)/(2z). The quantity v B(zv) is the BGF of B with v marking external nodes. By virtue of the pointing operation, the quantity 1 k ∂v (v B(zv)) , Uk := k! v=1

214

III. PARAMETERS AND MULTIVARIATE GFS

describes trees with k distinct external nodes distinguished (pointed). Let m = |t|. The quantity X V := Uk u k (z m )k satisfies V = (v B(zv))v=1+uz m ,

by virtue of Taylor’s formula. It is also the BGF of trees with distinguished occurrences of t marked by v. Setting v 7→ u − 1 in V then gives B(z, u) as q 1 1 − 1 − 4z − 4(u − 1)z m+1 . (78) B(z, u) = 2z p 1 1 − 1 − 4z + 4z m+1 represents the OGF of trees not containing In particular B(z, 0) = 2z pattern t. The method generalizes to any simple variety of trees. It can be used to prove that the factored p representation (as a directed acyclic graph) of a random tree of size n has expected size O(n/ log n). (These results appear in [257]; see also Example IX.26, p. 680, for a related Gausian law.)

III.41. Patterns in trees II. Here follows an alternative derivation of (78) that is based on the

root decomposition of trees. A pattern t occurs either in the left root subtree τ0 , or in the right root subtree τ1 , or at the root iself in the case in which t coincides with τ . Thus the number ω[τ ] of occurrences of t in τ satisfies the recursive definition ω[τ ] = ω[τ0 ] + ω[τ1 ] + [[τ = t]], ω[τ ] The function u is almost multiplicative, and

ω[∅] = 0.

u ω[τ ] = u [[τ =t]] u ω[τ0 ] u ω[τ1 ] = u ω[τ0 ] u ω[τ1 ] + [[τ = t]] · (u − 1). P Thus, the bivariate generating function B(z, u) := t z |t| u ω[t] satisfies the quadratic equation, B(z, u) = 1 + (u − 1)z m + z B(z, u)2 ,

which, when solved, yields (78).

III. 8. Extremal parameters Apart from additively inherited parameters already examined at length in this chapter, another important category is that of parameters defined by a maximum rule. Two major cases are the largest component in a combinatorial structure (for instance, the largest cycle of a permutation) and the maximum degree of nesting of constructions in a recursive structure (typically, the height of a tree). In this case, bivariate generating functions are of little help, because of the nonlinear character of the maxfunction. The standard technique consists in introducing a collection of univariate generating functions defined by imposing a bound on the parameter of interest. Such GFs can then be constructed by the symbolic method in its univariate version. III. 8.1. Largest components. Consider a construction B = 8[A], where 8 may involve an arbitrary combination of basic constructions, and assume here for simplicity that the construction for B is a non-recursive one. This corresponds to a relation between generating functions B(z) = 9[A(z)], where 9 is the functional that is the “image” of the combinatorial construction 8. Elements of A thus appear as components in an object β ∈ B. Let B hbi denote the subclass of B formed with objects whose A–components all have a size at most b. The

III. 8. EXTREMAL PARAMETERS

215

GF of B hbi is obtained by the same process as that of B itself, save that A(z) should be replaced by the GF of elements of size at most b. Thus, B hbi (z) = 9[Tb A(z)], where the truncation operator is defined on series by Tb f (z) =

b X

fn z

n=0

n

( f (z) =

∞ X

f n z n ).

n=0

Example III.27. A pot-pourri of largest components. Several instances of largest components have already been analysed in Chapters I and II. For instance, the cycle decomposition of permutations translated by 1 P = S ET(C YC(Z)) H⇒ P(z) = exp log 1−z gives more generally the EGF of permutations with longest cycle ≤ b, ! z2 zb z , + + ··· + P hbi (z) = exp 1 2 b which involves the truncated logarithm. The labelled specification of words over an m–ary alphabet W = S ETm (S ET(Z))

H⇒

W (z) = e z

leads to the EGF of words such that each letter occurs at most b times: !m z2 zb z hbi + + ··· + , W (z) = 1 + 1! 2! b!

m

which now involves the truncated exponential. Similarly, the EGF of set partitions with largest block of size at most b is ! z2 zb z hbi . + + ··· + S (z) = exp 1! 2! b! A slightly less direct example is that of the longest run in a binary string (p. 51), which we now revisit. The collection W of binary words over the alphabet {a, b} admits the unlabelled specification W = S EQ(a) · S EQ(b S EQ(a)), corresponding to a “scansion” dictated by the occurrences of the letter b. The corresponding OGF then appears under the form 1 1 W (z) = Y (z) · , where Y (z) = 1 − zY (z) 1−z corresponds to Y = S EQ(a). Thus, the OGF of strings with at most k − 1 consecutive occurrences of the letter a obtains upon replacing Y (z) by its truncation: 1 W hki (z) = Y hki (z) , where Y hki (z) = 1 + z + z 2 + · · · + z k−1 , 1 − zY hki (z) so that 1 − zk W hki (z) = . 1 − 2z + z k+1 An asymptotic analysis is given in Example V.4, p. 308. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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III. PARAMETERS AND MULTIVARIATE GFS

Generating functions for largest components are thus easy to derive. The asymptotic analysis of their coefficients is however often hard when compared to additive parameters, owing to the need to rely on complex analytic properties of the truncation operator. The bases of a general asymptotic theory have been laid by Gourdon [305].

III.42. Smallest components. The EGF of permutations with smallest cycle of size > b is z z2 zb 1 exp − − − ··· − 1−z 1 2 b

!

.

A symbolic theory of smallest components in combinatorial structures is easily developed as regards formal GFs. Elements of the corresponding asymptotic theory are provided by Panario and Richmond in [470].

III. 8.2. Height. The degree of nesting of a recursive construction is a generalization of the notion of height in the simpler case of trees. Consider for instance a recursively defined class B = 8[B], where 8 is a construction. Let B [h] denote the subclass of B composed solely of elements whose construction involves at most h applications of 8. We have by definition B [h+1] = 8{B [h] }. Thus, with 9 the image functional of construction 8, the corresponding GFs are defined by a recurrence, B [h+1] = 9[B [h] ].

(This discussion is related to the semantics of recursion, p. 33.)

Example III.28. Generating functions for tree height. Consider first general plane trees: z . G = Z × S EQ(G) H⇒ G(z) = 1 − G(z) Define the height of a tree as the number of edges on its longest branch. Then the set of trees of height ≤ h satisfies the recurrence G [0] = Z,

G [h+1] = Z × S EQ(G [h] ).

Accordingly, the OGF of trees of bounded height satisfies G [0] (z) = z,

G [h+1] (z) =

The recurrence unwinds and one finds (79)

G [h] (z) =

z . 1 − G [h] (z)

z 1−

,

z 1−

z ..

.

1−z where the number of stages in the fraction equals b. This is the finite form (technically known as a “convergent”) of a continued fraction expansion. From implied linear recurrences and an analysis based on Mellin transforms, de Bruijn, Knuth, and Rice [145] have determined the √ average height of a general plane tree to be ∼ π n. We provide a proof of this fact in Chapter V (p. 329) dedicated to applications of rational and meromorphic asymptotics.

III. 8. EXTREMAL PARAMETERS

217

For plane binary trees defined by B =Z +B×B

B(z) = z + (B(z))2 ,

so that

(size here is the number of external nodes), the recurrence is B [0] (z) = z, B [h+1] (z) = z + (B [h] (z))2 .

In this case, the B [h] are the approximants to a “continuous quadratic form”, namely B [h] (z) = z + (z + (z + (· · · )2 )2 )2 .

These are polynomials of degree 2h for which no closed form expression is known, nor even likely to exist6. However, using complex asymptotic methods and singularity analysis, √ Flajolet and Odlyzko [246] have shown that the average height of a binary plane tree is ∼ 2 π n. See Subsection VII. 10.2, p. 535 for the sketch of a proof. For Cayley trees, finally, the defining equation is T = Z ⋆ S ET(T )

T (z) = ze T (z) .

H⇒

The EGF of trees of bounded height satisfy the recurrence T [0] (z) = z,

[h] T [h+1] (z) = ze T (z) .

We are now confronted with a “continuous exponential”, T [h] (z) = ze ze

ze

..

. ze z .

The average height was found√by R´enyi and Szekeres who appealed again to complex analytic methods and found it to be ∼ 2π n. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

These examples show that height statistics are closely related to iteration theory. Except in a few cases like general plane trees, normally no algebra is available and one has to resort to complex analytic methods as expounded in forthcoming chapters. III. 8.3. Averages and moments. For extremal parameters, the GFs of mean values obey a general pattern. Let F be some combinatorial class with GF f (z). Consider for instance an extremal parameter χ such that f [h] (z) is the GF of objects with χ parameter at most h. The GF of objects for which χ = h exactly is equal to f [h] (z) − f [h−1] (z).

Thus differencing gives access to the probability distribution of height over F. The generating function of cumulated values (providing mean values after normalization) is then ∞ h i X 4(z) = h f [h] (z) − f [h−1] (z) h=0

=

∞ h X h=0

i f (z) − f [h] (z) ,

as is readily checked by rearranging the second sum, or equivalently using summation by parts. 6 These polynomials are exactly the much-studied Mandelbrot polynomials whose behaviour in the

complex plane gives rise to extraordinary graphics (Figure VII.23, p. 536).

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III. PARAMETERS AND MULTIVARIATE GFS

For the largest components, the formulae involve truncated Taylor series. For height, analysis involves in all generality the differences between the fixed point of a functional 8 (the GF f (z)) and the approximations to the fixed point ( f [h] (z)) provided by iteration. This is a common scheme in extremal statistics.

III.43. The height of increasing binary trees. Given the specification of increasing binary trees in Equation (61), p. 143, the EGF of trees of height at most h is given by the recurrence Z z I [0] (z) = 1, I [h+1] (z) = 1 + I [h] (w)2 dw. 0

Devroye [157, 158] showed in 1986 that the expected height of a tree of size n is asymptotic . to c log n where c = 4.31107 is a solution of c log((2e)/c) = 1.

III.44. Hierarchical partitions. Let ε(z) = ez − 1. The generating function

ε(ε(· · · (ε(z)))) (h times). can be interpreted as the EGF of certain hierarchical partitions. (Such structures show up in statistical classification theory [585, 586].)

III.45. Balanced trees. Balanced structures lead to counting GFs close to the ones obtained for height statistics. The OGF of balanced 2–3 trees of height h counted by the number of leaves satisfies the recurrence Z [h+1] (z) = Z [h] (z 2 + z 3 ) = (Z [h] (z))2 + (Z [h] (z))3 ,

which can be expressed in terms of the iterates of σ (z) = z 2 + z 3 (see Note I.67, p. 91, as well as Chapter IV, p. 281, for asymptotics). It is possible to express the OGF of cumulated values of the number of internal nodes in such trees in terms of the iterates of σ .

III.46. Extremal statistics in random mappings. One can express the EGFs relative to the largest cycle, longest branch, and diameter of functional graphs. Similarly for the largest tree, largest component. [Hint: see [247] for details.] III.47. Deep nodes in trees. The BGF giving the number of nodes at maximal depth in a general plane tree or a Cayley tree can be expressed in terms of a continued fraction or a continuous exponential.

III. 9. Perspective The message of this chapter is that we can use the symbolic method not just to count combinatorial objects but also to quantify their properties. The relative ease with which we are able to do so is testimony to the power of the method as a major organizing principle of analytic combinatorics. The global framework of the symbolic method leads us to a natural structural categorization of parameters of combinatorial objects. First, the concept of inherited parameters permits a direct extension of the already seen formal translation mechanisms from combinatorial structures to GFs, for both labelled and unlabelled objects—this leads to MGFs useful for solving a broad variety of classical combinatorial problems. Second, the adaptation of the theory to recursive parameters provides information about trees and similar structures, this even in the absence of explicit representations of the associated MGFs. Third, extremal parameters, which are defined by a maximum rule (rather than an additive rule), can be studied by analysing families of univariate GFs. Yet another illustration of the power of the symbolic method is found in the notion of complete GF, which in particular enables us to study Bernoulli trials and branching processes.

III. 9. PERSPECTIVE

219

As we shall see starting with Chapter IV, these approaches become especially powerful since they serve as the basis for the asymptotic analysis of properties of structures. Not only does the symbolic method provide precise information about particular parameters, but it also paves the way for the discovery of general schemas and theorems that tell us what to expect about a broad variety of combinatorial types. Bibliographic notes. Multivariate generating functions are a common tool from classical combinatorial analysis. Comtet’s book [129] is once more an excellent source of examples. A systematization of multivariate generating functions for inherited parameters is given in the book by Goulden and Jackson [303]. In contrast generating functions for cumulated values of parameters (related to averages) seemed to have received relatively little attention until the advent of digital computers and the analysis of algorithms. Many important techniques are implicit in Knuth’s treatises, especially [377, 378]. Wilf discusses related issues in his book [608] and the paper [606]. Early systems specialized to tree algorithms were proposed by Flajolet and Steyaert in the 1980s [215, 261, 262, 560]; see also Berstel and Reutenauer’s work [56]. Some of the ideas developed there initially drew their inspiration from the well-established treatment of formal power series in non-commutative indeterminates; see the books by Eilenberg [189] and Salomaa and Soittola [527] as well as the proceedings edited by Berstel [54]. Several computations in this area can nowadays even be automated with the help of computer algebra systems [255, 528, 628].

Je n’ai jamais e´ t´e assez loin pour bien sentir l’application de l’alg`ebre a` la g´eom´etrie. Je n’aimais point cette mani`ere d’op´erer sans voir ce qu’on fait, et il me sembloit que r´esoudre un probl`eme de g´eom´etrie par les e´ quations, c’´etoit jouer un air en tournant une manivelle. (“I never went far enough to get a good feel for the application of algebra to geometry. I was not pleased with this method of operating according to the rules without seeing what one does; solving geometrical problems by means of equations seemed like playing a tune by turning a crank.”)

— J EAN -JACQUES ROUSSEAU, Les Confessions, Livre VI

Part B

COMPLEX ASYMPTOTICS

IV

Complex Analysis, Rational and Meromorphic Asymptotics Entre deux v´erit´es du domaine r´eel, le chemin le plus facile et le plus court passe bien souvent par le domaine complexe. PAUL PAINLEV E´ [467, p. 2] It has been written that the shortest and best way between two truths of the real domain often passes through the imaginary one1. — JACQUES H ADAMARD [316, p. 123]

IV. 1. IV. 2. IV. 3. IV. 4. IV. 5. IV. 6. IV. 7. IV. 8.

Generating functions as analytic objects Analytic functions and meromorphic functions Singularities and exponential growth of coefficients Closure properties and computable bounds Rational and meromorphic functions Localization of singularities Singularities and functional equations Perspective

225 229 238 249 255 263 275 286

Generating functions are a central concept of combinatorial theory. In Part A, we have treated them as formal objects; that is, as formal power series. Indeed, the major theme of Chapters I–III has been to demonstrate how the algebraic structure of generating functions directly reflects the structure of combinatorial classes. From now on, we examine generating functions in the light of analysis. This point of view involves assigning values to the variables that appear in generating functions. Comparatively little benefit results from assigning only real values to the variable z that figures in a univariate generating function. In contrast, assigning complex values turns out to have serendipitous consequences. When we do so, a generating function becomes a geometric transformation of the complex plane. This transformation is very regular near the origin—one says that it is analytic (or holomorphic). In other words, near 0, it only effects a smooth distortion of the complex plane. Farther away from the origin, some cracks start appearing in the picture. These cracks—the dignified name is singularities—correspond to the disappearance of smoothness. It turns out that a function’s singularities provide a wealth of information regarding the function’s coefficients, and especially their asymptotic rate of growth. Adopting a geometric point of view for generating functions has a large pay-off. 1Hadamard’s quotation (1945) is a free rendering of the original one due to Painlev´e (1900); namely, “The shortest and easiest path betwen two truths of the real domain most often passes through the complex domain.”

223

224

IV. COMPLEX ANALYSIS, RATIONAL AND MEROMORPHIC ASYMPTOTICS

By focusing on singularities, analytic combinatorics treads in the steps of many respectable older areas of mathematics. For instance, Euler recognized that for the Riemann zeta function ζ (s) to become infinite (hence have a singularity) at 1 implies the existence of infinitely many prime numbers; Riemann, Hadamard, and de la Vall´ee-Poussin later uncovered deep connections between quantitative properties of prime numbers and singularities of 1/ζ (s). The purpose of this chapter is largely to serve as an accessible introduction or a refresher of basic notions regarding analytic functions. We start by recalling the elementary theory of functions and their singularities in a style tuned to the needs of analytic combinatorics. Cauchy’s integral formula expresses coefficients of analytic functions as contour integrals. Suitable uses of Cauchy’s integral formula then make it possible to estimate such coefficients by suitably selecting an appropriate contour of integration. For the common case of functions that have singularities at a finite distance, the exponential growth formula relates the location of the singularities closest to the origin—these are also known as dominant singularities—to the exponential order of growth of coefficients. The nature of these singularities then dictates the fine structure of the asymptotics of the function’s coefficients, especially the subexponential factors involved. As regards generating functions, combinatorial enumeration problems can be broadly categorized according to a hierarchy of increasing structural complexity. At the most basic level, we encounter scattered classes, which are simple enough, so that the associated generating function and coefficients can be made explicit. (Examples of Part A include binary and general plane trees, Cayley trees, derangements, mappings, and set partitions). In that case, elementary real-analysis techniques usually suffice to estimate asymptotically counting sequences. At the next, intermediate, level, the generating function is still explicit, but its form is such that no simple expression is available for coefficients. This is where the theory developed in this and the next chapters comes into play. It usually suffices to have an expression for a generating function, but not necessarily its coefficients, so as to be able to deduce precise asymptotic estimates of its coefficients. (Surjections, generalized derangements, unary–binary trees are easily subjected to this method. A striking example, that of trains, is detailed in Section IV. 4.) Properties of analytic functions then make this analysis depend only on local properties of the generating function at a few points, its dominant singularities. The third, highest, level, within the perspective of analytic combinatorics, comprises generating functions that can no longer be made explicit, but are only determined by a functional equation. This covers structures defined recursively or implicitly by means of the basic constructors of Part A. The analytic approach even applies to a large number of such cases. (Examples include simple families of trees, balanced trees, and the enumeration of certain molecules treated at the end of this chapter. Another characteristic example is that of non-plane unlabelled trees treated in Chapter VII.) As we shall see throughout this book, the analytic methodology applies to almost all the combinatorial classes studied in Part A, which are provided by the symbolic method. In the present chapter we carry out this programme for rational functions and meromorphic functions (i.e., functions whose singularities are poles).

IV. 1. GENERATING FUNCTIONS AS ANALYTIC OBJECTS

225

IV. 1. Generating functions as analytic objects Generating functions, considered in Part A as purely formal objects subject to algebraic operations, are now going to be interpreted as analytic objects. In so doing one gains easy access to the asymptotic form of their coefficients. This informal section offers a glimpse of themes that form the basis of Chapters IV–VII. In order to introduce the subject, let us start with two simple generating functions, one, f (z), being the OGF of the Catalan numbers (cf G(z), p. 35), the other, g(z), being the EGF of derangements (cf D (1) (z), p. 123): √ exp(−z) 1 g(z) = (1) f (z) = 1 − 1 − 4z , . 2 1−z

At this stage, the forms above are merely compact descriptions of formal power series built from the elementary series

1 1 = 1 + y + y2 + · · · , (1 − y)1/2 = 1 − y − y 2 − · · · , 2 8 1 1 exp(y) = 1 + y + y2 + · · · , 1! 2! by standard composition rules. Accordingly, the coefficients of both GFs are known in explicit form: 1 1 (−1)n 1 2n − 2 n n , gn := [z ]g(z) = . − + ··· + f n := [z ] f (z) = n n−1 0! 1! n! (1 − y)−1

Stirling’s formula and the comparison with the alternating series giving exp(−1) provide, respectively, (2)

4n−1 fn ∼ √ , n→∞ π n 3

. gn = ∼ e−1 = 0.36787. n→∞

Our purpose now is to provide intuition on how such approximations could be derived without appealing to explicit forms. We thus examine, heuristically for the moment, the direct relationship between the asymptotic forms (2) and the structure of the corresponding generating functions in (1). Granted the growth estimates available for f n and gn , it is legitimate to substitute in the power series expansions of the GFs f (z) and g(z) any real or complex value of a small enough modulus, the upper bounds on modulus being ρ f = 1/4 (for f ) and ρg = 1 (for g). Figure IV.1 represents the graph of the resulting functions when such real values are assigned to z. The graphs are smooth, representing functions that are differentiable any number of times for z interior to the interval (−ρ, +ρ). However, at the right boundary point, smoothness stops: g(z) become infinite at z = 1, and so it even ceases to be finitely defined; f (z) does tend to the limit 21 as z → ( 41 )− , but its derivative becomes infinite there. Such special points at which smoothness stops are called singularities, a term that will acquire a precise meaning in the next sections. Observe also that, in spite of the series expressions being divergent outside the specified intervals, the functions f (z) and g(z) can be continued in certain regions: it

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0.5 3

0.4 0.3 2

0.2 0.1 –0.2

–0.1 0 –0.1 –0.2

1

0.1 z

0.2

–1

0

z

1

Figure IV.1. Left: the graph of the Catalan OGF, f (z), for z ∈ (− 41 , + 14 ); right: the graph of the derangement EGF, g(z), for z ∈ (−1, +1).

√ suffices to make use of the global expressions of Equation (1), with exp and being assigned their usual real-analytic interpretation. For instance: √ 1 e2 1− 5 , f (−1) = g(−2) = . 2 3 Such continuation properties, most notably to the complex realm, will prove essential in developing efficient methods for coefficient asymptotics. One may proceed similarly with complex numbers, starting with numbers whose modulus is less than the radius of convergence of the series defining the GF. Figure IV.2 displays the images of regular grids by f and g, as given by (1). This illustrates the fact that a regular grid is transformed into an orthogonal network of curves and more precisely that f and g preserve angles—this property corresponds to complex differentiability and is equivalent to analyticity to be introduced shortly. The singularity of f is clearly perceptible on the right of its diagram, since, at z = 1/4 (corresponding to f (z) = 1/2), the function f folds lines and divides angles by a factor of 2. The singularity of g at z = 1 is indirectly perceptible from the fact that g(z) → ∞ as z → 1 (the square grid had to be truncated at z = 0.75, since this book can only accommodate finite graphs). Let us now turn to coefficient asymptotics. As is expressed by (2), the coefficients f n and gn each belong to a general asymptotic type for coefficients of a function F, namely, (3)

[z n ]F(z) = An θ (n),

corresponding to an exponential growth factor An modulated by a tame factor θ (n), which is subexponential. Here, one has A = 4 for f n and A = 1 for gn ; also, √ 1 −1 3 θ (n) ∼ 4 ( π n ) for f n and θ (n) ∼ e−1 for gn . Clearly, A should be related to the radius of convergence of the series. We shall see that, invariably, for combinatorial generating functions, the exponential rate of growth is given by A = 1/ρ, where ρ is the first singularity encountered along the positive real axis (Theorem IV.6,

IV. 1. GENERATING FUNCTIONS AS ANALYTIC OBJECTS 0.3

0.3

0.2

0.2

0.1 0

227

0.1 0.1

0

0.2

–0.1

–0.1

–0.2

–0.2

–0.3

0.1 0.2 0.3 0.4 0.5 0.6 0.7

–0.3

0.4

0.6 0.4

0.2 0.2

0

0

0.1 0.2 0.3 0.4 0.5

1

1.2

1.4

1.6

1.8

–0.2 –0.4

–0.2

–0.6

–0.4

Figure IV.2. The images of regular grids by f (z) (left) and g(z) (right).

p. 240). In addition, under general complex analytic conditions, it will be established that θ (n) = O(1) is systematically associated to a simple pole of the generating function (Theorem IV.10, p. 258), while θ (n) = O(n −3/2 ) systematically arises from a singularity that is of the square-root type (Chapters VI and VII). We enunciate: First Principle of Coefficient Asymptotics. The location of a function’s singularities dictates the exponential growth (An ) of its coefficients. Second Principle of Coefficient Asymptotics. The nature of a function’s singularities determines the associate subexponential factor (θ (n)). Observe that the rescaling rule, [z n ]F(z) = ρ −n [z n ]F(ρz), enables one to normalize functions so that they are singular at 1. Then, various theorems, starting with Theorems IV.9 and IV.10, provide sufficient conditions under which the following fundamental implication is valid, (4)

h(z) ∼ σ (z)

H⇒

[z n ]h(z) ∼ [z n ]σ (z).

There h(z), whose coefficients are to be estimated, is a function singular at 1 and σ (z) is a local approximation near the singularity; usually σ is a much simpler function, typically like (1 − z)α logβ (1 − z) whose coefficients are comparatively easy to estimate (Chapter VI). The relation (4) expresses a mapping between asymptotic scales of functions near singularities and asymptotics scales of coefficients. Under suitable conditions, it then suffices to estimate a function locally at a few special points (singularities), in order to estimate its coefficients asymptotically.

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A succinct roadmap. Here is what now awaits the reader. Section IV. 2 serves to introduce basic notions of complex function theory. Singularities and exponential growth of coefficients are examined in Section IV. 3, which justifies the First Principle. Next, in Section IV. 4, we establish the computability of exponential growth rates for all the non-recursive structures that are specifiable. Section IV. 5 presents two important theorems that deal with rational and meromorphic functions and illustrate the Second Principle, in its simplest version (the subexponential factors are merely polynomials). Then, Section IV. 6 examines constructively ways to locate singularities and treats in detail the case of patterns in words. Finally, Section IV. 7 shows how functions only known through a functional equation may be accessible to complex asymptotic methods.

IV.1. Euler, the discrete, and the continuous. Eulers’s proof of the existence of infinitely many prime numbers illustrates in a striking manner the way analysis of generating functions can inform us on the discrete realm. Define, for real s > 1 the function ζ (s) :=

∞ X 1 , ns

n=1

known as the Riemann zeta function. The decomposition ( p ranges over the prime numbers 2, 3, 5, . . .) 1 1 1 1 1 1 ζ (s) = 1 + s + 2s + · · · 1 + s + 2s + · · · 1 + s + 2s + · · · · · · 2 3 5 2 3 5 (5) Y 1 −1 1− s = p p expresses precisely the fact that each integer has a unique decomposition as a product of primes. Analytically, the identity (5) is easily checked to be valid for all s > 1. Now suppose that there were only finitely many primes. Let s tend to 1+ in (5). Then, the left-hand side becomes Q infinite, while the right-hand side tends to the finite limit p (1 − 1/ p)−1 : a contradiction has been reached.

IV.2. Elementary transfers. Elementary series manipulation yield the following general result: Let h(z) be a power series with radius of convergence > 1 and assume that h(1) 6= 0; then one has √ h(z) h(1) h(1) 1 [z n ] ∼ h(1), [z n ]h(z) 1 − z ∼ − √ , [z n ]h(z) log ∼ . 3 1−z 1 − z n 2 πn

See our discussion on p. 434 and Bender’s survey [36] for many similar statements, of which this chapter and Chapter VI provide many far-reaching extensions.

IV.3. Asymptotics of generalized derangements. The EGF of permutations without cycles of length 1 and 2 satisfies (p. 123) 2

j (z) =

e−z−z /2 1−z

with

e−3/2 . z→1 1 − z

j (z) ∼

Analogy with derangements suggests that [z n ] j (z) ∼ e−3/2 . [For a proof, use Note IV.2 or n→∞

refer to Example IV.9 below, p. 261.] Here is a table of exact values of [z n ] j (z) (with relative error of the approximation by e−3/2 in parentheses): jn : error :

n=5 0.2 (10−1 )

n = 10 0.22317 (2 · 10−4 )

n = 20 0.2231301600 (3 · 10−10 )

n = 50 0.2231301601484298289332804707640122 (10−33 )

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229

The quality of the asymptotic approximation is extremely good, such a property being, as we shall see, invariably attached to polar singularities.

IV. 2. Analytic functions and meromorphic functions Analytic functions are a primary mathematical concept of asymptotic theory. They can be characterized in two essentially equivalent ways (see Subsection IV. 2.1): by means of convergent series expansions (`a la Cauchy and Weierstrass) and by differentiability properties (`a la Riemann). The first aspect is directly related to the use of generating functions for enumeration; the second one allows for a powerful abstract discussion of closure properties that usually requires little computation. Integral calculus with analytic functions (see Subsection IV. 2.2) assumes a shape radically different from that which prevails in the real domain: integrals become quintessentially independent of details of the integration contour—certainly the prime example of this fact is Cauchy’s famous residue theorem. Conceptually, this independence makes it possible to relate properties of a function at a point (e.g., the coefficients of its expansion at 0) to its properties at another far-away point (e.g., its residue at a pole). The presentation in this section and the next one constitutes an informal review of basic properties of analytic functions tuned to the needs of asymptotic analysis of counting sequences. The entry in Appendix B.2: Equivalent definitions of analyticity, p. 741, provides further information, in particular a proof of the Basic Equivalence Theorem, Theorem IV.1 below. For a detailed treatment, we refer the reader to one of the many excellent treatises on the subject, such as the books by Dieudonn´e [165], Henrici [329], Hille [334], Knopp [373], Titchmarsh [577], or Whittaker and Watson [604]. The reader previously unfamiliar with the theory of analytic functions should essentially be able to adopt Theorems IV.1 and IV.2 as “axioms” and start from here using basic definitions and a fair knowledge of elementary calculus. Figure IV.19 at the end of this chapter (p. 287) recapitulates the main results of relevance to Analytic Combinatorics. IV. 2.1. Basics. We shall consider functions defined in certain regions of the complex domain C. By a region is meant an open subset of the complex plane that is connected. Here are some examples:

simply connected domain

slit complex plane

indented disc

annulus.

Classical treatises teach us how to extend to the complex domain the standard functions of real analysis: polynomials are immediately extended as soon as complex addition and multiplication have been defined, while the exponential is definable by means of Euler’s formula. One has for instance z 2 = (x 2 − y 2 ) + 2i x y,

e z = e x cos y + ie x sin y,

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if z = x + i y, that is, x = ℜ(z) and y = ℑ(z) are the real and imaginary parts of z. Both functions are consequently defined over the whole complex plane C. The square-root and logarithm functions are conveniently described in polar coordinates: √ √ (6) z = ρeiθ/2 , log z = log ρ + iθ,

if z = ρeiθ . One can take the domain of validity of (6) to be the complex plane slit along the axis from 0 to −∞, that is, restrict θ to the open interval (−π, +π ), in which case the definitions above specify what is known as the principal determination. There √ is no way for instance to extend by continuity the definition of z in any domain √ √ containing 0 in its interior since, for a > √ 0 and z → −a, one has z → i a as √ z → −a from above, whereas z → −i a as z → −a from below. This situation is depicted here:

√ +i a √ −i a

0

√ The values of z as z varies along |z| = a.

√ a

The point z = 0, where several determinations “meet”, is accordingly known as a branch point. Analytic functions. First comes the main notion of an analytic function that arises from convergent series expansions and is of obvious relevance to generatingfunctionology. Definition IV.1. A function f (z) defined over a region is analytic at a point z 0 ∈ if, for z in some open disc centred at z 0 and contained in , it is representable by a convergent power series expansion X (7) f (z) = cn (z − z 0 )n . n≥0

A function is analytic in a region iff it is analytic at every point of . As derived from an elementary property of power series (Note IV.4), given a function f that is analytic at a point z 0 , there exists a disc (of possibly infinite radius) with the property that the series representing f (z) is convergent for z inside the disc and divergent for z outside the disc. The disc is called the disc of convergence and its radius is the radius of convergence of f (z) at z = z 0 , which will be denoted by Rconv ( f ; z 0 ). The radius of convergence of a power series conveys basic information regarding the rate at which its coefficients grow; see Subsection IV. 3.2 below for developments. It is also easy to prove by simple series rearrangement that if a function is analytic at z 0 , it is then analytic at all points interior to its disc of convergence (see Appendix B.2: Equivalent definitions of analyticity, p. 741). P

f n z n be a power series. n Define R as the supremum of all values of x ≥ 0 such that { f n x } is bounded. Then, for

IV.4. The disc of convergence of a power series. Let f (z) =

IV. 2. ANALYTIC AND MEROMORPHIC FUNCTIONS

231

|z| < R, the sequence f n z n tends geometrically to 0; hence f (z) is convergent. For |z| > R, the sequence f n z n is unbounded; hence f (z) is divergent. In short: a power series converges in the interior of a disc; it diverges in its exterior.

Consider for instance the function f (z) = 1/(1 − z) defined over C \ {1} in the usual way via complex division. It is analytic at 0 by virtue of the geometric series sum, X 1 = 1 · zn , 1−z n≥0

which converges in the disc |z| < 1. At a point z 0 6= 1, we may write 1 1 1 1 = = z−z 0 1−z 1 − z 0 − (z − z 0 ) 1 − z 0 1 − 1−z 0 (8) n+1 X 1 n (z − z 0 ) . = 1 − z0 n≥0

The last equation shows that f (z) is analytic in the disc centred at z 0 with radius |1 − z 0 |, that is, the interior of the circle centred at z 0 and passing through the point 1. In particular Rconv ( f, z 0 ) = |1 − z 0 | and f (z) is globally analytic in the punctured plane C \ {1}. The example of (1 − z)−1 illustrates the definition of analyticity. However, the series rearrangement approach that it uses might be difficult to carry out for more complicated functions. In other words, a more manageable approach to analyticity is called for. The differentiability properties developed now provide such an approach. Differentiable (holomorphic) functions. The next important notion is a geometric one based on differentiability. Definition IV.2. A function f (z) defined over a region is called complex-differentiable (also holomorphic) at z 0 if the limit, for complex δ, f (z 0 + δ) − f (z 0 ) lim δ→0 δ exists. (In particular, the limit is independent of the way δ tends to 0 in C.) This d limit is denoted as usual by f ′ (z 0 ), or dz f (z) , or ∂z f (z 0 ). A function is complexz0

differentiable in iff it is complex-differentiable at every z 0 ∈ .

From the definition, if f (z) is complex-differentiable at z 0 and f ′ (z 0 ) 6= 0, it acts locally as a linear transformation: f (z) − f (z 0 ) = f ′ (z 0 )(z − z 0 ) + o(z − z 0 )

(z → z 0 ).

Then, f (z) behaves in small regions almost like a similarity transformation (composed of a translation, a rotation, and a scaling). In particular, it preserves angles2 and infinitesimal squares get transformed into infinitesimal squares; see Figure IV.3 for a rendering. Further aspects of the local shape of an analytic function will be examined in Section VIII. 1, p. 543, in relation with the saddle-point method. 2A mapping of the plane that locally preserves angles is also called a conformal map. Section VIII. 1

(p. 543) presents further properties of the local “shape” of an analytic function.

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8 6 4 2 0

2

4

6

8

10

–2 –4 –6 –8

1.5 10

1

8

0.5

6

0

4

–0.5 –1

2 0

2 2

1 1

0 y –1

0x –1 –2–2

2

–1.5 2

1 1

0

0x y –1

–1 –2–2

Figure IV.3. Multiple views of an analytic function. The image of the domain = {z |ℜ(z)| < 2, |ℑ(z)| < 2} by f (z) = exp(z) + z + 2: [top] transformation of a square grid in by f ; [bottom] the modulus and argument of f (z).

√ For instance the function z, defined by (6) in the complex plane slit along the ray (−∞, 0), is complex-differentiable at any z 0 of the slit plane since √ √ √ z0 + δ − z0 √ 1 1 + δ/z 0 − 1 = lim z 0 = √ , (9) lim δ→0 δ→0 δ δ 2 z0 √ which extends the customary proof of real analysis. Similarly, 1 − z is complexdifferentiable in the complex plane slit along the ray (1, +∞). More generally, the usual proofs from real analysis carry over almost verbatim to the complex realm, to the effect that ′ f′ 1 ′ ′ ′ ′ ′ ′ = − 2 , ( f ◦ g)′ = ( f ′ ◦ g)g ′ . ( f + g) = f + g , ( f g) = f g + f g , f f

The notion of complex differentiability is thus much more manageable than the notion of analyticity. It follows from a well known theorem of Riemann (see for instance [329, vol. 1, p 143] and Appendix B.2: Equivalent definitions of analyticity, p. 741) that analyticity and complex differentiability are equivalent notions. Theorem IV.1 (Basic Equivalence Theorem). A function is analytic in a region if and only if it is complex-differentiable in .

The following are known facts (see p. 236 and Appendix B): (i) if a function is analytic (equivalently complex-differentiable) in , it admits (complex) derivatives of any order there—this property markedly differs from real analysis: complexdifferentiable, equivalently analytic, functions are all smooth; (ii) derivatives of a

IV. 2. ANALYTIC AND MEROMORPHIC FUNCTIONS

233

function may be obtained through term-by-term differentiation of the series representation of the function. Meromorphic functions. We finally introduce meromorphic3 functions that are mild extensions of the concept of analyticity (or holomorphy) and are essential to the theory. The quotient of two analytic functions f (z)/g(z) ceases to be analytic at a point a where g(a) = 0; however, a simple structure for quotients of analytic functions prevails. Definition IV.3. A function h(z) is meromorphic at z 0 iff, for z in a neighbourhood of z 0 with z 6= z 0 , it can be represented as f (z)/g(z), with f (z) and g(z) being analytic at z 0 . In that case, it admits near z 0 an expansion of the form X (10) h(z) = h n (z − z 0 )n . n≥−M

If h −M 6= 0 and M ≥ 1, then h(z) is said to have a pole of order M at z = z 0 . The coefficient h −1 is called the residue of h(z) at z = z 0 and is written as Res[h(z); z = z 0 ]. A function is meromorphic in a region iff it is meromorphic at every point of the region. IV. 2.2. Integrals and residues. A path in a region is described by its parameterization, which is a continuous function γ mapping [0, 1] into . Two paths γ , γ ′ in that have the same end points are said to be homotopic (in ) if one can be continuously deformed into the other while staying within as in the following examples:

homotopic paths:

A closed path is defined by the fact that its end points coincide: γ (0) = γ (1), and a path is simple if the mapping γ is one-to-one. A closed path is said to be a loop of if it can be continuously deformed within to a single point; in this case one also says that the path is homotopic to 0. In what follows paths are taken to be piecewise continuously differentiable and, by default, loops are oriented positively. Integrals along curves in the complex plane are defined in the usual way as curvilinear integrals of complex-valued functions. Explicitly: let f (x + i y) be a function 3“Holomorphic” and “meromorphic” are words coming from Greek, meaning, respectively, “of com-

plete form” and “of partial form”.

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IV. COMPLEX ANALYSIS, RATIONAL AND MEROMORPHIC ASYMPTOTICS

and γ be a path; then, Z f (z) dz

:=

γ

=

Z

1

Z0 1 0

f (γ (t))γ ′ (t) dt [AC − B D] dt + i

Z

1 0

[AD + BC] dt,

where f ◦ γ = A + i B and γ ′ = C + i D. However, integral calculus in the complex plane greatly differs from its form on the real line—in many ways, it is much simpler and much more powerful. One has: Theorem IV.2 (Null Integral R Property). Let f be analytic in and let λ be a simple loop of . Then, one has λ f = 0. Equivalently, integrals are largely independent of details of contours: for f analytic in , one has Z Z (11) f = f, γ

γ′

provided γ and γ ′ are homotopic (not necessarily closed) paths in . A proof of Theorem IV.2 is sketched in Appendix B.2: Equivalent definitions of analyticity, p. 741. Residues. The important Residue Theorem due to Cauchy relates global properties of a meromorphic function (its integral along closed curves) to purely local characteristics at designated points (its residues at poles). Theorem IV.3 (Cauchy’s residue theorem). Let h(z) be meromorphic in the region and let λ be a positively oriented simple loop in along which the function is analytic. Then Z X 1 h(z) dz = Res[h(z); z = s], 2iπ λ s where the sum is extended to all poles s of h(z) enclosed by λ.

Proof. (Sketch) To see it in the representative case where h(z) has only a pole at z = 0, observe by appealing to primitive functions that n+1 Z Z X dz z + h −1 , h(z) dz = hn n+1 λ λ z λ n≥−M n6=−1

where the bracket notation u(z) λ designates the variation of the function u(z) along the contour λ. This expression reduces to its last term, itself equal to 2iπ h −1 , as is checked by using integration along a circle (set z = r eiθ ). The computation extends by translation to the case of a unique pole at z = a. Next, in the case of multiple poles, we observe that the simple loop can only enclose finitely many poles (by compactness). The proof then follows from a simple decomposition of the interior domain of λ into cells, each containing only one pole. Here is an illustration in the case of three poles.

IV. 2. ANALYTIC AND MEROMORPHIC FUNCTIONS

235

(Contributions from internal edges cancel.)

Global (integral) to local (residues) connections. Here is a textbook example of a reduction from global to local properties of analytic functions. Define the integrals Z ∞ dx Im := , 1 + x 2m −∞ and consider specifically I1 . Elementary calculus teaches us that I1 = π since the antiderivative of the integrand is an arc tangent: Z ∞ dx = [arctan x]+∞ I1 = −∞ = π. 2 −∞ 1 + x

Here is an alternative, and in many ways more fruitful, derivation. In the light of the residue theorem, we consider the integral over the whole line as the limit of integrals over large intervals of the form [−R, +R], then complete the contour of integration by means of a large semi-circle in the upper half-plane, as shown below:

11 00 00 11 00 i 11

−R

0

+R

Let γ be the contour comprised of the interval and the semi-circle. Inside γ , the integrand has a pole at x = i, where 1 i 1 1 =− + ··· , ≡ (x + i)(x − i) 2x −i 1 + x2 so that its residue there is −i/2. By the residue theorem, the integral taken over γ is equal to 2iπ times the residue of the integrand at i. As R → ∞, the integral along the semi-circle vanishes (it is less than π R/(R 2 − 1) in modulus), while the integral along the real segment gives I1 in the limit. There results the relation giving I1 : i 1 ; x = i = (2iπ ) − I1 = 2iπ Res = π. 2 1 + x2

The evaluation of the integral in the framework of complex analysis rests solely upon the local expansion of the integrand at special points (here, the point i). This is a remarkable feature of the theory, one that confers it much simplicity, when compared with real analysis.

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IV. COMPLEX ANALYSIS, RATIONAL AND MEROMORPHIC ASYMPTOTICS

iπ ) so that α 2m = −1. Contour integration of IV.5. The general integral Im . Let α = exp( 2m the type used for I1 yields m X 1 2 j−1 , Im = 2iπ Res ; x = α 1 + x 2m j=1

while, for any β = α 2 j−1 with 1 ≤ j ≤ m, one has

As a consequence,

1 1 1 β 1 ∼ ≡− . 2m 2m−1 x −β 2m x − β x→β 2mβ 1+x

π iπ α + α 3 + · · · + α 2m−1 = π . m m sin 2m √ p √ √ In particular, I2 = π/ 2, I3 = 2π/3, I4 = π4 2 2 + 2, and π1 I5 , π1 I6 are expressible by radicals, but π1 I7 , π1 I9 are not. The special cases π1 I17 , π1 I257 are expressible by radicals. I2m = −

IV.6. Integrals of rational fractions. Generally, all integrals of rational functions taken over the whole real line are computable by residues. In particular, Z +∞ Z +∞ dx dx , K = Jm = m 2 m 2 2 2 (1 + x ) (1 + x )(2 + x 2 ) · · · (m 2 + x 2 ) −∞ −∞

can be explicitly evaluated.

Cauchy’s coefficient formula. Many function-theoretic consequences are derived from the residue theorem. For instance, if f is analytic in , z 0 ∈ , and λ is a simple loop of encircling z 0 , one has Z 1 dζ (12) f (z 0 ) = . f (ζ ) 2iπ λ ζ − z0 This follows directly since

Res [ f (ζ )/(ζ − z 0 ); ζ = z 0 ] = f (z 0 ). Then, by differentiation with respect to z 0 under the integral sign, one has similarly Z 1 dζ 1 (k) f (z 0 ) = f (ζ ) . (13) k! 2iπ λ (ζ − z 0 )k+1

The values of a function and its derivatives at a point can thus be obtained as values of integrals of the function away from that point. The world of analytic functions is a very friendly one in which to live: contrary to real analysis, a function is differentiable any number of times as soon as it is differentiable once. Also, Taylor’s formula invariably holds: as soon as f (z) is analytic at z 0 , one has 1 ′′ f (z 0 )(z − z 0 )2 + · · · , 2! with the representation being convergent in a disc centred at z 0 . [Proof: a verification from (12) and (13), or a series rearrangement as in Appendix B, p. 742.]

(14)

f (z) = f (z 0 ) + f ′ (z 0 )(z − z 0 ) +

A very important application of the residue theorem concerns coefficients of analytic functions.

IV. 2. ANALYTIC AND MEROMORPHIC FUNCTIONS

237

Theorem IV.4 (Cauchy’s Coefficient Formula). Let f (z) be analytic in a region containing 0 and let λ be a simple loop around 0 in that is positively oriented. Then, the coefficient [z n ] f (z) admits the integral representation Z 1 dz f n ≡ [z n ] f (z) = f (z) n+1 . 2iπ λ z Proof. This formula follows directly from the equalities Z h i 1 dz f (z) n+1 = Res f (z)z −n−1 ; z = 0 = [z n ] f (z), 2iπ λ z of which the first one follows from the residue theorem, and the second one from the identification of the residue at 0 as a coefficient. Analytically, the coefficient formula allows us to deduce information about the coefficients from the values of the function itself, using adequately chosen contours of integration. It thus opens the possibility of estimating the coefficients [z n ] f (z) in the expansion of f (z) near 0 by using information on f (z) away from 0. The rest of this chapter will precisely illustrate this process in the case of rational and meromorphic functions. Observe also that the residue theorem provides the simplest proof of the Lagrange inversion theorem (see Appendix A.6: Lagrange Inversion, p. 732) whose rˆole is central to tree enumerations, as we saw in Chapters I and II. The notes below explore some independent consequences of the residue theorem and the coefficient formula.

IV.7. Liouville’s Theorem. If a function f (z) is analytic in the whole of C and is of modulus bounded by an absolute constant, | f (z)| ≤ B, then it must be a constant. [By trivial bounds, upon integrating on a large circle, it is found that the Taylor coefficients at the origin of index ≥ 1 are all equal to 0.] Similarly, if f (z) is of at most polynomial growth, | f (z)| ≤ B (|z|+1)r , over the whole of C, then it must be a polynomial.

IV.8. Lindel¨of integrals. Let a(s) be analytic in ℜ(s) > 14 where it is assumed to satisfy a(s) = O(exp((π − δ)|s|)) for some δ with 0 < δ < π . Then, one has for | arg(z)| < δ, Z 1/2+i∞ ∞ X 1 π a(k)(−z)k = − a(s)z s ds, 2iπ 1/2−i∞ sin π s k=1

in the sense that the integral exists and provides the analytic continuation of the sum in | arg(z)| < δ. [Close the integration contour by a large semi-circle on the right and evaluate by residues.] Such integrals, sometimes called Lindel¨of integrals, provide representations for many functions whose Taylor coefficients are given by an explicit rule [268, 408].

IV.9. Continuation of polylogarithms. As a consequence of Lindel¨of’s representation, the generalized polylogarithm functions, X Liα,k (z) = n −α (log n)k z n n≥1

(α ∈ R,

k ∈ Z≥0 ),

are analytic in the complex plane C slit along (1+, ∞). (More properties are presented in Section VI. 8, p. 408; see also [223, 268].) For instance, one obtains in this way r Z ∞ 1 X π 1 +∞ log( 4 + t 2 ) dt = 0.22579 · · · = log , “ (−1)n log n ” = − 4 −∞ cosh(π t) 2 n=1

when the divergent series on the left is interpreted as Li0,1 (−1) = limz→−1+ Li0,1 (z).

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IV. COMPLEX ANALYSIS, RATIONAL AND MEROMORPHIC ASYMPTOTICS

IV.10. Magic duality. Let φ be a function initially defined over the non-negative integers but admitting a meromorphic extension over the whole of C. Under growth conditions in the style of Note IV.8, the function X F(z) := φ(n)(−z)n , n≥1

which is analytic at the origin, is such that, near positive infinity, X F(z) ∼ E(z) − φ(−n)(−z)−n , z→+∞

n≥1

for some elementary function E(z), which is a linear combination of terms of the form z α (log z)k . [Starting from the representation of Note IV.8, close the contour of integration by a large semicircle to the left.] In such cases, the function is said to satisfy the principle of magic duality—its expansion at 0 and ∞ are given by one and the same rule. Functions 1 , log(1 + z), exp(−z), Li2 (−z), Li3 (−z), 1+z satisfy a form of magic duality. Ramanujan [52] made a great use of this principle, which applies to a wide class of functions including hypergeometric ones; see Hardy’s insightful discussion [321, Ch XI].

IV.11. Euler–Maclaurin and Abel–Plana summations. Under simple conditions on the analytic function f , one has Plana’s (also known as Abel’s) complex variables version of the Euler– Maclaurin summation formula: Z ∞ Z ∞ ∞ X 1 f (i y) − f (−i y) dy. f (n) = f (0) + f (x) d x + 2 e2iπ y − 1 0 0 n=0

(See [330, p. 274] for a proof and validity conditions.)

IV.12. N¨orlund–Rice integrals. Let a(z) be analytic for ℜ(z) > k0 − 21 and of at most polynomial growth in this right half-plane. Then, with γ a simple loop around the interval [k0 , n], one has Z n X n! ds 1 n a(s) . (−1)n−k a(k) = 2iπ γ s(s − 1)(s − 2) · · · (s − n) k k=k0

If a(z) is meromorphic and suitably small in a larger region, then the integral can be estimated by residues. For instance, with n n X X n (−1)k n (−1)k , Tn = Sn = , k k k2 + 1 k k=1

k=1

it is found that Sn = − Hn (a harmonic number), while Tn oscillates boundedly as n → +∞. [This technique is a classical one in the calculus of finite differences, going back to N¨orlund [458]. In computer science it is known as the method of Rice’s integrals [256] and is used in the analysis of many algorithms and data structures including digital trees and radix sort [378, 564].]

IV. 3. Singularities and exponential growth of coefficients For a given function, a singularity can be informally defined as a point where the function ceases to be analytic. (Poles are the simplest type of singularity.) Singularities are, as we have stressed repeatedly, essential to coefficient asymptotics. This section presents the bases of a discussion within the framework of analytic function theory.

IV. 3. SINGULARITIES AND EXPONENTIAL GROWTH OF COEFFICIENTS

239

IV. 3.1. Singularities. Let f (z) be an analytic function defined over the interior region determined by a simple closed curve γ , and let z 0 be a point of the bounding curve γ . If there exists an analytic function f ⋆ (z) defined over some open set ⋆ containing z 0 and such that f ⋆ (z) = f (z) in ⋆ ∩ , one says that f is analytically continuable at z 0 and that f ⋆ is an immediate analytic continuation of f . Pictorially: γ

Analytic continuation:

Ω* Ω

z0 (f)

f ⋆ (z) = f (z) on ⋆ ∩ .

( f* )

Consider for instance the P quasi-inverse function, f (z) = 1/(1 − z). Its power series representation f (z) = n≥0 z n initially converges in |z| < 1. However, the calculation of (8), p. 231, shows that it is representable locally by a convergent series near any point z 0 6= 1. In particular, it is continuable at any point of the unit disc except 1. (Alternatively, one may appeal to complex-differentiability to verify directly that f (z), which is given by a “global” expression, is holomorphic, hence analytic, in the punctured plane C \ {1}.) In sharp contrast with real analysis, where a smooth function admits of uncountably many extensions, analytic continuation is essentially unique: if f ⋆ (in ⋆ ) and f ⋆⋆ (in ⋆⋆ ) continue f at z 0 , then one must have f ⋆ (z) = f ⋆⋆ (z) in the intersection ⋆ ∩ ⋆⋆ , which in particular includes a small disc around z 0 . Thus, the notion of immediate analytic continuation at a boundary point is intrinsic. The process can be iterated and we say that g is an analytic continuation4 of f along a path, even if the domains of definition of f and g do not overlap, provided a finite chain of intermediate function elements connects f and g. This notion is once more intrinsic—this is known as the principle of unicity of analytic continuation (Rudin [523, Ch. 16] provides a thorough discussion). An analytic function is then much like a hologram: as soon as it is specified in any tiny region, it is rigidly determined in any wider region to which it can be continued. Definition IV.4. Given a function f defined in the region interior to the simple closed curve γ , a point z 0 on the boundary (γ ) of the region is a singular point or a singularity5 if f is not analytically continuable at z 0 . Granted the intrinsic character of analytic continuation, we can usually dispense with a detailed description of the original domain and the curve γ . In simple terms, a function is singular at z 0 if it cannot be continued as an analytic function beyond z 0 . A point at which a function is analytic is also called by √ contrast a regular point. The two functions f (z) = 1/(1 − z) and g(z) = 1 − z may be taken as initially defined over the open unit disc by their power series representation. Then, as we already know, they can be analytically continued to larger regions, the punctured plane 4The collection of all function elements continuing a given function gives rise to the notion of Riemann surface, for which many good books exist, e.g., [201, 549]. We shall not need to appeal to this theory. 5For a detailed discussion, see [165, p. 229], [373, vol. 1, p. 82], or [577].

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IV. COMPLEX ANALYSIS, RATIONAL AND MEROMORPHIC ASYMPTOTICS

= C \ {1} for f [e.g., by the calculation of (8), p. 231] and the complex plane slit along (1, +∞) for g [e.g., by virtue of continuity and differentiability as in (9), p. 232]. But both are singular at 1: for f , this results (say) from the fact that f (z) → ∞ as z → 1; for g this is due to the branching character of the square-root. Figure IV.4 displays a few types of singularities that are traceable by the way they deform a regular grid near a boundary point. A converging power series is analytic inside its disc of convergence; in other words, it can have no singularity inside this disc. However, it must have at least one singularity on the boundary of the disc, as asserted by the theorem below. In addition, a classical theorem, called Pringsheim’s theorem, provides a refinement of this property in the case of functions with non-negative coefficients, which happens to include all counting generating functions. Theorem IV.5 (Boundary singularities). A function f (z) analytic at the origin, whose expansion at the origin has a finite radius of convergence R, necessarily has a singularity on the boundary of its disc of convergence, |z| = R. Proof. Consider the expansion (15)

f (z) =

X

fn zn ,

n≥0

assumed to have radius of convergence exactly R. We already know that there can be no singularity of f within the disc |z| < R. To prove that there is a singularity on |z| = R, suppose a contrario that f (z) is analytic in the disc |z| < ρ for some ρ satisfying ρ > R. By Cauchy’s coefficient formula (Theorem IV.4, p. 237), upon integrating along the circle of radius r = (R + ρ)/2, and by trivial bounds, it is seen that the coefficient [z n ] f (z) is O(r −n ). But then, the series expansion of f would have to converge in the disc of radius r > R, a contradiction. Pringsheim’s Theorem stated and proved now is a refinement of Theorem IV.5 that applies to all series having non-negative coefficients, in particular, generating functions. It is central to asymptotic enumeration, as the remainder of this section will amply demonstrate. Theorem IV.6 (Pringsheim’s Theorem). If f (z) is representable at the origin by a series expansion that has non-negative coefficients and radius of convergence R, then the point z = R is a singularity of f (z).

IV.13. Proof of Pringsheim’s Theorem. (See also [577, Sec. 7.21].) In a nutshell, the idea

of the proof is that if f has positive coefficients and is analytic at R, then its expansion slightly to the left of R has positive coefficients. Then, the power series of f would converge in a disc larger than the postulated disc of convergence—a clear contradiction. Suppose then a contrario that f (z) is analytic at R, implying that it is analytic in a disc of radius r centred at R. We choose a number h such that 0 < h < 31 r and consider the expansion of f (z) around z 0 = R − h: X (16) f (z) = gm (z − z 0 )m . m≥0

IV. 3. SINGULARITIES AND EXPONENTIAL GROWTH OF COEFFICIENTS

f 0 (z) = 1.5

1 1−z

241

f 1 (z) = e z/(1−z) 4

1

2

0.5

0

0.5

1

1.5

2

2.5

0

3

-0.5

0

4

2

6

8

10

-2

-1 -4 -1.5

√ f 2 (z) = − 1 − z

f 3 (z) = −(1 − z)3/2 2

0.6

0.4 1 0.2

-1.4

-1.2

-1

-0.8

-0.6

-0.4

-0.2

0

0

-2.5

-2

-1.5

-1

-0.5

0

0

0.5

-0.2 -1 -0.4

-0.6 -2

f 4 (z) = log 1.5

1 1−z

1

0.5

0

0

1

2

3

4

-0.5

-1

-1.5

Figure IV.4. The images of a grid on the unit square (with corners ±1±i) by various functions singular at z = 1 reflect the nature of the singularities involved. Singularities are apparent near the right of each diagram where small grid squares get folded or unfolded in various ways. (In the case of functions f 0 , f 1 , f 4 that become infinite at z = 1, the grid has been slightly truncated to the right.)

242

IV. COMPLEX ANALYSIS, RATIONAL AND MEROMORPHIC ASYMPTOTICS

By Taylor’s formula and the representability of f (z) together with its derivatives at z 0 by means of (15), we have X n gm = f n z 0n−m , m n≥0

and in particular, gm ≥ 0. Given the way h was chosen, the series (16) converges at z = R + h (so that z − z 0 = 2h) as illustrated by the following diagram:

R

r

2h

R+h R z0 = R − h

Consequently, one has f (R + h) =

X

m≥0

X n

n≥0

m

f n z 0m−n (2h)m .

This is a converging double sum of positive terms, so that the sum can be reorganized in any way we like. In particular, one has convergence of all the series involved in X n f (R + h) = f n (R − h)m−n (2h)m m m,n≥0 X = f n [(R − h) + (2h)]n =

n≥0 X

n≥0

f n (R + h)n .

This establishes the fact that f n = o((R + h)−n ), thereby reaching a contradiction with the assumption that the series representation of f has radius of convergence exactly R. Pringsheim’s theorem is proved.

Singularities of a function analytic at 0, which lie on the boundary of the disc of convergence, are called dominant singularities. Pringsheim’s theorem appreciably simplifies the search for dominant singularities of combinatorial generating functions since these have non-negative coefficients—it is sufficient to investigate analyticity along the positive real line and detect the first place at which it ceases to hold. Example IV.1. Some combinatorial singularities. The derangement and the surjection EGFs, D(z) =

e−z , 1−z

R(z) = (2 − e z )−1

are analytic, except for a simple pole at z = 1 in the case of D(z), and for points χk = log 2 + 2ikπ that are simple poles in the case of R(z). Thus the dominant singularities for derangements and surjections are at 1 and log 2, respectively.

IV. 3. SINGULARITIES AND EXPONENTIAL GROWTH OF COEFFICIENTS

243

√ It is known that Z cannot be unambiguously defined as an analytic function in a neighbourhood of Z = 0. As a consequence, the function √ 1 − 1 − 4z G(z) = , 2 which is the generating function of general Catalan trees, is an analytic function in regions that must exclude 1/4; for instance, one may take the complex plane slit along the ray (1/4, +∞). The OGF of Catalan numbers C(z) = G(z)/z is, as G(z), a priori analytic in the slit plane, except perhaps at z = 0, where it has the indeterminate form 0/0. However, after C(z) is extended by continuity to C(0) = 1, it becomes an analytic function at 0, where its Taylor series converges in |z| < 14 . In this case, we say that that C(z) has an apparent or removable singularity at 0. (See also Morera’s Theorem, Note B.6, p. 743.) Similarly, the EGF of cyclic permutations 1 L(z) = log 1−z is analytic in the complex plane slit along (1, +∞). A function having no singularity at a finite distance is called entire; its Taylor series then converges everywhere in the complex plane. The EGFs, 2 e z+z /2

and

z ee −1 ,

associated, respectively, with involutions and set partitions, are entire. . . . . . . . . . . . . . . . . . . .

IV. 3.2. The Exponential Growth Formula. We say that a number sequence {an } is of exponential order K n , which we abbreviate as (the symbol ⊲⊳ is a “bowtie”) an ⊲⊳ K n

iff

lim sup |an |1/n = K .

The relation “an ⊲⊳ K n ” reads as “an is of exponential order K n ”. It expresses both an upper bound and a lower bound, and one has, for any ǫ > 0: (i) |an | >i.o (K − ǫ)n ; that is to say, |an | exceeds (K − ǫ)n infinitely often (for infinitely many values of n); (ii) |an | 0, f n (R − ǫ)n → 0. In particular, | f n |(R − ǫ)n < 1 for all sufficiently large n, so that | f n |1/n < (R − ǫ)−1 “almost everywhere”. (ii) In theP other direction, for any ǫ > 0, | f n |(R + ǫ)n cannot be a bounded sequence, since otherwise, n | f n |(R + ǫ/2)n would be a convergent series. Thus, | f n |1/n > (R + ǫ)−1 “infinitely often”. A global approach to the determination of growth rates is desirable. This is made possible by Theorem IV.5, p. 240, as shown by the following statement. Theorem IV.7 (Exponential Growth Formula). If f (z) is analytic at 0 and R is the modulus of a singularity nearest to the origin in the sense that6 R := sup r ≥ 0 f is analytic in |z| < r ,

then the coefficient f n = [z n ] f (z) satisfies n 1 . f n ⊲⊳ R

For functions with non-negative coefficients, including all combinatorial generating functions, one can also adopt R := sup r ≥ 0 f is analytic at all points of 0 ≤ z < r .

Proof. Let R be as stated. We cannot have R < Rconv ( f ; 0) since a function is analytic everywhere in the interior of the disc of convergence of its series representation. We cannot have R > Rconv ( f ; 0) by the Boundary Singularity Theorem. Thus R = Rconv ( f ; 0). The statement then follows from (17). The adaptation to non-negative coefficients results from Pringsheim’s theorem. The exponential growth formula thus directly relates the exponential growth of coefficients of a function to the location of its singularities nearest to the origin. This is precisely expressed by the First Principle of Coefficient Asymptotics (p. 227), which, given its importance, we repeat here: First Principle of Coefficient Asymptotics. The location of a function’s singularities dictates the exponential growth (An ) of its coefficient.

Example IV.2. Exponential growth and combinatorial enumeration. Here are a few immediate applications of exponential bounds. Surjections. The function R(z) = (2 − e z )−1 6 One should think of the process defining R as follows: take discs of increasing radii r and stop as soon as a singularity is encountered on the boundary. (The √ dual process that would start from a large disc and restrict its radius is in general ill-defined—think of 1 − z.)

IV. 3. SINGULARITIES AND EXPONENTIAL GROWTH OF COEFFICIENTS

n

1 n log rn

1 ∗ n log rn

10 20 50 100

0.33385 0.35018 0.35998 0.36325

∞

0.36651 (log 1/ρ)

−0.22508 −0.18144 −0.154449 −0.145447

245

−0.13644 (log(1/ρ ∗ )

Figure IV.5. The growth rate of simple and double surjections.

is the EGF of surjections. The denominator is an entire function, so that singularities may only arise from its zeros, to be found at the points χk = log 2 + 2ikπ , k ∈ Z. The dominant singularity of R is then at ρ = χ0 = log 2. Thus, with rn = [z n ]R(z), n 1 rn ⊲⊳ . log 2 Similarly, if “double” surjections are considered (each value in the range of the surjection is taken at least twice), the corresponding EGF is 1 , 2 + z − ez with the counts starting as 1,0,1,1,7,21,141 (EIS A032032). The dominant singularity is at ∗ ρ ∗ defined as the positive root of equation eρ − ρ ∗ = 2, and the coefficient rn∗ satisfies: ∗ ∗ n rn ⊲⊳ (1/ρ ) Numerically, this gives R ∗ (z) =

rn ⊲⊳ 1.44269n

and

rn∗ ⊲⊳ 0.87245n ,

with the actual figures for the corresponding logarithms being given in Figure IV.5. These estimates constitute a weak form of a more precise result to be established later in this chapter (p. 260): If random surjections of size n are considered equally likely, the probability of a surjection being a double surjection is exponentially small. 2 Derangements. For the cases d1,n = [z n ]e−z (1−z)−1 and d2,n = [z n ]e−z−z /2 (1−z)−1 , we have, from the poles at z = 1,

d1,n ⊲⊳ 1n

and

d2,n ⊲⊳ 1n .

The implied upper bound is combinatorially trivial. The lower bound expresses that the probability for a random permutation to be a derangement is not exponentially small. For d1,n , we have already proved (p. 225) by an elementary argument the stronger result d1,n → e−1 ; in the case of d2,n , we shall establish later (p. 261) the precise asymptotic estimate d2,n → e−3/2 .

Unary–binary trees. The expression p 1 − z − 1 − 2z − 3z 2 U (z) = = z + z2 + 2 z3 + 4 z4 + 9 z5 + · · · , 2z represents the OGF of (plane unlabelled) unary–binary trees. From the equivalent form, √ 1 − z − (1 − 3z)(1 + z) U (z) = , 2z

246

IV. COMPLEX ANALYSIS, RATIONAL AND MEROMORPHIC ASYMPTOTICS

it follows that U (z) is analytic in the complex plane slit along ( 31 , +∞) and (−∞, −1) and is singular at z = −1 and z = 1/3 where it has branch points. The closest singularity to the origin being at 31 , one has Un ⊲⊳ 3n .

In this case, the stronger upper bound Un ≤ 3n results directly from the possibility of encoding such trees by words over a ternary alphabet using Łukasiewicz codes (Chapter I, p. 74). A complete asymptotic expansion will be obtained, as one of the first applications of singularity analysis, in Chapter VI (p. 396). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

IV.15. Coding theory bounds and singularities. Let C be a combinatorial class. We say that it can be encoded with f (n) bits if, for all sufficiently large values of n, elements of Cn can be encoded as words of f (n) bits. (An interesting example occurs in Note I.23, p. 53.) Assume that C has OGF C(z) with radius of convergence R satisfying 0 < R < 1. Then, for any ǫ, C can be encoded with (1 + ǫ)κn bits where κ = − log2 R, but C cannot be encoded with (1 − ǫ)κn bits. b with radius of convergence R satisfying 0 < R < ∞, then C Similarly, if C has EGF C(z) can be encoded with n log(n/e) + (1 + ǫ)κn bits where κ = − log2 R, but C cannot be encoded with n log(n/e) + (1 − ǫ)κn bits. Since the radius of convergence is determined by the distance to singularities nearest to the origin, we have the following interesting fact: singularities convey information on optimal codes. Saddle-point bounds. The exponential growth formula (Theorem IV.7, p. 244) can be supplemented by effective upper bounds which are very easy to derive and often turn out to be surprisingly accurate. We state: Proposition IV.1 (Saddle-point bounds). Let f (z) be analytic in the disc |z| < R with 0 < R ≤ ∞. Define M( f ; r ) for r ∈ (0, R) by M( f ; r ) := sup|z|=r | f (z)|. Then, one has, for any r in (0, R), the family of saddle-point upper bounds (18)

[z n ] f (z)

≤

M( f ; r ) rn

implying

[z n ] f (z) ≤

inf

r ∈(0,R)

M( f ; r ) . rn

If in addition f (z) has non-negative coefficients at 0, then (19)

[z n ] f (z)

≤

f (r ) rn

implying

[z n ] f (z) ≤

inf

r ∈(0,R)

f (r ) . rn

Proof. In the general case of (18), the first inequality results from trivial bounds applied to the Cauchy coefficient formula, when integration is performed along a circle: Z 1 dz [z n ] f (z) = f (z) n+1 . 2iπ |z|=r z It is consequently valid for any r smaller than the radius of convergence of f at 0. The second inequality in (18) plainly represents the best possible bound of this type. In the positive case of (19), the bounds can be viewed as a direct specialization of (18). (Alternatively, they can be obtained in a straightforward manner, since fn ≤

f n−1 f n+1 f0 + ··· + + f n + n+1 + · · · , n r r r

whenever the f k are non-negative.)

IV. 3. SINGULARITIES AND EXPONENTIAL GROWTH OF COEFFICIENTS

247

Note that the value s that provides the best bound in (19) can be determined by setting a derivative to zero, (20)

s

f ′ (s) = n. f (s)

Thanks to the universal character of the first bound, any approximate solution of this last equation will in fact provide a valid upper bound. We shall see in Chapter VIII another way to conceive of these bounds as a first step in an important method of asymptotic analysis; namely, the saddle-point method, which explains where the term “saddle-point bound” originates from (Theorem VIII.2, p. 547). For reasons that are well developed there, the bounds usually capture the actual asymptotic behaviour up to a polynomial factor. A typical instance is the weak form of Stirling’s formula, 1 en ≡ [z n ]e z ≤ n , n! n √ which only overestimates the true asymptotic value by a factor of 2π n.

IV.16. A suboptimal but easy saddle-point bound. Let f (z) be analytic in |z| < 1 with non-negative coefficients. Assume that f (x) ≤ (1 − x)−β for some β ≥ 0 and all x ∈ (0, 1). Then [z n ] f (z) = O(n β ).

(Better bounds of the form O(n β−1 ) are usually obtained by the method of singularity analysis expounded in Chapter VI.) Example IV.3. Combinatorial examples of saddle-point bounds. Here are applications to fragmented permutations, set partitions (Bell numbers), involutions, and integer partitions. Fragmented permutations. First, fragmented permutations (Chapter II, p. 125) are labelled structures defined by F = S ET(S EQ≥1 (Z)). The EGF is e z/(1−z) ; we claim that

√ 1 −1/2 ) 1 Fn ≡ [z n ]e z/(1−z) ≤ e2 n− 2 +O(n . n! Indeed, the minimizing radius of the saddle-point bound (19) is s such that n s 1 d − . − n log s = 0= 2 ds 1 − s s (1 − s) √ The equation is solved by s = (2n +1− 4n + 1)/(2n). One can either use this exact value and compute an asymptotic approximation of f (s)/s n , or adopt right away the approximate value √ s1 = 1 − 1/ n, which leads to simpler calculations. The estimate (21) results. It is off from the actual asymptotic value only by a factor of order n −3/4 (cf Example VIII.7, p. 562).

(21)

Bell numbers and set partitions. Another immediate application is an upper bound on z Bell numbers enumerating set partitions, S = S ET(S ET≥1 (Z)), with EGF ee −1 . According to (20), the best saddle-point bound is obtained for s such that ses = n. Thus, s 1 Sn ≤ ee −1−n log s where s : ses = n; n! additionally, one has s = log n − log log n + o(log log n). See Chapter VIII, p. 561 for the complete saddle-point analysis.

(22)

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IV. COMPLEX ANALYSIS, RATIONAL AND MEROMORPHIC ASYMPTOTICS

n 100 200 300 400 500

e In 0.106579 · 1085 0.231809 · 10195 0.383502 · 10316 0.869362 · 10444 0.425391 · 10578

In 0.240533 · 1083 0.367247 · 10193 0.494575 · 10314 0.968454 · 10442 0.423108 · 10576

−1

−2 0

1

2

3

Figure IV.6. A √ comparison of the exact number of involutions In to its approxiIn ) against mation e In = n!e n+n/2 n −n/2 : [left] a table; [right] a plot of log10 (In /e log10 n suggesting that the ratio satisfies In /e In ∼ K · n −1/2 , the slope of the curve being ≈ − 12 . Involutions. Involutions are specified by I = S ET(C YC1,2 (Z)) and have EGF I (z) = √ exp(z + 12 z 2 ). One determines, by choosing s = n as an approximate solution to (20): √

1 e n+n/2 . (23) In ≤ n! n n/2 (See Figure IV.6 for numerical data and Example VIII.5, p. 558 for a full analysis.) Similar bounds hold for permutations with all cycle lengths ≤ k and permutations σ such that σ k = I d. Integer partitions. The function

(24)

P(z) =

∞ Y

k=1

∞ ℓ X 1 1 z = exp ℓ 1 − zℓ 1 − zk ℓ=1

is the OGF of integer partitions, an unlabelled analogue of set partitions. Its radius of convergence is a priori bounded from above by 1, since the set P is infinite and the second form of P(z) shows that it is exactly equal to 1. Therefore Pn ⊲⊳ 1n . A finer upper bound results from the estimate (see also p. 576) r π2 t 1 (25) L(t) := log P(e−t ) ∼ + log − t + O(t 2 ), 6t 2π 24 which is obtained from Euler–Maclaurin summation or, better, from a Mellin analysis following Appendix B.7: Mellin transform, p. 762. Indeed, the Mellin transform of L is, by the harmonic sum rule, L ⋆ (s) = ζ (s)ζ (s + 1)Ŵ(s),

s ∈ h1, +∞i,

and the successive left-most poles at s = 1 (simple pole), s = 0 (double pole), and s = −1 (simple pole) translate into the asymptotic expansion (25). When z → 1− , we have ! 2 e−π /12 √ π2 , 1 − z exp (26) P(z) ∼ √ 6(1 − z) 2π √ from which we derive (choose s = D n as an approximate solution to (20)) √

Pn ≤ Cn −1/4 eπ 2n/3 ,

for some C > 0. This last bound is once more only off by a polynomial factor, as we shall prove when studying the saddle-point method (Proposition VIII.6, p. 578). . . . . . . . . . . . . . . .

IV. 4. CLOSURE PROPERTIES AND COMPUTABLE BOUNDS

249

IV.17. A natural boundary. One has P(r eiθ ) → ∞ as r → 1− , for any angle θ that is a rational multiple of 2π . The points e2iπ p/q being dense on the unit circle, the function P(z) admits the unit circle as a natural boundary; that is, it cannot be analytically continued beyond this circle. IV. 4. Closure properties and computable bounds Analytic functions are robust: they satisfy a rich set of closure properties. This fact makes possible the determination of exponential growth constants for coefficients of a wide range of classes of functions. Theorem IV.8 below expresses computability of growth rate for all specifications associated with iterative specifications. It is the first result of several that relate symbolic methods of Part A with analytic methods developed here. Closure properties of analytic functions. The functions analytic at a point z = a are closed under sum and product, and hence form a ring. If f (z) and g(z) are analytic at z = a, then so is their quotient f (z)/g(z) provided g(a) 6= 0. Meromorphic functions are furthermore closed under quotient and hence form a field. Such properties are proved most easily using complex-differentiability and extending the usual relations from real analysis, for instance, ( f + g)′ = f ′ + g ′ , ( f g)′ = f g ′ + f ′ g. Analytic functions are also closed under composition: if f (z) is analytic at z = a and g(w) is analytic at b = f (a), then g ◦ f (z) is analytic at z = a. Graphically: a

f

g b=f(a)

c=g(b)

The proof based on complex-differentiability closely mimicks the real case. Inverse functions exist conditionally: if f ′ (a) 6= 0, then f (z) is locally linear near a, hence invertible, so that there exists a g satisfying f ◦ g = g ◦ f = I d, where I d is the identity function, I d(z) ≡ z. The inverse function is itself locally linear, hence complex-differentiable, hence analytic. In short: the inverse of an analytic function f at a place where the derivative does not vanish is an analytic function. We shall return to this important property later in this chapter (Subsection IV. 7.1, p. 275), then put it to full use in Chapter VI (p. 402) and VII (p. 452) in order to derive strong asymptotic properties of simple varieties of trees.

IV.18. A Mean Value Theorem for analytic functions. Let f be analytic in and assume the existence of M := supz∈ | f ′ (z)|. Then, for all a, b in , one has | f (b) − f (a)| ≤ 2M|b − a|. (Hint: a simple consequence of the Mean Value Theorem applied to ℜ( f ), ℑ( f ).)

Subsection IV. 6.2, p. 269, for a proof based on integration.)

IV.19. The analytic inversion lemma. Let f be analytic on ∋ z 0 and satisfy f ′ (z 0 ) = 6 0. Then, there exists a small region 1 ⊆ containing z 0 and a C > 0 such that | f (z) − f (z ′ )| > C|z − z ′ |, for all z, z ′ ∈ 1 , z 6= z ′ . Consequently, f maps bijectively 1 on f (1 ). (See also One way to establish closure properties, as suggested above, is to deduce analyticity criteria from complex differentiability by way of the Basic Equivalence Theorem (Theorem IV.1, p. 232). An alternative approach, closer to the original notion of analyticity, can be based on a two-step process: (i) closure properties are shown to hold

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IV. COMPLEX ANALYSIS, RATIONAL AND MEROMORPHIC ASYMPTOTICS

true for formal power series; (ii) the resulting formal power series are proved to be locally convergent by means of suitable majorizations on their coefficients. This is the basis of the classical method of majorant series originating with Cauchy.

series technique. Given two power series, define f (z) g(z) if nIV.20. The majorant n

[z ] f (z) ≤ [z ]g(z) for all n ≥ 0. The following two conditions are equivalent: (i) f (z) is analytic in the disc |z| < ρ; (ii) for any r > ρ −1 there exists a c such that c f (z) . 1 − rz

If f, g are majorized by c/(1 −r z), d/(1 −r z), respectively, then f + g and f · g are majorized, f (z) + g(z)

c+d , 1 − rz

f (z) · g(z)

e , 1 − sz

for any s > r and for some e dependent on s. Similarly, the composition f ◦ g is majorized: c f ◦ g(z) . 1 − r (1 + d)z

Constructions for 1/ f and for the functional inverse of f can be similarly developed. See Cartan’s book [104] and van der Hoeven’s study [587] for a systematic treatment.

As a consequence of closure properties, for functions defined by analytic expressions, singularities can be determined inductively in an intuitively transparent manner. If Sing( f ) and Zero( f ) are, respectively, the set of singularities and zeros of the function f , then, due to closure properties of analytic functions, the following informally stated guidelines apply. Sing( f ± g) Sing( f × g) Sing( f /g) Sing(√ f ◦ g) Sing( f ) Sing(log( f )) Sing( f (−1) )

⊆ ⊆ ⊆ ⊆ ⊆ ⊆ ⊆

Sing( f ) ∪ Sing(g) Sing( f ) ∪ Sing(g) Sing( f ) ∪ Sing(g) ∪ Zero(g) Sing(g) ∪ g (−1) (Sing( f )) Sing( f ) ∪ Zero( f ) Sing( f ) ∪ Zero( f ) f (Sing( f )) ∪ f (Zero( f ′ )).

A mathematically rigorous treatment would require considering multivalued functions and Riemann surfaces, so that we do not state detailed validity conditions and keep for these formulae the status of useful heuristics. In fact, because of Pringsheim’s theorem, the search of dominant singularities of combinatorial generating function can normally avoid considering the complete multivalued structure of functions, since only some initial segment of the positive real half-line needs to be considered. This in turn implies a powerful and easy way of determining the exponential order of coefficients of a wide variety of generating functions, as we explain next. Computability of exponential growth constants. As defined in Chapters I and II, a combinatorial class is constructible or specifiable if it can be specified by a finite set of equations involving only the basic constructors. A specification is iterative or nonrecursive if in addition the dependency graph (p. 33) of the specification is acyclic. In that case, no recursion is involved and a single functional term (written with sums, products, sequences, sets, and cycles) describes the specification.

IV. 4. CLOSURE PROPERTIES AND COMPUTABLE BOUNDS

251

Our interest here is in effective computability issues. We recall that a real number α is computable iff there exists a program 5α , which, on input m, outputs a rational number αm guaranteed to be within ±10−m of α. We state:

Theorem IV.8 (Computability of growth). Let C be a constructible unlabelled class that admits an iterative specification in terms of (S EQ, PS ET, MS ET, C YC; +, ×) starting with (1, Z). Then, the radius of convergence ρC of the OGF C(z) of C is either +∞ or a (strictly) positive computable real number. Let D be a constructible labelled class that admits an iterative specification in terms of (S EQ, S ET, C YC; +, ⋆) starting with (1, Z). Then, the radius of convergence ρ D of the EGF D(z) of D is either +∞ or a (strictly) positive computable real number. Accordingly, if finite, the constants ρC , ρ D in the exponential growth estimates, n n 1 1 1 n n [z ]C(z) ≡ Cn ⊲⊳ , [z ]D(z) ≡ Dn ⊲⊳ , ρC n! ρD

are computable numbers. Proof. In both cases, the proof proceeds by induction on the structural specification of the class. For each class F, with generating function F(z), we associate a signature, which is an ordered pair hρ F , τ F i, where ρ F is the radius of convergence of F and τ F is the value of F at ρ F , precisely, τ F := lim F(x). x→ρ F−

(The value τ F is well defined as an element of R ∪ {+∞} since F, being a counting generating function, is necessarily increasing on (0, ρ F ).) Unlabelled case. An unlabelled class G is either finite, in which case its OGF G(z) is a polynomial, or infinite, in which case it diverges at z = 1, so that ρG ≤ 1. It is clearly decidable, given the specification, whether a class is finite or not: a necessary and sufficient condition for a class to be infinite is that one of the unary constructors (S EQ, MS ET, C YC) intervenes in the specification. We prove (by induction) the assertion of the theorem together with the stronger property that τ F = ∞ as soon as the class is infinite. First, the signatures of the neutral class 1 and the atomic class Z, with OGF 1 and z, are h+∞, 1i and h+∞, +∞i. Any non-constant polynomial which is the OGF of a finite set has the signature h+∞, +∞i. The assertion is thus easily verified in these cases. Next, let F = S EQ(G). The OGF G(z) must be non-constant and satisfy G(0) = 0, in order for the sequence construction to be properly defined. Thus, by the induction hypothesis, one has 0 < ρG ≤ +∞ and τG = +∞. Now, the function G being increasing and continuous along the positive axis, there must exist a value β such that 0 < β < ρG with G(β) = 1. For z ∈ (0, β), the quasi-inverse F(z) = (1 − G(z))−1 is well defined and analytic; as z approaches β from the left, F(z) increases unboundedly. Thus, the smallest singularity of F along the positive axis is at β, and by Pringsheim’s theorem, one has ρ F = β. The argument shows at the same time that τ F = +∞. There only remains to check that β is computable. The coefficients of

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IV. COMPLEX ANALYSIS, RATIONAL AND MEROMORPHIC ASYMPTOTICS

G form a computable sequence of integers, so that G(x), which can be well approximated via a truncated Taylor series, is an effectively computable number7 if x is itself a positive computable number less than ρG . Then, binary search provides an effective procedure for determining β. Next, we consider the multiset construction, F = MS ET(G), whose translation into OGFs necessitates the P´olya exponential of Chapter I (p. 34): 1 1 2 3 F(z) = Exp(G(z)) where Exp(h(z)) := exp h(z) + h(z ) + h(z ) + · · · . 2 3 Once more, the induction hypothesis is assumed for G. If G is a polynomial, then F is a rational function with poles at roots of unity only. Thus, ρ F = 1 and τ F = ∞ in that particular case. In the general case of F = MS ET(G) with G infinite, we start by fixing arbitrarily a number r such that 0 < r < ρG ≤ 1 and examine F(z) for z ∈ (0, r ). The expression for F rewrites as 1 1 G(z 2 ) + G(z 3 ) + · · · . Exp(G(z)) = e G(z) · exp 2 3 The first factor is analytic for z on (0, ρG ) since, the exponential function being entire, e G has the singularities of G. As to the second factor, one has G(0) = 0 (in order for the set construction to be well-defined), while G(x) is convex for x ∈ [0, r ] (since its second derivative is positive). Thus, there exists a positive constant K such that G(x) ≤ K x when x ∈ [0, r ]. Then, the series 12 G(z 2 ) + 13 G(z 3 ) + · · · has its terms dominated by those of the convergent series K 2 K 3 r + r + · · · = K log(1 − r )−1 − K r. 2 3 By a well-known theorem of analytic function theory, a uniformly convergent sum of analytic functions is itself analytic; consequently, 21 G(z 2 ) + 13 G(z 3 ) + · · · is analytic at all z of (0, r ). Analyticity is then preserved by the exponential, so that F(z), being analytic at z ∈ (0, r ) for any r < ρG has a radius of convergence that satisfies ρ F ≥ ρG . On the other hand, since F(z) dominates termwise G(z), one has ρ F ≤ ρG . Thus finally one has ρ F = ρG . Also, τG = +∞ implies τ F = +∞. A parallel discussion covers the case of the powerset construction (PS ET) whose associated functional Exp is a minor modification of the P´olya exponential Exp. The cycle construction can be treated by similar arguments based on consideration of “P´olya’s logarithm” as F = C YC(G) corresponds to F(z) = Log

1 , 1 − G(z)

where

Log h(z) = log h(z) +

1 log h(z 2 ) + · · · . 2

In order to conclude with the unlabelled case, it only remains to discuss the binary constructors +, ×, which give rise to F = G + H , F = G · H . It is easily verified that 7 The present argument only establishes non-constructively the existence of a program, based on the

fact that truncated Taylor series converge geometrically fast at an interior point of their disc of convergence. Making explict this program and the involved parameters from the specification itself however represents a much harder problem (that of “uniformity” with respect to specifications) that is not addressed here.

IV. 4. CLOSURE PROPERTIES AND COMPUTABLE BOUNDS

253

ρ F = min(ρG , ρ H ). Computability is granted since the minimum of two computable numbers is computable. That τ F = +∞ in each case is immediate.

Labelled case. The labelled case is covered by the same type of argument as above, the discussion being even simpler, since the ordinary exponential and logarithm replace the P´olya operators Exp and Log. It is still a fact that all the EGFs of infinite non-recursive classes are infinite at their dominant positive singularity, though the radii of convergence can now be of any magnitude (compared to 1).

IV.21. Restricted constructions. This is an exercise in induction. Theorem IV.8 is stated for specifications involving the basic constructors. Show that the conclusion still holds if the corresponding restricted constructions (K=r , Kr , with K being any of the basic constructors) are also allowed. IV.22. Syntactically decidable properties. For unlabelled classes F , the property ρ F = 1 is decidable. For labelled and unlabelled classes, the property ρ F = +∞ is decidable.

IV.23. P´olya–Carlson and a curious property of OGFs. Here is a statement first conjectured by P´olya, then proved by Carlson in 1921 (see [164, p. 323]): If a function is represented by a power series with integer coefficients that converges inside the unit disc, then either it is a rational function or it admits the unit circle as a natural boundary. This theorem applies in particular to the OGF of any combinatorial class.

IV.24. Trees are recursive structures only! General and binary trees cannot receive an iterative specification since their OGFs assume a finite value at their Pringsheim singularity. [The same is true of most simple families of trees; cf Proposition VI.6, p. 404]. IV.25. Non-constructibility of permutations and graphs. The class P of all P permutations

n cannot be specified as a constructible unlabelled class since the OGF P(z) = n n!z has radius of convergence 0. (It is of course constructible as a labelled class.) Graphs, whether labelled or unlabelled, are too numerous to form a constructible class.

Theorem IV.8 establishes a link between analytic combinatorics, computability theory, and symbolic manipulation systems. It is based on an article of Flajolet, Salvy, and Zimmermann [255] devoted to such computability issues in exact and asymptotic enumeration. Recursive specifications are not discussed now since they tend to give rise to branch points, themselves amenable to singularity analysis techniques to be fully developed in Chapters VI and VII. The inductive process, implied by the proof of Theorem IV.8, that decorates a specification with the radius of convergence of each of its subexpressions, provides a practical basis for determining the exponential growth rate of counts associated to a non-recursive specification. Example IV.4. Combinatorial trains. This purposely artificial example from [219] (see Figure IV.7) serves to illustrate the scope of Theorem IV.8 and demonstrate its inner mechanisms at work. Define the class of all labelled trains by the following specification, Tr = Wa ⋆ S EQ(Wa ⋆ S ET(Pa)), Wa = S EQ≥1 (Pℓ), (27) Pℓ = Z ⋆ Z ⋆ (1 + C YC(Z)), Pa = C YC(Z) ⋆ C YC(Z).

In figurative terms, a train (T r ) is composed of a first wagon (Wa) to which is appended a sequence of passenger wagons, each of the latter capable of containing a set of passengers (Pa). A wagon is itself composed of “planks” (Pℓ) conventionally identified by their two end points (Z ⋆ Z) and to which a circular wheel (C YC(Z)) may optionally be attached. A passenger is

254

IV. COMPLEX ANALYSIS, RATIONAL AND MEROMORPHIC ASYMPTOTICS

Tr

0.48512

⋆ 0.48512 Wa

0.68245

Seq

0.68245 1

Seq≥1

⋆ 0.68245

⋆

Z

(Wa)

Z

Set

∞

1 ∞

⋆

+ 1

∞

Cyc

Cyc

Cyc

Z

Z

Z

1 1

1 ∞

1

1

∞

∞

Figure IV.7. The inductive determination of the radius of convergence of the EGF of trains: (left) a hierarchical view of the specification of T r ; (right) the corresponding radii of convergence for each subspecification.

composed of a head and a belly that are each circular arrangements of atoms. Here is a depiction of a random train:

The translation into a set of EGF equations is immediate and a symbolic manipulation system readily provides the form of the EGF of trains as

log((1−z)−1 ) z 2 1 + log((1 − z)−1 ) e 1 − T r (z) = 1 − z 2 1 + log((1 − z)−1 ) 1 − z 2 1 + log((1 − z)−1 )

z 2 1 + log((1 − z)−1 )

2 −1

together with the expansion T r (z) = 2

,

z2 z3 z4 z5 z6 z7 +6 + 60 + 520 + 6660 + 93408 + ··· . 2! 3! 4! 5! 6! 7!

The specification (27) has a hierarchical structure, as suggested by the top representation of Figure IV.7, and this structure is itself directly reflected by the form of the expression tree of the GF T r (z). Then, each node in the expression tree of T r (z) can be tagged with the corresponding value of the radius of convergence. This is done according to the principles of Theorem IV.8;

IV. 5. RATIONAL AND MEROMORPHIC FUNCTIONS

255

see the right diagram of Figure IV.7. For instance, the quantity 0.68245 associated to W a(z) is given by the sequence rule and is determined as the smallest positive solution of the equation z 2 1 − log(1 − z)−1 = 1.

The tagging process works upwards till the root of the tree is reached; here the radius of con. vergence of T r is determined to be ρ = 0.48512 · · · , a quantity that happens to coincide with 49 50 the ratio [z ]T r (z)/[z ]T r (z) to more than 15 decimal places. . . . . . . . . . . . . . . . . . . . . . . . .

IV. 5. Rational and meromorphic functions The last section has fully justified the First Principle of Coefficient Asymptotics leading to the exponential growth formula f n ⊲⊳ An for the coefficients of an analytic function f (z). Indeed, as we saw, one has A = 1/ρ, where ρ equals both the radius of convergence of the series representing f and the distance of the origin to the dominant, i.e., closest, singularities. We are going to start examining here the Second Principle, already given on p. 227 and relative to the form f n = An θ (n), with θ (n) the subexponential factor: Second Principle of Coefficient Asymptotics. The nature of a function’s singularities determines the associate subexponential factor (θ (n)). In this section, we develop a complete theory in the case of rational functions (that is, quotients of polynomials) and, more generally, meromorphic functions. The net result is that, for such functions, the subexponential factors are essentially polynomials: Polar singularities

;

subexponential factors θ (n) of polynomial growth.

A distinguishing feature is the extremely good quality of the asymptotic approximations obtained; for naturally occurring combinatorial problems, 15 digits of accuracy is not uncommon in coefficients of index as low as 50 (see Figure IV.8, p. 260 below for a striking example). IV. 5.1. Rational functions. A function f (z) is a rational function iff it is of the form f (z) = N (z)/D(z), with N (z) and D(z) being polynomials, which we may, without loss of generality, assume to be relatively prime. For rational functions that are analytic at the origin (e.g., generating functions), we have D(0) 6= 0. Sequences { f n }n≥0 that are coefficients of rational functions satisfy linear recurrence relations with constant coefficients. This fact is easy to establish: compute [z n ] f (z) · D(z); then, with D(z) = d0 + d1 z + · · · + dm z m , one has, for all n > deg(N (z)), m X d j f n− j = 0. j=0

The main theorem we prove now provides an exact finite expression for coefficients of f (z) in terms of the poles of f (z). Individual terms in these expressions are sometimes called exponential–polynomials.

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IV. COMPLEX ANALYSIS, RATIONAL AND MEROMORPHIC ASYMPTOTICS

Theorem IV.9 (Expansion of rational functions). If f (z) is a rational function that is analytic at zero and has poles at points α1 , α2 , . . . , αm , then its coefficients are a sum of exponential–polynomials: there exist m polynomials {5 j (x)}mj=1 such that, for n larger than some fixed n 0 , (28)

f n ≡ [z n ] f (z) =

m X

5 j (n)α −n j .

j=1

Furthermore the degree of 5 j is equal to the order of the pole of f at α j minus one. Proof. Since f (z) is rational it admits a partial fraction expansion. To wit: X cα,r f (z) = Q(z) + , (z − α)r (α,r )

where Q(z) is a polynomial of degree n 0 := deg(N ) − deg(D) if f = N /D. Here α ranges over the poles of f (z) and r is bounded from above by the multiplicity of α as a pole of f . Coefficient extraction in this expression results from Newton’s expansion, 1 (−1)r n 1 (−1)r n + r − 1 −n α . [z n ] = [z ] = r r −1 (z − α)r αr αr 1 − αz

The binomial coefficient is a polynomial of degree r − 1 in n, and collecting terms associated with a given α yields the statement of the theorem. Notice that the expansion (28) is also an asymptotic expansion in disguise: when grouping terms according to the α’s of increasing modulus, each group appears to be exponentially smaller than the previous one. In particular, if there is a unique dominant pole, |α1 | < |α2 | ≤ |α3 | ≤ · · · , then f n ∼ α1−n 51 (n),

and the error term is exponentially small as it is O(α2−n nr ) for some r . A classical instance is the OGF of Fibonacci numbers, z , F(z) = 1 − z − z2 √ √ −1 + 5 . −1 − 5 . with poles at = 0.61803 and = −1.61803, so that 2 2 1 1 ϕn 1 [z n ]F(z) ≡ Fn = √ ϕ n − √ ϕ¯ n = √ + O( n ), ϕ 5 5 5 √ with ϕ = (1 + 5)/2 the golden ratio, and ϕ¯ its conjugate.

IV.26. A simple exercise. Let f (z) be as in Theorem IV.9, assuming additionally a single dominant pole α1 , with multiplicity r . Then, by inspection of the proof of Theorem IV.9: 1 C with C = lim (z − α1 )r f (z). α1−n+r nr −1 1 + O fn = z→α1 (r − 1)! n

This is certainly the most direct illustration of the Second Principle: under the assumptions, a one-term asymptotic expansion of the function at its dominant singularity suffices to determine the asymptotic form of the coefficients.

IV. 5. RATIONAL AND MEROMORPHIC FUNCTIONS

257

Example IV.5. Qualitative analysis of a rational function. This is an artificial example designed to demonstrate that all the details of the full decomposition are usually not required. The rational function 1 f (z) = 2 3 2 (1 − z ) (1 − z 2 )3 (1 − z2 )

2 2iπ/3 a cubic root of unity), has a pole of order 5 at z = 1, poles of order 2 at z = ω, √ω (ω = e a pole of order 3 at z = −1, and simple poles at z = ± 2. Therefore,

f n = P1 (n) + P2 (n)ω−n + P3 (n)ω−2n + P4 (n)(−1)n +

+P5 (n)2−n/2 + P6 (n)(−1)n 2−n/2 where the degrees of P1 , . . . , P6 are 4, 1, 1, 2, 0, 0. For an asymptotic equivalent of f n , only the poles at roots of unity need to be considered since they correspond to the fastest exponential growth; in addition, only z = 1 needs to be considered for first-order asymptotics; finally, at z = 1, only the term of fastest growth needs to be taken into account. In this way, we find the correspondence 1 1 n4 1 n+4 H⇒ f ∼ f (z) ∼ ∼ . n 864 4 32 · 23 · ( 21 ) (1 − z)5 32 · 23 · ( 12 ) The way the analysis can be developed without computing details of partial fraction expansion is typical. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Theorem IV.9 applies to any specification leading to a GF that is a rational function8. Combined with the qualitative approach to rational coefficient asymptotics, it gives access to a large number of effective asymptotic estimates for combinatorial counting sequences. Example IV.6. Asymptotics of denumerants. Denumerants are integer partitions with summands restricted to be from a fixed finite set (Chapter I, p. 43). We let P T be the class relative to set T ⊂ Z>0 , with the known OGF, Y 1 P T (z) = . 1 − zω ω∈T

Without loss of generality, we assume that gcd(T ) = 1; that is, the coin denomination are not all multiples of a number d > 1. A particular case is the one of integer partitions whose summands are in {1, 2, . . . , r }, P {1,...,r } (z) =

r Y

m=1

1 . 1 − zm

The GF has all its poles being roots of unity. At z = 1, the order of the pole is r , and one has 1 1 P {1,...,r } (z) ∼ , r ! (1 − z)r as z → 1. Other poles have strictly smaller multiplicity. For instance the multiplicity of z = −1 is equal to the number of factors (1 − z 2 j )−1 in P {1,...,r } , which is the same as the number of coin denominations that are even; this last number is at most r − 1 since, by the gcd assumption gcd(T ) = 1, at least one is odd. Similarly, a primitive qth root of unity is found to have 8 In Part A, we have been occasionally led to discuss coefficients of some simple enough rational functions, thereby anticipating the statement of the theorem: see for instance the discussion of parts in compositions (p. 168) and of records in sequences (p. 190).

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IV. COMPLEX ANALYSIS, RATIONAL AND MEROMORPHIC ASYMPTOTICS

multiplicity at most r − 1. It follows that the pole z = 1 contributes a term of the form nr −1 to the coefficient of index n, while each of the other poles contributes a term of order at most nr −2 . We thus find 1 {1,...,r } . ∼ cr nr −1 with cr = Pn r !(r − 1)!

The same argument provides the asymptotic form of PnT , since, to first order asymptotics, only the pole at z = 1 counts. Proposition IV.2. Let T be a finite set of integers without a common divisor (gcd(T ) = 1). The number of partitions with summands restricted to T satisfies PnT ∼

1 nr −1 , τ (r − 1)!

with τ :=

Y

ω,

r := card(T ).

ω∈T

For instance, in a strange country that would have pennies (1 cent), nickels (5 cents), dimes (10 cents), and quarters (25 cents), the number of ways to make change for a total of n cents is [z n ]

1 (1 − z)(1 − z 5 )(1 − z 10 )(1 − z 25 )

∼

1 n3 n3 ≡ , 1 · 5 · 10 · 25 3! 7500

asymptotically. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

IV. 5.2. Meromorphic functions. An expansion similar to that of Theorem IV.9 (p. 256) holds true for coefficients of a much larger class; namely, meromorphic functions. Theorem IV.10 (Expansion of meromorphic functions). Let f (z) be a function meromorphic at all points of the closed disc |z| ≤ R, with poles at points α1 , α2 , . . . , αm . Assume that f (z) is analytic at all points of |z| = R and at z = 0. Then there exist m polynomials {5 j (x)}mj=1 such that: (29)

f n ≡ [z n ] f (z) =

m X j=1

5 j (n)α −n + O(R −n ). j

Furthermore the degree of 5 j is equal to the order of the pole of f at α j minus one. Proof. We offer two different proofs, one based on subtracted singularities, the other one based on contour integration. (i) Subtracted singularities. Around any pole α, f (z) can be expanded locally: X f (z) = (30) cα,k (z − α)k k≥−M

(31)

=

Sα (z) + Hα (z)

where the “singular part” Sα (z) is obtained by collecting all the terms with index in [−M . . − 1] (that is, forming Sα (z) = Nα (z)/(z − α) M with Nα (z) a polynomial P of degree less than M) and Hα (z) is analytic at α. Thus setting S(z) := j Sα j (z), we observe that f (z) − S(z) is analytic for |z| ≤ R. In other words, by collecting the singular parts of the expansions and subtracting them, we have “removed” the singularities of f (z), whence the name of method of subtracted singularities sometimes given to the method [329, vol. 2, p. 448].

IV. 5. RATIONAL AND MEROMORPHIC FUNCTIONS

259

Taking coefficients, we get: [z n ] f (z) = [z n ]S(z) + [z n ]( f (z) − S(z)). The coefficient of [z n ] in the rational function S(z) is obtained from Theorem IV.9. It suffices to prove that the coefficient of z n in f (z) − S(z), a function analytic for |z| ≤ R, is O(R −n ). This fact follows from trivial bounds applied to Cauchy’s integral formula with the contour of integration being λ = {z : |z| = R}, as in the proof of Proposition IV.1, p 246 (saddle-point bounds): Z n dz 1 O(1) [z ]( f (z) − S(z)) = 1 2π R. ( f (z) − S(z)) n+1 ≤ 2π 2π z R n+1 |z|=R

(ii) Contour integration. There is another line of proof for Theorem IV.10 which we briefly sketch as it provides an insight which is useful for applications to other types of singularities treated in Chapter VI. It consists in using Cauchy’s coefficient formula and “pushing” the contour of integration past singularities. In other words, one computes directly the integral Z dz 1 f (z) n+1 In = 2iπ |z|=R z

by residues. There is a pole at z = 0 with residue f n and poles at the α j with residues corresponding to the terms in the expansion stated in Theorem IV.10; for instance, if f (z) ∼ c/(z − a) as z → a, then c c −n−1 −n−1 Res( f (z)z ; z = a) = Res z ; z = a = n+1 . (z − a) a Finally, by the same trivial bounds as before, In is O(R −n ).

IV.27. Effective error bounds. The error term O(R −n ) in (29), call it εn , satisfies |εn | ≤ R −n · sup | f (z)|. |z|=R

This results immediately from the second proof. This bound may be useful, even in the case of rational functions to which it is clearly applicable.

As a consequence of Theorem IV.10, all GFs whose dominant singularities are poles can be easily analysed. Prime candidates from Part A are specifications that are “driven” by a sequence construction, since the translation of sequences involves a quasi-inverse, itself conducive to polar singularities. This covers in particular surjections, alignments, derangements, and constrained compositions, which we treat now. Example IV.7. Surjections. These are defined as sequences of sets (R = S EQ(S ET≥1 (Z))) with EGF R(z) = (2 − e z )−1 (see p. 106). We have already determined the poles in Exam. ple IV.2 (p. 244), the one of smallest modulus being at log 2 = 0.69314. At this dominant 1 −1 pole, one finds R(z) ∼ − 2 (z − log 2) . This implies an approximation for the number of surjections: n+1 1 n! n · . Rn ≡ n![z ]R(z) ∼ ξ(n), with ξ(n) := 2 log 2

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3 75 4683 545835 102247563 28091567595 10641342970443 5315654681981355 3385534663256845323 2677687796244384203115 2574844419803190384544203 2958279121074145472650648875 4002225759844168492486127539083 6297562064950066033518373935334635 11403568794011880483742464196184901963 23545154085734896649184490637144855476395

3 75 4683 545835 102247563 28091567595 10641342970443 5315654681981355 338553466325684532 6 2677687796244384203 088 2574844419803190384544 450 295827912107414547265064 6597 40022257598441684924861275 55859 6297562064950066033518373935 416161 1140356879401188048374246419617 4527074 2354515408573489664918449063714 5314147690

Figure IV.8. The surjection numbers pyramid: for n = 2, 4, . . . , 32, the exact values of the numbers Rn (left) compared to the approximation ⌈ξ(n)⌋ with discrepant digits in boldface (right).

Figure IV.8 gives, for n = 2, 4, . . . , 32, a table of the values of the surjection numbers (left) compared with the asymptotic approximation rounded9 to the nearest integer, ⌈ξ(n)⌋: It is piquant to see that ⌈ξ(n)⌋ provides the exact value of Rn for all values of n = 1, . . . , 15, and it starts losing one digit for n = 17, after which point a few “wrong” digits gradually appear, but in very limited number; see Figure IV.8. (A similar situation prevails for tangent numbers discussed in our Invitation, p. 5.) The explanation of such a faithful asymptotic representation owes to the fact that the error terms provided by meromorphic asymptotics are exponentially small. In effect, there is no other pole in |z| ≤ 6, the next ones being at log 2 ± 2iπ with modulus of about 6.32. Thus, for rn = [z n ]R(z), there holds n+1 Rn 1 1 (32) ∼ · + O(6−n ). n! 2 log 2 For the double surjection problem, R ∗ (z) = (2 + z − e z ), we get similarly 1 [z n ]R ∗ (z) ∼ ρ ∗ (ρ ∗ )−n−1 , e −1 ∗

with ρ ∗ = 1.14619 the smallest positive root of eρ − ρ ∗ = 2. . . . . . . . . . . . . . . . . . . . . . . . . .

It is worth reflecting on this example as it is representative of a “production chain” based on the two successive implications which are characteristic of Part A and Part B of the book: 1 R = S EQ(S ET≥1 (Z)) H⇒ R(z) = 2 − ez 1 1 1 1 R(z) ∼ − −→ Rn ∼ (log 2)−n−1 . z→log 2 2 (z − log 2) n! 2 9The notation ⌈x⌋ represents x rounded to the nearest integer: ⌈x⌋ := ⌊x + 1 ⌋. 2

IV. 5. RATIONAL AND MEROMORPHIC FUNCTIONS

261

The first implication (written “H⇒”, as usual) is provided automatically by the symbolic method. The second one (written here “−→”) is a direct translation from the expansion of the GF at its dominant singularity to the asymptotic form of coefficients; it is valid conditionally upon complex analytic conditions, here those of Theorem IV.10. Example IV.8. Alignments. These are sequences of cycles (O = S EQ(C YC(Z)), p. 119) with EGF 1 O(z) = . 1 1 − log 1−z

There is a singularity when log(1 − z)−1 = 1, which is at ρ = 1 − e−1 and which arises before z = 1, where the logarithm becomes singular. Then, the computation of the asymptotic form of [z n ]O(z) only requires a local expansion near ρ, O(z) ∼

−e−1 z − 1 + e−1

[z n ]O(z) ∼

−→

e−1 , (1 − e−1 )n+1

and the coefficient estimates result from Theorem IV.10. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

IV.28. Some “supernecklaces”. One estimates [z n ] log

1 1 1 − log 1−z

!

∼

1 (1 − e−1 )−n , n

where the EGF enumerates labelled cycles of cycles (supernecklaces, p. 125). [Hint: Take derivatives.] Example IV.9. Generalized derangements. The probability that the shortest cycle in a random permutation of size n has length larger than k is [z n ]D (k) (z),

where

D (k) (z) =

1 − z − z 2 −···− z k k , e 1 2 1−z

as results from the specification D(k) = S ET(C YC>k (Z)). For any fixed k, one has (easily) D (k) (z) ∼ e− Hk /(1 − z) as z → 1, with 1 being a simple pole. Accordingly the coefficients [z n ]D (k) (z) tend to e− Hk as n → ∞. In summary, due to meromorphy, we have the characteristic implication e − Hk −→ [z n ]D (k) (z) ∼ e− Hk . 1−z Since there is no other singularity at a finite distance, the error in the approximation is (at least) exponentially small, D (k) (z) ∼

1 − z − z 2 −···− z k k = e− Hk + O(R −n ), e 1 2 1−z for any R > 1. The cases k = 1, 2 in particular justify the estimates mentioned at the beginning of this chapter, on p. 228. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (33)

[z n ]

This example is also worth reflecting upon. In prohibiting cycles of length < k, k we modify the EGF of all permutations, (1 − z)−1 by a factor e−z/1−···−z /k . The resulting EGF is meromorphic at 1; thus only the value of the modifying factor at z = 1 matters, so that this value, namely e− Hk , provides the asymptotic proportion of k–derangements. We shall encounter more and more shortcuts of this sort as we progress into the book.

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IV. COMPLEX ANALYSIS, RATIONAL AND MEROMORPHIC ASYMPTOTICS

IV.29. Shortest cycles of permutations are not too long. Let Sn be the random variable denoting the length of the shortest cycle in a random permutation of size n. Using the circle |z| = 2 to estimate the error in the approximation e− Hk above, one finds that, for k ≤ log n, 1 k+1 P(Sn > k) − e− Hk ≤ n e2 , 2

which is exponentially small in this range of k-values. Thus, the approximation e− Hk remains usable when k is allowed to tend sufficiently slowly to ∞ with n. One can also explore the possibility of better bounds and larger regions of validity of the main approximation. (See Panario and Richmond’s study [470] for a general theory of smallest components in sets.)

IV.30. Expected length of the shortest cycle. The classical approximation of the harmonic

numbers, Hk ≈ log k + γ , suggests e−γ /k as a possible approximation to (33) for both large n and large k in suitable regions. In agreement with this heuristic argument, the expected length of the shortest cycle in a random permutation of size n is effectively asymptotic to n X e−γ ∼ e−γ log n, k

k=1

a property first discovered by Shepp and Lloyd [540].

The next example illustrates the analysis of a collection of rational generating functions (Smirnov words) paralleling nicely the enumeration of a special type of integer composition (Carlitz compositions), which belongs to meromorphic asymptotics. Example IV.10. Smirnov words and Carlitz compositions. Bernoulli trials have been discussed in Chapter III (p. 204), in relation to weighted word models. Take the class W of all words over an r –ary alphabet, where letter j is assigned probability p j and letters of words are drawn P independently. With this weighting, the GF of all words is W (z) = 1/(1 − p j z) = (1 − z)−1 . Consider the problem of determining the probability that a random word of length n is of Smirnov type, that is, all blocks of length 2 are formed with unequal letters. In order to avoid degeneracies, we impose r ≥ 3 (since for r = 2, the only Smirnov words are ababa. . . and babab. . . ). By our discussion in Example III.24 (p. 204), the GF of Smirnov words (again with the probabilistic weighting) is S(z) =

1−

1 P pjz . 1+ p j z

By monotonicity of the denominator, this rational function has a dominant singularity at the unique positive solution of the equation (34)

r X

j=1

pjρ = 1, 1 + pjρ

and the point ρ is a simple pole. Consequently, ρ is a well-characterized algebraic number defined implicitly by a polynomial equation of degree ≤ r . One can furthermore check, by studying the variations of the denominator, that the other roots are all real and negative; thus, ρ is the unique dominant singularity. (Alternatively, appeal to the Perron–Frobenius argument of Example V.11, p. 349) It follows that the probability for a word to be Smirnov is, not too

IV. 6. LOCALIZATION OF SINGULARITIES

263

surprisingly, exponentially small, the precise formula being −1 r X p ρ j . [z n ]S(z) ∼ C · ρ −n , C = (1 + p j ρ)2 j=1

A similar analysis, using bivariate generating functions, shows that in a random word of length n conditioned to be Smirnov, the letter j appears with asymptotic frequency (35)

qj =

pj 1 , Q (1 + p j ρ)2

Q :=

r X

j=1

pj (1 + p j ρ)2

,

in the sense that the mean number of occurrences of letter j is asymptotic to q j n. All these results are seen to be consistent with the equiprobable letter case p j = 1/r , for which ρ = r/(r − 1). Carlitz compositions illustrate a limit situation, in which the alphabet is infinite, while letters have different sizes. Recall that a Carlitz composition of the integer n is a composition of n such that no two adjacent summands have equal value. By Note III.32, p. 201, such compositions can be obtained by substitution from Smirnov words, to the effect that −1 ∞ j X z . (36) K (z) = 1 − 1+zj j=1

The asymptotic form of the coefficients then results from an analysis of dominant poles. The OGF has a simple pole at ρ, which is the smallest positive root of the equation

(37)

∞ X

j=1

ρj = 1. 1+ρj

(Note the analogy with (34) due to commonality of the combinatorial argument.) Thus: . . Kn ∼ C · βn , C = 0.45636 34740, β = 1.75024 12917. There, β = 1/ρ with ρ as in (37). In a way analogous to Smirnov words, the asymptotic frequency of summand k appears to be proportional to kρ k /(1 + ρ k )2 ; see [369, 421] for further properties. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

IV. 6. Localization of singularities There are situations where a function possesses several dominant singularities, that is, several singularities are present on the boundary of the disc of convergence. We examine here the induced effect on coefficients and discuss ways to locate such dominant singularities. IV. 6.1. Multiple singularities. In the case when there exists more than one dominant singularity, several geometric terms of the form β n sharing the same modulus (and each carrying its own subexponential factor) must be combined. In simpler situations, such terms globally induce a pure periodic behaviour for coefficients that is easy to describe. In the general case, irregular fluctuations of a somewhat arithmetic nature may prevail.

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IV. COMPLEX ANALYSIS, RATIONAL AND MEROMORPHIC ASYMPTOTICS

300000 200000 100000 00

100

200

300

400

-100000 -200000 -300000

Figure IV.9. The coefficients [z n ] f (z) of the rational function f (z) = −3 −1 1 + 1.02z 4 1 − 1.05z 5 illustrate a periodic superposition of regimes, depending on the residue class of n modulo 40.

Pure periodicities. When several dominant singularities of f (z) have the same modulus and are regularly spaced on the boundary of the disc of convergence, they may induce complete cancellations of the main exponential terms in the asymptotic expansion of the coefficient f n . In that case, different regimes will be present in the coefficients f n based on congruence properties of n. For instance, the functions 1 = 1 − z2 + z4 − z6 + z8 − · · · , 1 + z2

1 = 1 + z3 + z6 + z9 + · · · , 1 − z3

exhibit patterns of periods 4 and 3, respectively, this corresponding to poles that are roots of unity or order 4 (±i), and 3 (ω : ω3 = 1). Then, the function φ(z) =

1 2 − z 2 + z 3 + z 4 + z 8 + z 9 − z 10 1 + = 1 + z2 1 − z3 1 − z 12

has coefficients that obey a pattern of period 12 (for example, the coefficients φn such that n ≡ 1, 5, 6, 7, 11 modulo 12 are zero). Accordingly, the coefficients of [z n ]ψ(z)

where

ψ(z) = φ(z) +

1 , 1 − z/2

manifest a different exponential growth when n is congruent to 1, 5, 6, 7, 11 mod 12. See Figure IV.9 for such a superposition of pure periodicities. In many combinatorial applications, generating functions involving periodicities can be decomposed at sight, and the corresponding asymptotic subproblems generated are then solved separately.

IV.31. Decidability of polynomial properties. Given a polynomial p(z) ∈ Q[z], the following

properties are decidable: (i) whether one of the zeros of p is a root of unity; (ii) whether one of the zeros of p has an argument that is commensurate with π . [One can use resultants. An algorithmic discussion of this and related issues is given in [306].]

Nonperiodic fluctuations. As a representative example, consider the polynomial D(z) = 1 − 65 z + z 2 , whose roots are α=

3 4 +i , 5 5

α¯ =

3 4 −i , 5 5

IV. 6. LOCALIZATION OF SINGULARITIES

1

1

0.5

0.5

00

50

100

150

200

00

-0.5

-0.5

-1

-1

5

265

10

15

20

Figure IV.10. The coefficients of f (z) = 1/(1 − 56 z + z 2 ) exhibit an apparently chaotic behaviour (left) which in fact corresponds to a discrete sampling of a sine function (right), reflecting the presence of two conjugate complex poles.

both of modulus 1 (the numbers 3, 4, 5 form a Pythagorean triple), with argument . ±θ0 where θ0 = arctan( 34 ) = 0.92729. The expansion of the function f (z) = 1/D(z) starts as 1 11 84 3 779 4 2574 5 6 z − z − z + ··· , = 1 + z + z2 − 6 2 5 25 125 625 3125 1 − 5z + z the sign sequence being

+ + + − − − + + + + − − − + + + − − − − + + + − − − − + + + − − −,

which indicates a somewhat irregular oscillating behaviour, where blocks of three or four pluses follow blocks of three or four minuses. The exact form of the coefficients of f results from a partial fraction expansion: b 1 3 1 3 a + with a = + i, b = − i, f (z) = 1 − z/α 1 − z/α¯ 2 8 2 8

where α = eiθ0 , α = e−iθ0 Accordingly,

sin((n + 1)θ0 ) . sin(θ0 ) This explains the sign changes observed. Since the angle θ0 is not commensurate with π , the coefficients fluctuate but, unlike in our earlier examples, no exact periodicity is present in the sign patterns. See Figure IV.10 for a rendering and Figure V.3 (p. 299) for a meromorphic case linked to compositions into prime summands. Complicated problems of an arithmetical nature may occur if several such singularities with non-commensurate arguments combine, and some open problem remain even in the analysis of linear recurring sequences. (For instance no decision procedure is known to determine whether such a sequence ever vanishes [200].) Fortunately, such problems occur infrequently in combinatorial applications, where dominant poles of rational functions (as well as many other functions) tend to have a simple geometry as we explain next. (38)

f n = ae−inθ0 + beinθ0 =

IV.32. Irregular fluctuations and Pythagorean triples. The quantity θ0 /π is an irrational number, so that the sign fluctuations of (38) are “irregular” (i.e., non-purely periodic). [Proof: a contrario. Indeed, otherwise, α = (3 + 4i)/5 would be a root of unity. But then the minimal

266

IV. COMPLEX ANALYSIS, RATIONAL AND MEROMORPHIC ASYMPTOTICS

polynomial of α would be a cyclotomic polynomial with non-integral coefficients, a contradiction; see [401, VIII.3] for the latter property.]

IV.33. Skolem-Mahler-Lech Theorem. Let f n be the sequence of coefficients of a rational

function, f (z) = A(z)/B(z), where A, B ∈ Q[z]. The set of all n such that f n = 0 is the union of a finite (possibly empty) set and a finite number (possibly zero) of infinite arithmetic progressions. (The proof is based on p-adic analysis, but the argument is intrinsically nonconstructive; see [452] for an attractive introduction to the subject and references.)

Periodicity conditions for positive generating functions. By the previous discussion, it is of interest to locate dominant singularities of combinatorial generating functions, and, in particular, determine whether their arguments (the “dominant directions”) are commensurate to 2π . In the latter case, different asymptotic regimes of the coefficients manifest themselves, depending on the congruence properties of n. Definition IV.5. For a sequence ( f n ) with GF f (z), the support of f , denoted Supp( f ), is the set of all n such that f n 6= 0. The sequence ( f n ), as well as its GF f (z), is said to admit a span d if for some r , there holds Supp( f ) ⊆ r + dZ≥0 ≡ {r, r + d, r + 2d, . . .}. The largest span, p, is the period, all other spans being divisors of p. If the period is equal to 1, the sequence and its GF are said to be aperiodic. If f is analytic at 0, with span d, there exists a function g analytic at 0 such that f (z) = z r g(z d ), for some r ∈ Z≥0 . With E := Supp( f ), the maximal span [the period] is determined as p = gcd(E − E) (pairwise differences) as well as p = gcd(E − {r }) where r := min(E). For instance sin(z) has period 2, cos(z) + cosh(z) 5 has period 4, z 3 e z has period 5, and so on. In the context of periodicities, a basic property is expressed by what we have chosen to name figuratively the “Daffodil Lemma”. By virtue of this lemma, the span of a function f with non-negative coefficients is related to the behaviour of | f (z)| as z varies along circles centred at the origin (Figure IV.11). Lemma IV.1 (“Daffodil Lemma”). Let f (z) be analytic in |z| < ρ and have nonnegative coefficients at 0. Assume that f does not reduce to a monomial and that for some non-zero non-positive z satisfying |z| < ρ, one has | f (z)| = f (|z|). Then, the following hold: (i) the argument of z must be commensurate to 2π , i.e., z = Reiθ with θ/(2π ) = rp ∈ Q (an irreducible fraction) and 0 < r < p; (ii) f admits p as a span. Proof. This classical lemma is a simple consequence of the strong triangle inequality. Indeed, for Part (i) of the statement, with z = Reiθ , the equality | f (z)| = f (|z|) implies that the complex numbers f n R n einθ , for n ∈ Supp( f ), all lie on the same ray (a half-line emanating from 0). This is impossible if θ/(2π ) is irrational, since, by assumption, the expansion of f contains at least two monomials (one cannot have n 1 θ ≡ n 2 θ (mod 2π )). Thus, θ/(2π ) = r/ p is a rational number. Regarding Part (ii), consider two distinct indices n 1 and n 2 in Supp( f ) and let θ/(2π ) = r/ p. Then, by the strong triangle inequality again, one must have (n 1 − n 2 )θ ≡ 0 (mod 2π ); that

IV. 6. LOCALIZATION OF SINGULARITIES

267

1.5 1 0.5 0 -1.5

-1

-0.5

0

0.5

1

1.5

-0.5 -1 -1.5

Figure IV.11. Illustration of the “Daffodil Lemma”: the images of circles z = Reiθ 25 (R = 0.4 . . 0.8) rendered by a polar plot of | f (z)| in the case of f (z) = z 7 e z + z 2 /(1 − z 10 )), which has span 5.

is, (n i − n j )r/ p = (k1 − k2 ), for some k1 , k2 ∈ Z ≥ 0. This is only possible if p divides n 1 − n 2 . Hence, p is a span. Berstel [53] first realized that rational generating functions arising from regular languages can only have dominant singularities of the form ρω j , where ω is a certain root of unity. This property in fact extends to many non-recursive specifications, as shown by Flajolet, Salvy, and Zimmermann in [255]. Proposition IV.3 (Commensurability of dominant directions). Let S be a constructible labelled class that is non-recursive, in the sense of Theorem IV.8. Assume that the EGF S(z) has a finite radius of convergence ρ. Then there exists a computable integer d ≥ 1 such that the set of dominant singularities of S(z) is contained in the set {ρω j }, where ωd = 1. Proof. (Sketch; see [53, 255]) By definition, a non-recursive class S is obtained from 1 and Z by means of a finite number of union, product, sequence, set, and cycle constructions. We have seen earlier, in Section IV. 4 (p. 249), an inductive algorithm that determines radii of convergence. It is then easy to enrich that algorithm and determine simultaneously (by induction on the specification) the period of its GF and the set of dominant directions. The period is determined by simple rules. For instance, if S = T ⋆ U (S = T · U ) and T, U are infinite series with respective periods p, q, one has the implication Supp(T ) ⊆ a + pZ,

Supp(U ) ⊆ b + qZ

with ξ = gcd( p, q). Similarly, for S = S EQ(T ), Supp(T ) ⊆ a + pZ

H⇒

H⇒

Supp(S) ⊆ a + b + ξ Z,

Supp(S) ⊆ δZ,

where now δ = gcd(a, p). Regarding dominant singularities, the case of a sequence construction is typical. It corresponds to g(z) = (1 − f (z))−1 . Assume that f (z) = z a h(z p ), with p the

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IV. COMPLEX ANALYSIS, RATIONAL AND MEROMORPHIC ASYMPTOTICS

maximal period, and let ρ > 0 be such that f (ρ) = 1. The equations determining any dominant singularity ζ are f (ζ ) = 1, |ζ | = ρ. In particular, the equations imply | f (ζ )| = f (|ζ |), so that, by the Daffodil Lemma, the argument of ζ must be of the form 2πr/s. An easy refinement of the argument shows that, for δ = gcd(a, p), all the dominant directions coincide with the multiples of 2π/δ. The discussion of cycles is entirely similar since log(1 − f )−1 has the same dominant singularities as (1 − f )−1 . Finally, for exponentials, it suffices to observe that e f does not modify the singularity pattern of f , since exp(z) is an entire function.

IV.34. Daffodil lemma and unlabelled classes. Proposition IV.3 applies to any unlabelled class S that admits a non-recursive specification, provided its radius of convergence ρ satisfies ρ < 1. (When ρ = 1, there is a possibility of having the unit circle as a natural boundary, a property that is otherwise decidable from the specification.) The case of regular specifications will be investigated in detail in Section V. 3, p. 300.

Exact formulae. The error terms appearing in the asymptotic expansion of coefficients of meromorphic functions are already exponentially small. By peeling off the singularities of a meromorphic function layer by layer, in order of increasing modulus, one is led to extremely precise, sometimes even exact, expansions for the coefficients. Such exact representations are found for Bernoulli numbers Bn , surjection numbers Rn , as well as Secant numbers E 2n and Tangent numbers E 2n+1 , defined by ∞ X zn z Bn = (Bernoulli numbers) z n! e −1 n=0 ∞ X zn 1 R = (Surjection numbers) n n! 2 − ez n=0 (39) ∞ X z 2n 1 E 2n = (Secant numbers) (2n)! cos(z) n=0 ∞ X z 2n+1 E 2n+1 = tan(z) (Tangent numbers). (2n + 1)! n=0

Bernoulli numbers. These numbers traditionally written Bn can be defined by their EGF B(z) = z/(e z − 1), and they are central to Euler–Maclaurin expansions (p. 726). The function B(z) has poles at the points χk = 2ikπ , with k ∈ Z \ {0}, and the residue at χk is equal to χk , z χk ∼ (z → χk ). z e −1 z − χk

The expansion theorem for meromorphic functions is applicable here: start with the Cauchy integral formula, and proceed as in the proof of Theorem IV.10, using as external contours a large circle of radius R that passes half-way between poles. As R tends to infinity, the integrand tends to 0 (as soon as n ≥ 2) because the Cauchy kernel z −n−1 decreases as an inverse power of R while the EGF remains O(R). In the limit, corresponding to an infinitely large contour, the coefficient integral becomes equal to the sum of all residues of the meromorphic function over the whole of the complex plane.

IV. 6. LOCALIZATION OF SINGULARITIES

269

P From this argument, we get the representation Bn = −n! k∈Z\{0} χk−n . This verifies that Bn = 0 if n is odd and n ≥ 3. If n is even, then grouping terms two by two, we get the exact representation (which also serves as an asymptotic expansion): (40)

∞

X 1 B2n = (−1)n−1 21−2n π −2n . (2n)! k 2n k=1

Reverting the equality, we have also established that ζ (2n) = (−1)n−1 22n−1 π 2n

B2n , (2n)!

with

ζ (s) =

∞ X 1 , ks k=1

Bn = n![z n ]

ez

z , −1

a well-known identity that provides values of the Riemann zeta function ζ (s) at even integers as rational multiples of powers of π . Surjection numbers. In the same vein, the surjection numbers have EGF R(z) = (2 − e z )−1 with simple poles at χk = log 2 + 2ikπ

where

R(z) ∼

1 1 . 2 χk − z

Since R(z) stays bounded on circles passing half-way in between poles, we find the P exact formula, Rn = 12 n! k∈Z χk−n−1 . An equivalent real formulation is n+1 X ∞ cos((n + 1)θk ) 2kπ 1 Rn 1 , θk := arctan( + = ), (41) 2 2 2 (n+1)/2 n! 2 log 2 log 2 (log 2 + 4k π ) k=1 which exhibits infinitely many harmonics of fast decaying amplitude.

IV.35. Alternating permutations, tangent and secant numbers. The relation (40) also provides a representation of the tangent numbers since E 2n−1 = (−1)n−1 B2n 4n (4n − 1)/(2n). The secant numbers E 2n satisfy ∞ X

k=1

(−1)k (π/2)2n+1 = E 2n , 2 (2n)! (2k + 1)2n+1

which can be read either as providing an asymptotic expansion of E 2n or as an evaluation of the sums on the left (the values of a Dirichlet L-function) in terms of π . The asymptotic number of alternating permutations (pp. 5 and 143) is consequently known to great accuracy.

IV.36. Solutions to the equation tan(x) = x. Let P xn be the nth positive root of the equation tan(x) = x. For any integer r ≥ 1, the sum S(r ) := n xn−2r is a computable rational number. For instance: S2 = 1/10, S4 = 1/350, S6 = 1/7875. [From mathematical folklore.] IV. 6.2. Localization of zeros and poles. We gather here a few results that often prove useful in determining the location of zeros of analytic functions, and hence of poles of meromorphic functions. A detailed treatment of this topic may be found in Henrici’s book [329, §4.10]. Let f (z) be an analytic function in a region and let γ be a simple closed curve interior to , and on which f is assumed to have no zeros. We claim that the quantity Z f ′ (z) 1 dz (42) N( f ; γ ) = 2iπ γ f (z)

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IV. COMPLEX ANALYSIS, RATIONAL AND MEROMORPHIC ASYMPTOTICS

exactly equals the number of zeros of f inside γ counted with multiplicity. [Proof: the function f ′ / f has its poles exactly at the zeros of f , and the residue at each pole α equals the multiplicity of α as a root of f ; the assertion then results from the residue theorem.] Since a primitive function (antiderivative) of f ′ / f is log f , the integral also represents the variation of log f along γ , which is written [log f ]γ . This variation itself reduces to 2iπ times the variation of the argument of f along γ , since log(r eiθ ) = log r + iθ and the modulus r has variation equal to 0 along a closed contour ([log r ]γ = 0). The quantity [θ ]γ is, by its definition, 2π multiplied by the number of times the transformed contour f (γ ) winds about the origin, a number known as the winding number. This observation is known as the Argument Principle: Argument Principle. The number of zeros of f (z) (counted with multiplicities) inside the simple loop γ equals the winding number of the transformed contour f (γ ) around the origin. By the same argument, if f is meromorphic in ∋ γ , then N ( f ; γ ) equals the difference between the number of zeros and the number of poles of f inside γ , multiplicities being taken into account. Figure IV.12 exemplifies the use of the argument principle in localizing zeros of a polynomial. By similar devices, we get Rouch´e’s theorem: Rouch´e’s theorem. Let the functions f (z) and g(z) be analytic in a region containing in its interior the closed simple curve γ . Assume that f and g satisfy |g(z)| < | f (z)| on the curve γ . Then f (z) and f (z) + g(z) have the same number of zeros inside the interior domain delimited by γ . An intuitive way to visualize Rouch´e’s Theorem is as follows: since |g| < | f |, then f (γ ) and ( f + g)(γ ) must have the same winding number.

IV.37. Proof of Rouch´e’s theorem. Under the hypothesis of Rouch´e’s theorem, for 0 ≤ t ≤ 1, the function h(z) = f (z) + tg(z) is such that N (h; γ ) is both an integer and an analytic, hence continuous, function of t in the given range. The conclusion of the theorem follows. IV.38. The Fundamental Theorem of Algebra. Every complex polynomial p(z) of degree n has exactly n roots. A proof follows by Rouch´e’s theorem from the fact that, for large enough |z| = R, the polynomial assumed to be monic is a “perturbation” of its leading term, z n . [Other proofs can be based on Liouville’s Theorem (Note IV.7, p. 237) or on the Maximum Modulus Principle (Theorem VIII.1, p. 545).]

IV.39. Symmetric function of the zeros. Let Sk ( f ; γ ) be the sum of the kth powers of the roots of equation f (z) = 0 inside γ . One has Sk ( f ; γ ) =

1 2iπ

Z

by a variant of the proof of the Argument Principle.

f ′ (z) k z dz, f (z)

These principles form the basis of numerical algorithms for locating zeros of analytic functions, in particular the ones closest to the origin, which are of most interest to us. One can start from an initially large domain and recursively subdivide it until roots have been isolated with enough precision—the number of roots in a subdomain being at each stage determined by numerical integration; see Figure IV.12 and refer for instance to [151] for a discussion. Such algorithms even acquire the status of full

IV. 6. LOCALIZATION OF SINGULARITIES

271

0.8 1.5

0.6 0.4

1

0.2

0.5

0 0.2 0.4 0.6 0.8 -0.2

1

1.2 1.4 1.6 1.8

-0.5 00

1

0.5

1.5

2

2.5

3

-0.5

-0.4

-1

-0.6

-1.5

-0.8

4

8 6

2

4 2

-2

-1

00 -2

1

2

3

4

5

-8 -6 -4 -2 00 -2

2

4

6

8

10

-4 -6

-4

-8

Figure IV.12. The transforms of γ j = {|z| = 410j } by P4 (z) = 1 − 2z + z 4 , for j = 1, 2, 3, 4, demonstrate, via winding numbers, that P4 (z) has no zero inside |z| < 0.4, one zero inside |z| < 0.8, two zeros inside |z| < 1.2 and four zeros inside |z| < 1.6. The actual zeros are at ρ4 = 0.54368, 1 and 1.11514 ± 0.77184i.

proofs if one operates with guaranteed precision routines (using, for instance, careful implementations of interval arithmetics). IV. 6.3. Patterns in words: a case study. Analysing the coefficients of a single generating function that is rational is a simple task, often even bordering on the trivial, granted the exponential–polynomial formula for coefficients (Theorem IV.9, p. 256). However, in analytic combinatorics, we are often confronted with problems that involve an infinite family of functions. In that case, Rouch´e’s Theorem and the Argument Principle provide decisive tools for localizing poles, while Theorems IV.3 (Residue Theorem, p. 234) and IV.10 (Expansion of meromorphic functions, p. 258) serve to determine effective error terms. An illustration of this situation is the analysis of patterns in words for which GFs have been derived in Chapters I (p. 60) and III (p. 212). Example IV.11. Patterns in words: asymptotics. All patterns are not born equal. Surprisingly, in a random sequence of coin tossings, the pattern HTT is likely to occur much sooner (after 8 tosses on average) than the pattern HHH (needing 14 tosses on average); see the preliminary

272

IV. COMPLEX ANALYSIS, RATIONAL AND MEROMORPHIC ASYMPTOTICS

Length (k) k=3 k=4

types aab, abb, bba, baa aba, bab aaa, bbb aaab, aabb, abbb, bbba, bbaa, baaa aaba, abba, abaa, bbab, baab, babb abab, baba aaaa, bbbb

c(z) 1 1 + z2 1 + z + z2

ρ 0.61803 0.56984 0.54368

1

0.54368

1 + z3 1 + z2 1 + z + z2 + z3

0.53568 0.53101 0.51879

Figure IV.13. Patterns of length 3, 4: autocorrelation polynomial and dominant poles of S(z).

discussion in Example I.12 (p. 59). Questions of this sort are of obvious interest in the statistical analysis of genetic sequences [414, 603]. Say you discover that a sequence of length 100,000 on the four letters A,G,C,T contains the pattern TACTAC twice. Can this be assigned to chance or is this likely to be a meaningful signal of some yet unknown structure? The difficulty here lies in quantifying precisely where the asymptotic regime starts, since, by Borges’s Theorem (Note I.35, p. 61), sufficiently long texts will almost certainly contain any fixed pattern. The analysis of rational generating functions supplemented by Rouch´e’s theorem provides definite answers to such questions, under Bernoulli models at least. We consider here the class W of words over an alphabet A of cardinality m ≥ 2. A pattern p of some length k is given. As seen in Chapters I and III, its autocorrelation polynomial P j is central to enumeration. This polynomial is defined as c(z) = k−1 j=0 c j z , where c j is 1 if p coincides with its jth shifted version and 0 otherwise. We consider here the enumeration of words containing the pattern p at least once, and dually of words excluding the pattern p. In other words, we look at problems such as: What is the probability that a random text of length n does (or does not) contain your name as a block of consecutive letters? The OGF of the class of words excluding p is, we recall, (43)

c(z) S(z) = k . z + (1 − mz)c(z)

(Proposition I.4, p. 61), and we shall start with the case m = 2 of a binary alphabet. The function S(z) is simply a rational function, but the location and nature of its poles is yet unknown. We only know a priori that it should have a pole in the positive interval somewhere between 12 and 1 (by Pringsheim’s Theorem and since its coefficients are in the interval [1, 2n ], for n large enough). Figure IV.13 gives a small list, for patterns of length k = 3, 4, of the pole ρ of S(z) that is nearest to the origin. Inspection of the figure suggests ρ to be close to 21 as soon as the pattern is long enough. We are going to prove this fact, based on Rouch´e’s Theorem applied to the denominator of (43). As regards termwise domination of coefficients, the autocorrelation polynomial lies between 1 (for less correlated patterns like aaa. . . ab) and 1 + z + · · · + z k−1 (for the special case aaa. . . aa). We set aside the special case of p having only equal letters, i.e., a “maximal” autocorrelation polynomial—this case is discussed at length in the next chapter. Thus, in this scenario, the autocorrelation polynomial starts as 1 + z ℓ + · · · for some ℓ ≥ 2. Fix the

IV. 6. LOCALIZATION OF SINGULARITIES

273 1

1

0.5

0.5

0 -1

-0.5

0 0

0.5

1

-1

-0.5

-0.5

0

0.5

1

-0.5

-1

-1

Figure IV.14. Complex zeros of z 31 + (1 − 2z)c(z) represented as joined by a polygonal line: (left) correlated pattern a(ba)15 ; (right) uncorrelated pattern a(ab)15 .

number A = 0.6, which proves suitable for our subsequent analysis. On |z| = A, we have 1 A2 2 3 . (44) |c(z)| ≥ 1 − (A + A + · · · ) = 1 − = 1− A 10

In addition, the quantity (1 − 2z) ranges over the circle of diameter [−0.2, 1.2] as z varies along |z| = A, so that |1 − 2z| ≥ 0.2. All in all, we have found that, for |z| = A, |(1 − 2z)c(z)| ≥ 0.02.

On the other hand, for k > 7, we have |z k | < 0.017 on the circle |z| = A. Then, among the two terms composing the denominator of (43), the first is strictly dominated by the second along |z| = A. By virtue of Rouch´e’s Theorem, the number of roots of the denominator inside |z| ≤ A is then same as the number of roots of (1 − 2z)c(z). The latter number is 1 (due to the root 21 ) since c(z) cannot be 0 by the argument of (44). Figure IV.14 exemplifies the extremely well-behaved characters of the complex zeros. In summary, we have found that for all patterns with at least two different letters (ℓ ≥ 2) and length k ≥ 8, the denominator has a unique root in |z| ≤ A = 0.6. The same property for lengths k satisfying 4 ≤ k ≤ 7 is then easily verified directly. The case ℓ = 1 where we are dealing with long runs of identical letters can be subjected to an entirely similar argument (see also Example V.4, p. 308, for details). Therefore, unicity of a simple pole ρ of S(z) in the interval (0.5, 0.6) is granted, for a binary alphabet. It is then a simple matter to determine the local expansion of S(z) near z = ρ, e 3 , z→ρ ρ − z

S(z) ∼

e := 3

c(ρ) , 2c(ρ) − (1 − 2ρ)c′ (ρ) − kρ k−1

from which a precise estimate for coefficients results from Theorems IV.9 (p. 256) and IV.10 (p. 258). The computation finally extends almost verbatim to non-binary alphabets, with ρ being now close to 1/m. It suffices to use the disc of radius A = 1.2/m. The Rouch´e part of the argument grants us unicity of the dominant pole in the interval (1/m, A) for k ≥ 5 when m = 3, and for k ≥ 4 and any m ≥ 4. (The remaining cases are easily checked individually.)

274

IV. COMPLEX ANALYSIS, RATIONAL AND MEROMORPHIC ASYMPTOTICS

Proposition IV.4. Consider an m–ary alphabet. Let p be a fixed pattern of length k ≥ 4, with autocorrelation polynomial c(z). Then the probability that a random word of length n does not contain p as a pattern (a block of consecutive letters) satisfies n 5 (45) PWn (p does not occur) = 3p(mρ)−n−1 + O , 6 6 ) of the equation z k + (1 − mz)c(z) = 0 and where ρ ≡ ρp is the unique root in ( m1 , 5m 3p := mc(ρ)/(mc(ρ) − c′ (ρ)(1 − mρ) − kρ k−1 ).

Despite their austere appearance, these formulae have indeed a fairly concrete content. First, the equation satisfied by ρ can be put under the form mz = 1 + z k /c(z), and, since ρ is close to 1/m, we may expect the approximation (remember the use of “≈” as meaning “numerically approximately equal”, but not implying strict asymptotic equivalence) mρ ≈ 1 +

1 , γ mk

where γ := c(m −1 ) satisfies 1 ≤ γ < m/(m − 1). By similar principles, the probabilities in (45) are approximately −n k 1 PWn (p does not occur) ≈ 1 + ≈ e−n/(γ m ) . γ mk For a binary alphabet, this tells us that the occurrence of a pattern of length k starts becoming likely when n is of the order of 2k , that is, when k is of the order of log2 n. The more precise moment when this happens must depend (via γ ) on the autocorrelation of the pattern, with strongly correlated patterns having a tendency to occur a little late. (This vastly generalizes our empirical observations of Chapter I.) However, the mean number of occurrences of a pattern in a text of length n does not depend on the shape of the pattern. The apparent paradox is easily resolved, as we already observed in Chapter I: correlated patterns tend to occur late, while being prone to appear in clusters. For instance, the “late” pattern aaa, when it occurs, still has probability 21 to occur at the next position as well and cash in another occurrence; in contrast no such possibility is available to the “early” uncorrelated pattern aab, whose occurrences must be somewhat spread out. Such analyses are important as they can be used to develop a precise understanding of the behaviour of data compression algorithms (the Lempel–Ziv scheme); see Julien Fayolle’s contribution [204] for details. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

IV.40. Multiple pattern occurrences. A similar analysis applies to the generating function S hsi (z) of words containing a fixed number s of occurrences of a pattern p. The OGF is obtained by expanding (with respect to u) the BGF W (z, u) obtained in Chapter III, p. 212, by means of an inclusion–exclusion argument. For s ≥ 1, one finds S hsi (z) = z k

N (z)s−1 , D(z)s+1

D(z) = z k + (1 − mz)c(z),

which now has a pole of multiplicity s + 1 at z = ρ.

N (z) = z k + (1 − mz)(c(z) − 1)),

IV.41. Patterns in Bernoulli sequences—asymptotics. Similar results hold when letters are assigned non-uniform probabilities, p j = P(a j ), for a j ∈ A. The weighted autocorrelation polynomial is then defined by protrusions, as in Note III.39 (p. 213). Multiple pattern occurrences can be also analysed.

IV. 7. SINGULARITIES AND FUNCTIONAL EQUATIONS

275

IV. 7. Singularities and functional equations In the various combinatorial examples discussed so far in this chapter, we have been dealing with functions that are given by explicit expressions. Such situations essentially cover non-recursive structures as well as the very simplest recursive ones, such as Catalan or Motzkin trees, whose generating functions are expressible in terms of radicals. In fact, as we shall see extensively in this book, complex analytic methods are instrumental in analysing coefficients of functions implicitly specified by functional equations. In other words: the nature of a functional equation can often provide information regarding the singularities of its solution. Chapter V will illustrate this philosophy in the case of rational functions defined by systems of positive equations; a very large number of examples will then be given in Chapters VI and VII, where singularities that are much more general than poles are treated. In this section, we discuss three representative functional equations, 1 f (z) = ze f (z) , f (z) = z + f (z 2 + z 3 ), f (z) = , 1 − z f (z 2 ) associated, respectively, to Cayley trees, balanced 2–3 trees, and P´olya’s alcohols. These illustrate the use of fundamental inversion or iteration properties for locating dominant singularities and derive exponential growth estimates of coefficients. IV. 7.1. Inverse functions. We start with a generic problem already introduced on p. 249: given a function ψ analytic at a point y0 with z 0 = ψ(y0 ) what can be said about its inverse, namely the solution(s) to the equation ψ(y) = z when z is near z 0 and y near y0 ? Let us examine what happens when ψ ′ (y0 ) 6= 0, first without paying attention to analytic rigour. One has locally (“≈” means as usual “approximately equal”) (46)

ψ(y) ≈ ψ(y0 ) + ψ ′ (y0 )(y − y0 ),

so that the equation ψ(y) = z should admit, for z near z 0 , a solution satisfying 1 (z − z 0 ). (47) y ≈ y0 + ′ ψ (y0 ) If this is granted, the solution being locally linear, it is differentiable, hence analytic. The Analytic Inversion Lemma10 provides a firm foundation for such calculations. Lemma IV.2 (Analytic Inversion). Let ψ(z) be analytic at y0 , with ψ(y0 ) = z 0 . Assume that ψ ′ (y0 ) 6= 0. Then, for z in some small neighbourhood 0 of z 0 , there exists an analytic function y(z) that solves the equation ψ(y) = z and is such that y(z 0 ) = y0 . Proof. (Sketch) The proof involves ideas analogous to those used to establish Rouch´e’s Theorem and the Argument Principle (see especially the argument justifying Equation (42), p. 269). As a preliminary step, define the integrals ( j ∈ Z≥0 ) Z 1 ψ ′ (y) (48) σ j (z) := y j dy, 2iπ γ ψ(y) − z 10A more general statement and several proof techniques are also discussed in Appendix B.5: Implicit Function Theorem, p. 753.

276

IV. COMPLEX ANALYSIS, RATIONAL AND MEROMORPHIC ASYMPTOTICS

where γ is a small enough circle centred at y0 in the y-plane. First consider σ0 . This function satisfies σ0 (z 0 ) = 1 [by the Residue Theorem] and is a continuous function of z whose value can only be an integer, this value being the number of roots of the equation ψ(y) = z. Thus, for z close enough to z 0 , one must have σ0 (z) ≡ 1. In other words, the equation ψ(y) = z has exactly one solution, the function ψ is locally invertible and a solution y = y(z) that satisfies y(z 0 ) = y0 is well-defined. Next examine σ1 . By the Residue Theorem once more, the integral defining σ1 (z) is the sum of the roots of the equation ψ(y) = z that lie inside γ , that is, in our case, the value of y(z) itself. (This is also a particular case of Note IV.39, p. 270.) Thus, one has σ1 (z) ≡ y(z). Since the integral defining σ1 (z) depends analytically on z for z close enough to z 0 , analyticity of y(z) results.

IV.42. Details. Let ψ be analytic in an open disc D centred at y0 . Then, there exists a small circle γ centred at y0 and contained in D such that ψ(y) 6= y0 on γ . [Zeros of analytic functions are isolated, a fact that results from the definition of an analytic expansion]. The integrals σ j (z) are thus well defined for z restricted to be close enough to z 0 , which ensures that there exists a δ > 0 such that |ψ(y) − z| > δ for all y ∈ γ . One can then expand the integrand as a power series in (z − z 0 ), integrate the expansion termwise, and form in this way the analytic expansions of σ0 , σ1 at z 0 . (This line of proof follows [334, I, §9.4].) IV.43. Inversion and majorant series. The process corresponding to (46) and (47) can be transformed into a sound proof: first derive a formal power series solution, then verify that the formal solution is locally convergent using the method of majorant series (p. 250).

The Analytic Inversion Lemma states the following: An analytic function locally admits an analytic inverse near any point where its first derivative is non-zero. However, as we see next, a function cannot be analytically inverted in a neighbourhood of a point where its first derivative vanishes. Consider now a function ψ(y) such that ψ ′ (y0 ) = 0 but ψ ′′ (y0 ) 6= 0, then, by the Taylor expansion of ψ, one expects 1 (49) ψ(y) ≈ ψ(y0 ) + (y − y0 )2 ψ ′′ (y0 ). 2 Solving formally for y now indicates a locally quadratic dependency 2 (z − z 0 ), (y − y0 )2 ≈ ′′ ψ (y0 ) and the inversion problem admits two solutions satisfying s 2 √ (50) y ≈ y0 ± z − z0. ′′ ψ (y0 ) What this informal argument suggests is that the solutions have a singularity at z 0 , and, in order for them to be suitably specified, one must somehow restrict their domain of √ definition: the case of z (the root(s) of y 2 − z = 0) discussed on p. 230 is typical. Given some point z 0 and a neighbourhood of z 0 , the slit neighbourhood along direction θ is the set \θ := z ∈ arg(z − z 0 ) 6≡ θ mod 2π, z 6= z 0 . We state:

IV. 7. SINGULARITIES AND FUNCTIONAL EQUATIONS

277

Lemma IV.3 (Singular Inversion). Let ψ(y) be analytic at y0 , with ψ(y0 ) = z 0 . Assume that ψ ′ (y0 ) = 0 and ψ ′′ (y0 ) 6= 0. There exists a small neighbourhood 0 of z 0 such that the following holds: for any fixed direction θ , there exist two functions, \θ \θ y1 (z) and y2 (z) defined on 0 that satisfy ψ(y(z)) = z; each is analytic in 0 , has a singularity at the point z 0 , and satisfies limz→z 0 y(z) = y0 . Proof. (Sketch) Define the functions σ j (z) as in the proof of the previous lemma, Equation (48). One now has σ0 (z) = 2, that is, the equation ψ(y) = z possesses two roots near y0 , when z is near z 0 . In other words ψ effects a double covering of a small neighbourhood of y0 onto the image neighbourhood 0 = ψ() ∋ z 0 . By possibly restricting , we may furthermore assume that ψ ′ (y) only vanishes at y0 in (zeros of analytic functions are isolated) and that is simply connected. \θ Fix any direction θ and consider the slit neighbourhood 0 . Fix a point ζ in this slit domain; it has two preimages, η1 , η2 ∈ . Pick up the one named η1 . Since ψ ′ (η1 ) is non-zero, the Analytic Inversion Lemma applies: there is a local analytic \θ inverse y1 (z) of ψ. This y1 (z) can then be uniquely continued11 to the whole of 0 , and similarly for y2 (z). We have thus obtained two distinct analytic inverses. Assume a contrario that y1 (z) can be analytically continued at z 0 . It would then admit a local expansion X y1 (z) = cn (z − z 0 )n , n≥0

while satisfying ψ(y1 (z)) = z. But then, composing the expansions of ψ and y would entail (z → z 0 ), ψ(y1 (z)) = z 0 + O (z − z 0 )2

which cannot coincide with the identity function (z). A contradiction has been reached. The point z 0 is thus a singular point for y1 (as well as for y2 ).

IV.44. Singular inversion and majorant series. In a way that parallels Note IV.43, the process summarized by Equations (49) and (50) can be justified by the method of majorant series, which leads to an alternative proof of the Singular Inversion Lemma.

IV.45. Higher order branch points. If all derivatives of ψ till order r − 1 inclusive vanish at y0 , there are r inverses, y1 (z), . . . , yr (z), defined over a slit neighbourhood of z 0 . Tree enumeration. We can now consider the problem of obtaining information on the coefficients of a function y(z) defined by an implicit equation (51)

y(z) = zφ(y(z)),

when φ(u) is analytic at u = 0. In order for the problem to be well-posed (i.e., algebraically, in terms of formal power series, as well as analytically, near the origin, there should be a unique solution for y(z)), we assume that φ(0) 6= 0. Equation (51) may then be rephrased as u (52) ψ(y(z)) = z where ψ(u) = , φ(u) 11The fact of slitting makes the resulting domain simply connected, so that analytic continuation 0

becomes uniquely defined. In contrast, the punctured domain 0 \ {z 0 } is not simply connected, so that the argument cannot be applied to it. As a matter of fact, y1 (z) gets continued to y2 (z), when the ray of angle θ is crossed: the point z 0 where two determinations meet is a branch point.

278

IV. COMPLEX ANALYSIS, RATIONAL AND MEROMORPHIC ASYMPTOTICS 2

φ(u)

y(z)

ψ(u)

1.5 0.3

6

y 1 0.2

4

0.5 0.1 2 0

0.5

1

u

1.5

2

0

0.5

1

u

1.5

2

0

0.1

z

0.2

0.3

Figure IV.15. Singularities of inverse functions: φ(u) = eu (left); ψ(u) = u/φ(u) (centre); y = Inv(ψ) (right).

so that it is in fact an instance of the inversion problem for analytic functions. Equation (51) occurs in the counting of various types of trees, as seen in Subsections I. 5.1 (p. 65), II. 5.1 (p. 126), and III. 6.2 (p. 193). A typical case is φ(u) = eu , which corresponds to labelled non-plane trees (Cayley trees). The function φ(u) = (1+u)2 is associated to unlabelled plane binary trees and φ(u) = 1+u +u 2 to unary– binary trees (Motzkin trees). A full analysis was developed by Meir and Moon [435], themselves elaborating on earlier ideas of P´olya [488, 491] and Otter [466]. In all these cases, the exponential growth rate of the number of trees can be automatically determined. Proposition IV.5. Let φ be a function analytic at 0, having non-negative Taylor coefficients, and such that φ(0) 6= 0. Let R ≤ +∞ be the radius of convergence of the series representing φ at 0. Under the condition, (53)

lim

x→R −

xφ ′ (x) > 1, φ(x)

there exists a unique solution τ ∈ (0, R) of the characteristic equation, (54)

τ φ ′ (τ ) = 1. φ(τ )

Then, the formal solution y(z) of the equation y(z) = zφ(y(z)) is analytic at 0 and its coefficients satisfy the exponential growth formula: n 1 τ 1 [z n ] y(z) ⊲⊳ where ρ = = ′ . ρ φ(τ ) φ (τ ) Note that condition (53) is automatically realized as soon as φ(R − ) = +∞, which covers our earlier examples as well as all the cases where φ is an entire function (e.g., a polynomial). Figure IV.15 displays graphs of functions on the real line associated to a typical inversion problem, that of Cayley trees, where φ(u) = eu .

Proof. By Note IV.46 below, the function xφ ′ (x)/φ(x) is an increasing function of x for x ∈ (0, R). Condition (53) thus guarantees the existence and unicity of a solution

IV. 7. SINGULARITIES AND FUNCTIONAL EQUATIONS

Type binary tree Motzkin tree gen. Catalan tree Cayley tree

φ(u) (1 + u)2 1 + u + u2 1 1−u eu

(R) (∞) (∞)

τ 1 1

(1)

1 2

(∞)

1

ρ 1 4 1 3 1 4 e−1

yn ⊲⊳ ρ −n yn ⊲⊳ 4n yn ⊲⊳ 3n yn ⊲⊳ 4n yn ⊲⊳ en

279

(p. 67) (p. 68) (p. 65) (p. 128)

Figure IV.16. Exponential growth for classical tree families.

of the characteristic equation. (Alternatively, rewrite the characteristic equation as φ0 = φ2 τ 2 + 2φ3 τ 3 + · · · , where the right side is clearly an increasing function.) Next, we observe that the equation y = zφ(y) admits a unique formal power series solution, which furthermore has non-negative coefficients. (This solution can for instance be built by the method of indeterminate coefficients.) The Analytic Inversion Lemma (Lemma IV.2) then implies that this formal solution represents a function, y(z), that is analytic at 0, where it satisfies y(0) = 0. Now comes the hunt for singularities and, by Pringsheim’s Theorem, one may restrict attention to the positive real axis. Let r ≤ +∞ be the radius of convergence of y(z) at 0 and set y(r ) := limx→r − y(x), which is well defined (although possibly infinite), given positivity of coefficients. Our goal is to prove that y(r ) = τ . — Assume a contrario that y(r ) < τ . One would then have ψ ′ (y(r )) 6= 0. By the Analytic Inversion Lemma, y(z) would be analytic at r , a contradiction. — Assume a contrario that y(r ) > τ . There would then exist r ∗ ∈ (0, r ) such that ψ ′ (y(r ∗ )) = 0. But then y would be singular at r ∗ , by the Singular Inversion Lemma, also a contradiction.

Thus, one has y(r ) = τ , which is finite. Finally, since y and ψ are inverse functions, one must have r = ψ(τ ) = τ/φ(τ ) = ρ, by continuity as x → r − , which completes the proof.

Proposition IV.5 thus yields an algorithm that produces the exponential growth rate associated to tree functions. This rate is itself invariably a computable number as soon as φ is computable (i.e., its sequence of coefficients is computable). This computability result complements Theorem IV.8 (p. 251), which is relative to nonrecursive structures only. As an example of application of Proposition IV.5, general Catalan trees correspond to φ(y) = (1 − y)−1 , whose radius of convergence is R = 1. The characteristic equation is τ/(1 − τ ) = 1, which implies τ = 1/2 and ρ = 1/4. We obtain (not a surprise!) yn ⊲⊳ 4n , a weak asymptotic formula for the Catalan numbers. Similarly, for Cayley trees, φ(u) = eu and R = +∞. The characteristic equation reduces to (τ − 1)eτ = 0, so that τ = 1 and ρ = e−1 , giving a weak form of Stirling’s formula: [z n ]y(z) = n n−1 /n! ⊲⊳ en . Figure IV.16 summarizes the application of the method to a few already encountered tree families.

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IV. COMPLEX ANALYSIS, RATIONAL AND MEROMORPHIC ASYMPTOTICS

As our previous discussion suggests, the dominant singularity of tree generating functions is, under mild conditions, of the square-root type. Such a singular behaviour can then be analysed by the methods of Chapter VI: the coefficients admit an asymptotic form [z n ] y(z) ∼ C · ρ −n n −3/2 ,

with a subexponential factor of the form n −3/2 ; see Section VI. 7, p. 402.

IV.46. Convexity of GFs, Boltzmann models, and the Variance Lemma. Let φ(z) be a non-constant analytic function with non-negative coefficients and a non-zero radius of convergence R, such that φ(0) 6= 0. For x ∈ (0, R) a parameter, define the Boltzmann random variable 4 (of parameter x) by the property (55)

P(4 = n) =

φn x n , φ(x)

with

E(s 4 ) =

φ(sx) φ(x)

the probability generating function of 4. By differentiation, the first two moments of 4 are E(4) =

xφ ′ (x) , φ(x)

E(42 ) =

x 2 φ ′′ (x) xφ ′ (x) + . φ(x) φ(x)

There results, for any non-constant GF φ, the general convexity inequality valid for 0 < x < R: d xφ ′ (x) (56) > 0, dx φ(x) due to the fact that the variance of a non-degenerate random variable is always positive. Equivalently, the function log(φ(et )) is convex for t ∈ (−∞, log R). (In statistical physics, a Boltzmann model (of parameter x) corresponds to a class 8 (with OGF φ) from which elements are drawn according to the size distribution (55). An alternative derivation of (56) is given in Note VIII.4, p. 550.)

IV.47. A variant form of the inversion problem. Consider the equation y = z+φ(y), where φ

is assumed to have non-negative coefficients and be entire, with φ(u) = O(u 2 ) at u = 0. This corresponds to a simple variety of trees in which trees are counted by the number of their leaves only. For instance, we have already encountered labelled hierarchies (phylogenetic trees in Section II. 5, p. 128) corresponding to φ(u) = eu −1−u, which gives rise to one of “Schr¨oder’s problems”. Let τ be the root of φ ′ (τ ) = 1 and set ρ = τ − φ(τ ). Then, [z n ]y(z) ⊲⊳ ρ −n . For the EGF L of labelled hierarchies (L = z + e L − 1 − L), this gives L n /n! ⊲⊳ (2 log 2 − 1)−n . (Observe that Lagrange inversion also provides [z n ]y(z) = n1 [wn−1 ](1 − y −1 φ(y))−n .)

IV. 7.2. Iteration. The study of iteration of analytic functions was launched by Fatou and Julia in the first half of the twentieth century. Our reader is certainly aware of the beautiful images associated with the name of Mandelbrot whose works have triggered renewed interest in these questions, now classified as belonging to the field of “complex dynamics” [31, 156, 443, 473]. In particular, the sets that appear in this context are often of a fractal nature. Mathematical objects of this sort are occasionally encountered in analytic combinatorics. We present here the first steps of a classic analysis of balanced trees published by Odlyzko [459] in 1982. Example IV.12. Balanced trees. Consider the class E of balanced 2–3 trees defined as trees whose node degrees are restricted to the set {0, 2, 3}, with the additional property that all leaves are at the same distance from the root (Note I.67, p. 91). We adopt as notion of size the number

IV. 7. SINGULARITIES AND FUNCTIONAL EQUATIONS

281

1

x0 x1 x2 x3 x4 x5 x6 x7 x8 x9 x10

0.8

0.6

0.4

0.2

0 0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

= = = . = . = . = . = . = . = . = . =

0.6 0.576 0.522878976 0.416358802 0.245532388 0.075088357 0.006061629 0.000036966 0.000000001 1.867434390 × 10−18 3.487311201 × 10−36

Figure IV.17. The iterates of a point x0 ∈ (0, ϕ1 ), here x0 = 0.6, by σ (z) = z 2 + z 3 converge fast to 0.

of leaves (also called external nodes), the list of all 4 trees of size 8 being:

Given an existing tree, a new tree is obtained by substituting in all possible ways to each external node (2) either a pair (2, 2) or a triple (2, 2, 2), and symbolically, one has E[2] = 2 + E 2 → (22 + 222) .

In accordance with the specification, the OGF of E satisfies the functional equation E(z) = z + E(z 2 + z 3 ),

(57)

corresponding to the seemingly innocuous recurrence n X k Ek with En = n − 2k k=0

E 0 = 0, E 1 = 1.

Let σ (z) = z 2 + z 3 . Equation (57) can be expanded by iteration in the ring of formal power series, (58)

E(z) = z + σ (z) + σ [2] (z) + σ [3] (z) + · · · ,

where σ [ j] (z) denotes the jth iterate of the polynomial σ : σ [0] (z) = z, σ [h+1] (z) = σ [h] (σ (z)) = σ (σ [h] (z)). Thus, E(z) is nothing but the sum of all iterates of σ . The problem is to determine the radius of convergence of E(z), and, by Pringsheim’s theorem, the quest for dominant singularities can be limited to the positive real line. For z > 0, the polynomial σ (z) has a unique fixed point, ρ = σ (ρ), at √ 1 1+ 5 ρ= where ϕ= ϕ 2 is the golden ratio. Also, for any positive x satisfying x < ρ, the iterates σ [ j] (x) do converge to 0; see Figure IV.17. Furthermore, since σ (z) ∼ z 2 near 0, these iterates converge to 0 doubly

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IV. COMPLEX ANALYSIS, RATIONAL AND MEROMORPHIC ASYMPTOTICS

0.9

0.8

0.7

0.6 0

1

2

3

4

5

6

Figure IV.18. Left: the fractal domain of analyticity of E(z) (inner domain in white and gray, with lighter areas representing slower convergence of the iterates of σ ) and its circle of convergence. Right: the ratio E n /(ϕ n n −1 ) plotted against log n for n = 1 . . 500 confirms that E n ⊲⊳ ϕ n and illustrates the periodic fluctuations of (60). exponentially fast (Note IV.48). By the triangle inequality, we have |σ (z)| ≤ σ (|z|), so that the sum in (58) is a normally converging sum of analytic functions, and is thus itself analytic for |z| < ρ. Consequently, E(z) is analytic in the whole of the open disc |z| < ρ. It remains to prove that the radius of convergence of E(z) is exactly equal to ρ. To that purpose it suffices to observe that E(z), as given by (58), satisfies E(x) → +∞

as

x → ρ−.

Let N be an arbitrarily large but fixed integer. It is possible to select a positive x N sufficiently close to ρ with x N < ρ, such that the N th iterate σ [N ] (x N ) is larger than 21 (the function σ [N ] (x) admits ρ as a fixed point and it is continuous and increasing at ρ). Given the sum expression (58), this entails the lower bound E(x N ) > N2 for such an x N < ρ. Thus E(x) is unbounded as x → ρ − and ρ is a singularity. The dominant positive real singularity of E(z) is thus ρ = ϕ −1 , and the Exponential Growth Formula gives the following estimate. Proposition IV.6. The number of balanced 2–3 trees satisfies: √ !n 1+ 5 n (59) [z ] E(z) ⊲⊳ . 2 It is notable that this estimate could be established so simply by a purely qualitative examination of the basic functional equation and of a fixed point of the associated iteration scheme. The complete asymptotic analysis of the E n requires the full power of singularity analysis methods to be developed in Chapter VI. Equation (60) below states the end result, which involves fluctuations that are clearly visible on Figure IV.18 (right). There is overconvergence of the representation (58), that is, convergence in certain domains beyond the disc of convergence

IV. 7. SINGULARITIES AND FUNCTIONAL EQUATIONS

283

of E(z). Figure IV.18 (left) displays the domain of analyticity of E(z) and reveals its fractal nature (compare with Figure VII.23, p. 536). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

IV.48. Quadratic convergence. First, for x ∈ [0, 21 ], one has σ (x) ≤ 23 x 2 , so that σ [ j] (x) ≤ j

j

(3/2)2 −1 x 2 . Second, for x ∈ [0, A], where A is any number < ρ, there is a number k A such k−k A that σ [k A ] (x) < 12 , so that σ [k] (x) ≤ (3/2) (3/4)2 . Thus, for any A < ρ, the series of iterates of σ is quadratically convergent when z ∈ [0, A].

IV.49. The asymptotic number of 2–3 trees. This analysis is from [459, 461]. The number of 2–3 trees satisfies asymptotically n ϕn ϕ , (60) En = (log n) + O n n2 . where is a periodic function with mean value (ϕ log(4−ϕ))−1 = 0.71208 and period log(4− . ϕ) = 0.86792. Thus oscillations are inherent in E n ; see Figure IV.18 (right).

IV. 7.3. Complete asymptotics of a functional equation. George P´olya (1887– 1985) is mostly remembered by combinatorialists for being at the origin of P´olya theory, a branch of combinatorics that deals with the enumeration of objects invariant under symmetry groups. However, in his classic article [488, 491] which founded this theory, P´olya discovered at the same time a number of startling applications of complex analysis to asymptotic enumeration12. We detail one of these now. Example IV.13. P´olya’s alcohols. The combinatorial problem of interest here is the determination of the number Mn of chemical P isomeres of alcohols Cn H2n+1 O H without asymmetric carbon atoms. The OGF M(z) = n Mn z n that starts as (EIS A000621) (61)

M(z) = 1 + z + z 2 + 2z 3 + 3z 4 + 5z 5 + 8z 6 + 14z 7 + 23z 8 + 39z 9 + · · · ,

is accessible through a functional equation, 1 . 1 − z M(z 2 ) which we adopt as our starting point. Iteration of the functional equation leads to a continued fraction representation, 1 M(z) = , z 1− z2 1− z4 1− .. . from which P´olya found: (62)

M(z) =

Proposition IV.7. Let M(z) be the solution analytic around 0 of the functional equation 1 . 1 − z M(z 2 ) Then, there exist constants K , β, and B > 1, such that . β = 1.68136 75244, Mn = K · β n 1 + O(B −n ) , M(z) =

. K = 0.36071 40971.

12In many ways, P´olya can be regarded as the grandfather of the field of analytic combinatorics.

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IV. COMPLEX ANALYSIS, RATIONAL AND MEROMORPHIC ASYMPTOTICS

We offer two proofs. The first one is based on direct consideration of the functional equation and is of a fair degree of applicability. The second one, following P´olya, makes explicit a special linear structure present in the problem. As suggested by the main estimate, the dominant singularity of M(z) is a simple pole. First proof. By positivity of the functional equation, M(z) dominates coefficientwise any P GF (1 − z M <m (z 2 ))−1 , where M <m (z) := 0≤ j<m Mn z n is the mth truncation of M(z). In particular, one has the domination relation (use M 0). Thus ∂z (z M(z ) z=ρ

1 , 1 − z/ρ

1 . ρ M(ρ 2 ) + 2ρ 3 M ′ (ρ 2 ) √ . The argument shows at the same time that M(z) is meromorphic in |z| < ρ = 0.77. That 2 ρ is the only pole of M(z) on |z| = ρ results from the fact that z M(z ) = z + z 3 + · · · can be subjected to the type of argument encountered in the context of the Daffodil Lemma (see the discussion of quasi-inverses in the proof of Proposition IV.3, p. 267). The translation of the singular expansion (63) then yields the statement. IV.51. The growth constant of molecules. The quantity ρ can be obtained as the limit of P 2n+1 the ρm satisfying m = 1, together with ρ ∈ [ 41 , 0.69]. In each case, only a n=0 Mn ρm . few of the Mn (provided by the functional equation) are needed. One obtains: ρ10 = 0.595, . . . ρ20 = 0.594756, ρ30 = 0.59475397, ρ40 = 0.594753964. This algorithms constitutes a . geometrically convergent scheme with limit ρ = 0.59475 39639. (63)

M(z) ∼ K z→ρ

K :=

IV. 7. SINGULARITIES AND FUNCTIONAL EQUATIONS

285

Second proof. First, a sequence of formal approximants follows from (62) starting with 1,

1 , 1−z

1 − z2 , = z 1 − z − z2 1− 1 − z2

1

1

1−

z

=

1 − z2 − z4 , 1 − z − z2 − z4 + z5

z2 1 − z4 which permits us to compute any number of terms of the series M(z). Closer examination of (62) suggests to set ψ(z 2 ) , M(z) = ψ(z) 1−

where ψ(z) = 1 − z − z 2 − z 4 + z 5 − z 8 + z 9 + z 10 − z 16 + · · · . Back substitution into (62) yields ψ(z 2 ) 1 ψ(z 2 ) ψ(z 2 ) = = or , ψ(z) ψ(z) ψ(z 2 ) − zψ(z 4 ) ψ(z 4 ) 1−z ψ(z 2 ) which shows ψ(z) to be a solution of the functional equation ψ(z) = ψ(z 2 ) − zψ(z 4 ),

ψ(0) = 1.

The coefficients of ψ satisfy the recurrence ψ4n = ψ2n ,

ψ4n+1 = −ψn ,

ψ4n+2 = ψ2n+1 ,

ψ4n+3 = 0,

which implies that their values are all contained in the set {0, −1, +1}. Thus, M(z) appears to be the quotient of two function, ψ(z 2 )/ψ(z), each analytic in the unit disc, and M(z) is meromorphic in the unit disc. A numerical evaluation then shows that . ψ(z) has its smallest positive real zero at ρ = 0.59475, which is a simple root. The quantity ρ is thus a pole of M(z) (since, numerically, ψ(ρ 2 ) 6= 0). Thus n ψ(ρ 2 ) 1 ψ(ρ 2 ) M(z) ∼ H⇒ M ∼ − . n (z − ρ)ψ ′ (ρ) ρψ ′ (ρ) ρ Numerical computations then yield P´olya’s estimate. Et voil`a! . . . . . . . . . . . . . . . . . . . . . . . . . .

The example of P´olya’s alcohols is exemplary, both from a historical point of view and from a methodological perspective. As the first proof of Proposition IV.7 demonstrates, quite a lot of information can be pulled out of a functional equation without solving it. (A similar situation will be encountered in relation to coin fountains, Example V.9, p. 330.) Here, we have made great use of the fact that if f (z) is analytic in |z| < r and some a priori bounds imply the strict inequalities 0 < r < 1, then one can regard functions like f (z 2 ), f (z 3 ), and so on, as “known” since they are analytic in the disc of convergence of f and even beyond, a situation also evocative of our earlier discussion of P´olya operators in Section IV. 4, p. 249. Globally, the lesson is that functional equations, even complicated ones, can be used to bootstrap the local singular behaviour of solutions, and one can often do so even in the absence of any explicit generating function solution. The transition from singularities to coefficient asymptotics is then a simple jump.

IV.52. An arithmetic exercise. The coefficients ψn = [z n ]ψ(z) can be characterized simply

in terms of the binary representation of n. Find the asymptotic proportion of the ψn for n ∈ [1 . . 2 N ] that assume each of the values 0, +1, and −1.

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IV. COMPLEX ANALYSIS, RATIONAL AND MEROMORPHIC ASYMPTOTICS

IV. 8. Perspective In this chapter, we have started examining generating functions under a new light. Instead of being merely formal algebraic objects—power series—that encode exactly counting sequences, generating functions can be regarded as analytic objects— transformations of the complex plane—whose singularities provide a wealth of information concerning asymptotic properties of structures. Singularities provide a royal road to coefficient asymptotics. We could treat here, with a relatively simple apparatus, singularities that are poles. In this perspective, the two main statements of this chapter are the theorems relative to the expansion of rational and meromorphic functions, (Theorems IV.9, p. 256, and IV.10, p. 258). These are classical results of analysis. Issai Schur (1875–1941) is to be counted among the very first mathematicians who recognized their rˆole in combinatorial enumerations (denumerants, Example IV.6, p. 257). The complex analytic thread was developed much further by George P´olya in his famous paper of 1937 (see [488, 491]), which Read in [491, p. 96] describes as a “landmark in the history of combinatorial analysis”. There, P´olya laid the groundwork of combinatorial chemistry, the enumeration of objects under group actions, and, last but not least, the complex asymptotic theory of graphs and trees. Thanks to complex analytic methods, many combinatorial classes amenable to symbolic descriptions can be thoroughly analysed, with regard to their asymptotic properties, by means of a selected collection of basic theorems of complex analysis. The case of structures such as balanced trees and molecules, where only a functional equation of sorts is available, is exemplary. The present chapter then serves as the foundation stone of a rich theory to be developed in future chapters. Chapter V will elaborate on the analysis of rational and meromorphic functions, and present a coherent theory of paths in graphs, automata, and transfer matrices in the perspective of analytic combinatorics. Next, the method of singularity analysis developed in Chapter VI considerably extends the range of applicability of the Second Principle to functions having singularities appreciably more complicated that poles (e.g., those involving fractional powers, logarithms, iterated logarithms, and so on). Applications will be given to recursive structures, including many types of trees, in Chapter VII. Chapter VIII, dedicated to saddle-point methods will then complete the picture of univariate asymptotics by providing a unified treatment of counting GFs that are either entire functions (hence, have no singularity at a finite distance) or manifest a violent growth at their singularities (hence, fall outside of the scope of meromorphic or singularity-analysis asymptotics). Finally, in Chapter IX, the corresponding perturbative methods will be put to use in order to distil limit laws for parameters of combinatorial structures. Bibliographic notes. This chapter has been designed to serve as a refresher of basic complex analysis, with special emphasis on methods relevant for analytic combinatorics. See Figure IV.19 for a concise summary of results. References most useful for the discussion given here include the books of Titchmarsh [577] (oriented towards classical analysis), Whittaker and Watson [604] (stressing special functions), Dieudonn´e [165], Hille [334], and Knopp [373]. Henrici [329] presents complex analysis under the perspective of constructive and numerical methods, a highly valuable point of view for this book.

IV. 8. PERSPECTIVE

287

Basics. The theory of analytic functions benefits from the equivalence between two notions, analyticity and differentiability. It is the basis of a powerful integral calculus, much different from its real variable counterpart. The following two results can serve as “axioms” of the theory. T HEOREM IV.1 [Basic Equivalence Theorem] (p. 232): Two fundamental notions are equivalent, namely, analyticity (defined by convergent power series) and holomorphy (defined by differentiability). Combinatorial generating functions, a priori determined by their expansions at 0 thus satisfy the rich set of properties associated with these two equivalent notions. T HEOREM IV.2 [Null Integral Property] (p. 234): The integral of an analytic function along a simple loop (closed path that can be contracted to a single point) is 0. Consequently, integrals are largely independent of particular details of the integration contour. Residues. For meromorphic functions (functions with poles), residues are essential. Coefficients of a function can be evaluated by means of integrals. The following two theorems provide connections between local properties of a function (e.g., coefficients at one point) and global properties of the function elsewhere (e.g., an integral along a distant curve). T HEOREM IV.3 [Cauchy’s residue theorem] (p. 234): In the realm of meromorphic functions, integrals of a function can be evaluated based on local properties of the function at a few specific points, its poles. T HEOREM IV.4 [Cauchy’s Coefficient Formula] (p. 237): This is an almost immediate consequence of Cauchy’s residue theorem: The coefficients of an analytic function admit of a representation by a contour integral. Coefficients can then be evaluated or estimated using properties of the function at points away from the origin. Singularities and growth. Singularities (places where analyticity stops), provide essential information on the growth rate of a function’s coefficients. The “First Principle” relates the exponential growth rate of coefficients to the location of singularities. T HEOREM IV.5 [Boundary singularities] (p. 240): A function (given by its series expansion at 0) always has a singularity on the boundary of its disc of convergence. T HEOREM IV.6 [Pringsheim’s Theorem] (p. 240): This theorem refines the previous one for functions with non-negative coefficients. It implies that, in the case of combinatorial generating functions, the search for a dominant singularity can be restricted to the positive real axis. T HEOREM IV.7 [Exponential Growth Formula] (p. 244): The exponential growth rate of coefficients is dictated by the location of the singularities nearest to the origin—the dominant singularities. T HEOREM IV.8 [Computability of growth] (p. 251): For any combinatorial class that is nonrecursive (iterative), the exponential growth rate of coefficients is invariably a computable number. This statement can be regarded as the first general theorem of analytic combinatorics. Coefficient asymptotics. The “Second Principle” relates subexponential factors of coefficients to the nature of singularities. For rational and meromorphic functions, everything is simple. T HEOREM IV.9 [Expansion of rational functions] (p. 256): Coefficients of rational functions are explicitly expressible in terms of the poles, given their location (values) and nature (multiplicity). T HEOREM IV.10 [Expansion of meromorphic functions] (p. 258): Coefficients of meromorphic functions admit of a precise asymptotic form with exponentially small error terms, given the location and nature of the dominant poles. Figure IV.19. A summary of the main results of Chapter IV.

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De Bruijn’s classic booklet [143] is a wonderfully concrete introduction to effective asymptotic theory, and it contains many examples from discrete mathematics thoroughly worked out using a complex analytic approach. The use of such analytic methods in combinatorics was pioneered in modern times by Bender and Odlyzko, whose first publications in this area go back to the 1970s. The state of affairs in 1995 regarding analytic methods in combinatorial enumeration is superbly summarized in Odlyzko’s scholarly chapter [461]. Wilf devotes Chapter 5 of his Generatingfunctionology [608] to this question. The books by Hofri [335], Mahmoud [429], and Szpankowski [564] contain useful accounts in the perspective of analysis of algorithms. See also our book [538] for a light introduction and the chapter by Vitter and Flajolet [598] for more on this specific topic.

Despite all appearances they [generating functions] belong to algebra and not to analysis. Combinatorialists use recurrence, generating functions, and such transformations as the Vandermonde convolution; others to my horror, use contour integrals, differential equations, and other resources of mathematical analysis. — J OHN R IORDAN [513, p. viii] and [512, Pref.]

V

Applications of Rational and Meromorphic Asymptotics

Analytic methods are extremely powerful and when they apply, they often yield estimates of unparalleled precision. — A NDREW O DLYZKO [461]

V. 1. V. 2. V. 3. V. 4. V. 5. V. 6. V. 7.

A roadmap to rational and meromorphic asymptotics The supercritical sequence schema Regular specifications and languages Nested sequences, lattice paths, and continued fractions Paths in graphs and automata Transfer matrix models Perspective

290 293 300 318 336 356 373

The primary goal of this chapter is to provide combinatorial illustrations of the power of complex analytic methods, and specifically of the rational–meromorphic framework developed in the previous chapter. At the same time, we shift gears and envisage counting problems at a new level of generality. Precisely, we organize combinatorial problems into wide families of combinatorial types amenable to a common treatment and associated with a common collection of asymptotic properties. Without attempting a formal definition, we call schema any such family determined by combinatorial and analytic conditions that covers an infinity of combinatorial classes. First, we discuss a general schema of analytic combinatorics known as the supercritical sequence schema, which provides a neat illustration of the power of meromorphic asymptotics (Theorem IV.10, p. 258), while being of wide applicability. This schema unifies the analysis of compositions, surjections, and alignments; it applies to any class which is defined as a sequence, provided components satisfy a simple analytic condition (“supercriticality”). For instance, one can predict very precisely (and easily) the number of ways in which an integer can be decomposed additively as a sum of primes (or twin primes), this even though many details of the distribution of primes are still surrounded in mystery. The next schema comprises regular specifications and languages, which a priori lead to rational generating functions and are thus systematically amenable to Theorem IV.9 (p. 256), to the effect that coefficients are described as exponential polynomials. In the case of regular specifications, much additional structure is present, especially positivity. Accordingly, counting sequences are of a simple exponential– polynomial form and fluctuations can be systematically circumvented. Applications presented in this chapter include the analysis of longest runs, attached to maximal 289

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V. APPLICATIONS OF RATIONAL AND MEROMORPHIC ASYMPTOTICS

sequences of good (or bad) luck in games of chance, pure birth processes, and the occurrence of hidden patterns (subsequences) in random texts. We then consider an important subset of regular specifications, corresponding to nested sequences, that combinatorially describe a variety of lattice paths. Such nested sequences naturally lead to nested quasi-inverses, which are none other than continued fractions. A wealth of combinatorial, algebraic, and analytic properties then surround such constructions. A prime illustration is the complete analysis of height in Dyck paths and general Catalan trees; other interesting applications relate to coin fountain and interconnection networks. Finally, the last two sections examine positive linear systems of generating functions, starting with the simplest case of finite graphs and automata, and concluding with the general framework of transfer matrices. Although the resulting generating functions are once more bound to be rational, there is benefit in examining them as defined implicitly (rather than solving explicitly) and work out singularities directly. The spectrum of matrices (the set of eigenvalues) then plays a central rˆole. An important case is the irreducible linear system schema, which is closely related to the Perron–Frobenius theory of non-negative matrices, whose importance has been long recognized in the theory of finite Markov chains. A general discussion of singularities can then be conducted, leading to valuable consequences on a variety of models— paths in graphs, finite automata, and transfer matrices. The last example discussed in this chapter treats locally constrained permutations, where rational functions combined with inclusion–exclusion provide an entry to the world of value-constrained permutations. In the various combinatorial examples encountered in this chapter, the generating functions are meromorphic in some domain extending beyond their disc of convergence at 0. As a consequence, the asymptotic estimates of coefficients involve main terms that are explicit exponential–polynomials and error terms that are exponentially smaller. This is a situation well summarized by Odlyzko’s aphorism quoted on p. 289: “Analytic methods [. . . ] often yield estimates of unparalleled precision”. V. 1. A roadmap to rational and meromorphic asymptotics The key character in this chapter is the combinatorial sequence construction S EQ. Since its translation into generating functions involves a quasi-inverse, (1 − f )−1 , the construction should in many cases be expected to induce polar singularities. Also, linear systems of equations, of which the simplest case is X = 1 + AX , are solvable by means of inverses: the solution is X = (1 − A)−1 in the scalar case, and it is otherwise expressible as a quotient of determinants (by Cramer’s rule) in the matrix case. Consequently, linear systems of equations are also conducive to polar singularities. This chapter accordingly develops along two main lines. First, we study nonrecursive families of combinatorial problems that are, in a suitable sense, driven by a sequence construction (Sections V. 2–V. 4). Second, we examine families of recursive problems that are naturally described by linear systems of equations (Sections V. 5– V. 6). Clearly, the general theorems giving the asymptotic forms of coefficients of rational and meromorphic functions apply. As we shall see, the additional positivity

V. 1. A ROADMAP TO RATIONAL AND MEROMORPHIC ASYMPTOTICS

291

structure arising from combinatorics entails notable simplifications in the asymptotic form of counting sequences. The supercritical sequence schema. This schema, fully described in Section V. 2 (p. 293) corresponds to the general form F = S EQ(G), together with a simple analytic condition, “supercriticality”, attached to the generating function G(z) of G. Under this condition, the sequence (Fn ) happens to be predictable and an asymptotic estimate, (1)

Fn = cβ n + O(B n ),

0 ≤ B < β,

c ∈ R>0 ,

applies with β such that G(1/β) = 1. Integer compositions, surjections, and alignments presented in Chapters I and II can then be treated in a unified manner. The supercritical sequence schema even covers situations where G is not necessarily constructible: this includes compositions into summands that are prime numbers or twin primes. Parameters, like the number of components and more generally profiles, are under these circumstances governed by laws that hold with a high probability. Regular specification and languages. This topic is treated in Section V. 3 (p. 300). Regular specifications are non-recursive specifications that only involve the constructions (+, ×, S EQ). In the unlabelled case, they can always be interpreted as describing a regular language in the sense of Section I. 4, p. 49. The main result here is the following: given a regular specification R, it is possible to determine constructively a number D, so that an asymptotic estimate of the form (2)

Rn = P(n)β n + O(B n ),

0 ≤ B < β,

P a polynomial,

holds, once the index n is restricted to a fixed congruence class modulo D. (Naturally, the quantities P, β, B may depend on the particular congruence class considered.) In other words, a “pure” exponential polynomial form holds for each of the D “sections” [subsequences defined on p. 302] of the counting sequence (Rn )n≥0 . In particular, irregular fluctuations, which might otherwise arise from the existence of several dominant poles sharing the same modulus but having incommensurable arguments (see the discussion in Subsection IV. 6.1, p. 263 dedicated to multiple singularities), are simply not present in regular specifications and languages. Similar estimates hold for profiles of regular specifications, where the profile of an object is understood to be the number of times any fixed construction is employed. Nested sequences, lattice paths, and continued fractions. The material considered in Section V. 4 (p. 318) could be termed the S EQ ◦ · · · ◦ S EQ schema, corresponding to nested sequences. The associated GFs are chains of quasi-inverses; that is, continued fractions. Although the general theory of regular specifications applies, the additional structure resulting from nested sequences implies, in essence, uniqueness and simplicity of the dominant pole, resulting directly in an estimate of the form (3)

Sn = cβ n + O(B n ),

0 ≤ B < β,

c ∈ R>0 ,

for objects enumerated by nested sequences. This schema covers lattice paths of bounded height, their weighted versions, as well as several other bijectively equivalent classes, like interconnection networks. In each case, profiles can be fully characterized, the estimates being of a simple form.

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V. APPLICATIONS OF RATIONAL AND MEROMORPHIC ASYMPTOTICS

Paths in graphs and automata. The framework of paths in directed graphs expounded in Section V. 5 (p. 336) is of considerable generality. In particular, it covers the case of finite automata introduced in Subsection I. 4.2, p. 56. Although, in the abstract, the descriptive power of this framework is formally equivalent to the one of regular specifications (Appendix A.7: Regular languages, p. 733), there is great advantage in considering directly problems whose natural formulation is recursive and phrased in terms of graphs or automata. (The reduction of automata to regular expressions is non-trivial so that it does not tend to preserve the original combinatorial structure.) The algebraic theory is that of matrices of the form (I − zT )−1 , where T is a matrix with non-negative entries. The analytic theory behind the scene is now that of positive matrices and the companion Perron–Frobenius theory. Uniqueness and simplicity of dominant poles of generating functions can be guaranteed under easily testable structural conditions—principally, the condition of irreducibility that corresponds to a strong connectedness of the system. Then a pure exponential polynomial form holds, (4)

Cn ∼ cλn1 + O(3n ),

0 ≤ 3 < λ1 ,

c ∈ R>0 ,

where λ1 is the (unique) dominant eigenvalue of the transition matrix T . Applications include walks over various types of graphs (the interval graph, the devil’s staircase) and words excluding one or several patterns (walks on the De Bruijn graph). Transfer matrices. This framework, whose origins lie in statistical physics, is an extension of automata and paths in graphs. What is retained is the notion of a finite state system, but transitions can now take place at different speeds. Algebraically, one is dealing with matrices of the form (I − T (z))−1 , where T is a matrix whose entries are polynomials (in z) with non-negative coefficients. Perron–Frobenius theory can be adapted to cover such cases, that, to a probabilist, look like a mixture of Markov chain and renewal theory. The consequence, for this category of models, is once more an estimate of the type (4), under irreducibility conditions; namely (5)

Dn ∼ cµn1 + O(M n ),

0 ≤ M < µ1 ,

c ∈ R>0 ,

where µ1 = 1/σ and σ is the smallest positive value of z such that T (z) has dominant eigenvalue 1. A striking application of transfer matrices is a study, with an experimental mathematics flavour, of self-avoiding walks and polygons in the plane: it turns out to be possible to predict, with a high degree of confidence (but no mathematical certainty, yet), what the number of polygons is and which distribution of area is to be expected. A combination of the transfer matrix approach with a suitable use of inclusion–exclusion (Subsection V. 6.4, p. 367) finally provides a solution to the classic m´enage problem of combinatorial theory as well as to many related questions regarding value-constrained permutations. Browsing notes. We, authors, recommend that our gentle reader first gets a bird’s eye view of this chapter, by skimming through sections, before descending to ground level and studying examples in detail—some of the latter are indeed somewhat technically advanced (e.g., they make use of Mellin transforms and/or develop limit laws). The contents of this chapter are not needed for Chapters VI–VIII, so that the reader who is impatient to penetrate further the logic of analytic combinatorics can at any

V. 2. THE SUPERCRITICAL SEQUENCE SCHEMA

293

time have a peek at Chapters VI–VIII. We shall see in Chapter IX (specifically, Section IX. 6, p. 650) that all the schemas considered here are, under simple nondegeneracy conditions, associated to Gaussian limit laws. Sections V. 2 to V. 6 are organized following a common pattern: first, we discuss “combinatorial aspects”, then “analytic aspects”, and finally “applications”. Each of Sections V. 2 to V. 5 is furthermore centred around two analytic–combinatorial theorems, one describing asymptotic enumeration, the other quantifying the asymptotic profiles of combinatorial structures. We examine in this way the supercritical sequence schema (Section V. 2), general regular specifications (Section V. 3), nested sequences (Section V. 4), and path-in-graphs models (Section V. 5). The last section (Section V. 6) departs slightly from this general pattern, since transfer matrices are reducible rather simply to the framework of paths in graphs and automata, so that we do not need specifically new statements. V. 2. The supercritical sequence schema This schema is combinatorially the simplest treated in this chapter, since it plainly deals with the sequence construction. An auxiliary analytic condition, named “supercriticality” ensures that meromorphic asymptotics applies and entails strong statistical regularities. The paradigm of supercritical sequences unifies the asymptotic properties of a number of seemingly different combinatorial types, including integer compositions, surjections, and alignments. V. 2.1. Combinatorial aspects. We consider a sequence construction, which may be taken in either the unlabelled or the labelled universe. In either case, we have F = S EQ(G)

H⇒

F(z) =

with G(0) = 0. It will prove convenient to set f n = [z n ]F(z),

1 , 1 − G(z)

gn = [z n ]G(z),

so that the number of Fn structures is f n in the unlabelled case and n! f n otherwise. From Chapter III, the BGF of F–structures with u marking the number of G– components is (6)

F = S EQ(uG)

H⇒

F(z, u) =

1 . 1 − uG(z)

We also have access to the BGF of F with u marking the number of Gk –components: (7) F hki = S EQ (uGk + (G \ Gk )) H⇒ F hki (z, u) =

1 . 1 − G(z) + (u − 1)gk z k

V. 2.2. Analytic aspects. We restrict attention to the case where the radius of convergence ρ of G(z) is non-zero, in which case, the radius of convergence of F(z) is also non-zero by virtue of closure properties of analytic functions. Here is the basic concept of this section.

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V. APPLICATIONS OF RATIONAL AND MEROMORPHIC ASYMPTOTICS

Definition V.1. Let F, G be generating functions with non-negative coefficients that are analytic at 0, with G(0) = 0. The analytic relation F(z) = (1 − G(z))−1 is said to be supercritical if G(ρ) > 1, where ρ = ρG is the radius of convergence of G. A combinatorial schema F = S EQ(G) is said to be supercritical if the relation F(z) = (1−G(z))−1 between the corresponding generating functions is supercritical.

Note that G(ρ) is well defined in R∪{+∞} as the limit limx→ρ − G(x) since G(x) increases along the positive real axis, for x ∈ (0, ρ). (The value G(ρ) corresponds to what has been denoted earlier by τG when discussing “signatures” in Section IV. 4, p. 249.) From now on we assume that G(z) is strongly aperiodic in the sense that there does not exist an integer d ≥ 2 such that G(z) = h(z d ) for some h analytic at 0. (Put otherwise, the span of 1 + G(z), as defined on p. 266, is equal to 1.) This condition entails no loss of analytic generality. Theorem V.1 (Asymptotics of supercritical sequence). Let the schema F = S EQ(G) be supercritical and assume that G(z) is strongly aperiodic. Then, one has 1 · σ −n 1 + O(An ) , [z n ]F(z) = ′ σ G (σ ) where σ is the root in (0, ρG ) of G(σ ) = 1 and A is a number less than 1. The number X of G–components in a random F–structure of size n has mean and variance satisfying G ′′ (σ ) 1 · (n + 1) − 1 + + O(An ) σ G ′ (σ ) G ′ (σ )2 σ G ′′ (σ ) + G ′ (σ ) − σ G ′ (σ )2 Vn (X ) = · n + O(1). σ 2 G ′ (σ )3 In particular, the distribution of X on Fn is concentrated. En (X )

=

Proof. See also [260, 547]. The basic observation is that G increases continuously from G(0) = 0 to G(ρG ) = τG (with τG > 1 by assumption) when x increases from 0 to ρG . Therefore, the positive number σ , which satisfies G(σ ) = 1 is well defined. Then, F is analytic at all points of the interval (0, σ ). The function G being analytic at σ , satisfies, in a neighbourhood of σ 1 G(z) = 1 + G ′ (σ )(z − σ ) + G ′′ (σ )(z − σ )2 + · · · . 2! so that F(z) has a pole at z = σ ; also, this pole is simple since G ′ (σ ) > 0, by positivity of the coefficients of G. Thus, we have 1 1 1 ≡ . F(z) ∼ − ′ ′ z→ρ G (σ )(z − σ ) σ G (σ ) 1 − z/σ Pringsheim’s theorem (Theorem IV.6, p. 240) then implies that the radius of convergence of F must coincide with σ . There remains to show that F(z) is meromorphic in a disc of some radius R > σ with the point σ as the only singularity inside the disc. This results from the assumption that G is strongly aperiodic. In effect, as a consequence of the Daffodil Lemma (Lemma IV.3, p. 267), one has G(σ eiθ ) 6= 1, for all θ 6≡ 0 (mod 2π ) . Thus, by compactness, there exists a closed disc of radius R > σ in which F is analytic except

V. 2. THE SUPERCRITICAL SEQUENCE SCHEMA

295

for a unique pole at σ . We can now apply the main theorem of meromorphic function asymptotics (Theorem IV.10, p. 258) to deduce the stated formula with A = σ/R. Next, the number of G–components in a random F structure of size n has BGF given by (6), and by differentiation, we get 1 G(z) 1 n 1 n ∂ [z ] [z ] = En (X ) = . f ∂u 1 − uG(z) f (1 − G(z))2 n

u=1

n

The problem is now reduced to extracting coefficients in a univariate generating function with a double pole at z = σ , and it suffices to expand the GF locally at σ : 1 1 G(z) 1 ∼ ≡ 2 ′ 2 . 2 ′ 2 2 z→ρ (1 − G(z)) G (σ ) (z − σ ) σ G (σ ) (1 − z/σ )2

The variance calculation is similar, with a triple pole being involved.

When a sequence construction is supercritical, the√number of components is in the mean of order n while its standard deviation is O( n). Thus, the distribution is concentrated (in the sense of Section III. 2.2, p. 161). In fact, there results from a general theorem of Bender [35] that the distribution of the number of components is asymptotically Gaussian, a property to be established in Section IX. 6, p. 650. Profiles of supercritical sequences. We have seen in Chapter III that integer compositions and integer partitions, when sampled at random, tend to assume rather different aspects. Given a sequence construction, F = S EQ(G), the profile of an element α ∈ F is the vector (X h1i , X h2i , . . .) where X h ji (α) is the number of G– components in α that have size j. In the case of (unrestricted) integer compositions, it could be proved elementarily (Example III.6, p. 167) that, on average, for size n, the number of 1-summands is ∼ n/2, the number of 2-summands is ∼ n/4, and so on. Now that meromorphic asymptotics is available, such a property can be placed in a much wider perspective. Theorem V.2 (Profiles of supercritical sequences). Consider a supercritical sequence construction, F = S EQ(G), with G(z) strongly aperiodic, as in Theorem V.1. The number of G–components of any fixed size k in a random F–object of size n satisfies (8)

En (X hki ) =

gk σ k n + O(1), σ G ′ (σ )

Vn (X hki ) = O(n),

where σ in (0, σG ) is such that G(σ ) = 1, and gk = [z k ]G(z).

Proof. The BGF with u marking the number of G–components of size k is given in (7). The mean value is then obtained as a quotient, gk z k 1 n 1 n ∂ hki [z ] F(z, u) [z ] = En (X ) = . fn ∂u fn (1 − G(z))2 u=1

The GF of cumulated values has a double pole at z = σ , and the estimate of the mean value follows. The variance is estimated similarly, after two successive differentiations and the analysis of a triple pole. P hki The total number of components X satisfies X = X , and, by Theorem V.1, its mean is asymptotic to n/(σ G ′ (σ )). Thus, Equation (8) indicates that, at least

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V. APPLICATIONS OF RATIONAL AND MEROMORPHIC ASYMPTOTICS

in some average-value sense, the “proportion” of components of size k among all components is given by gk σ k .

V.1. Proportion of k–components and convergence in probability. For any fixed k, the random hki variable X n / X n converges in probability to the value gk σ k , ( ) hki hki Xn Xn P k k k −→ gk σ , i.e., lim P gk σ (1 − ǫ) ≤ ≤ gk σ (1 + ǫ) = 1, n→∞ Xn Xn for any ǫ > 0. The proof is an easy consequence of the Chebyshev inequalities (the distributions hki of X n and X n are both concentrated).

V. 2.3. Applications. We examine here two types of applications of the supercritical sequence schema. Example V.1 makes explicit the asymptotic enumeration and the analysis of profiles of compositions, surjections and alignments. What stands out is the way the mean profile of a structure reflects the underlying inner construction K in schemas of the form S EQ(K(Z)). Example V.2 discusses compositions into restricted summands, including the striking case of compositions into primes. Example V.1. Compositions, surjections, and alignments. The three classes of interest here are integer compositions (C), surjections (R) and alignments (O), which are specified as C = S EQ(S EQ≥1 (Z)),

R = S EQ(S ET≥1 (Z)),

O = S EQ(C YC(Z))

and belong to either the labelled universe (C) or to the labelled universe (R and O). The generating functions (of type OGF, EGF, and EGF, respectively) are C(z) =

1 z , 1 − 1−z

R(z) =

1 , 1 − (e z − 1)

O(z) =

1 . 1 − log(1 − z)−1

A direct application of Theorem V.1 (p. 294) gives us back the known results Cn = 2n−1 ,

1 1 Rn ∼ (log 2)−n−1 , n! 2

1 On = e−1 (1 − e−1 )−n−1 , n!

corresponding to σ equal to 12 , log 2, and 1 − e−1 , respectively. Similarly, the expected number of summands in a random composition of the integer n is ∼ n/2; the expected cardinality of the range of a random surjection whose domain has cardinality n is asymptotic to βn with β = 1/(2 log 2); the expected number of components in a random alignment of size n is asymptotic to n/(e − 1). Theorem V.2 also applies, providing the mean number of components of size k in each case. The following table summarizes the conclusions. Structures

specification

Compositions

S EQ(S EQ≥1 (Z))

Surjections

S EQ(S ET≥1 (Z))

Alignments

S EQ(C YC(Z))

law (gk σ k ) 1 2k 1 (log 2)k k! 1 (1 − e−1 )k k

type Geometric

σ 1 2

Poisson

log 2

Logarithmic

1 − e−1

Note that the stated laws necessitate k ≥ 1. The geometric and Poisson law are classical; the logarithmic distribution (also called “logarithmic-series distribution”) of a parameter λ > 0 is

V. 2. THE SUPERCRITICAL SEQUENCE SCHEMA

297

Figure V.1. Profile of structures drawn at random represented by the sizes of their components in sorted order: (from left to right) a random composition, surjection, and alignment of size n = 100. by definition the law of a discrete random variable Y such that P(Y = k) =

λk 1 , −1 k log(1 − λ)

k ≥ 1.

The way the internal construction K in the schema S EQ(K(Z)) determines the asymptotic proportion of component of each size, Sequence 7→ Geometric;

Set 7→ Poisson;

Cycle 7→ Logarithmic,

stands out. Figure V.1 exemplifies the phenomenon by displaying components sorted by size and represented by vertical segments of corresponding lengths for three randomly drawn objects of size n = 100. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Example V.2. Compositions with restricted summands, compositions into primes. Unrestricted integer compositions are well understood as regards enumeration: their number is exactly Cn = 2n−1 , their OGF is C(z) = (1 − z)/(1 − 2z), and compositions with k summands are enumerated by binomial coefficients. Such simple exact formulae disappear when restricted compositions are considered, but, as we now show, asymptotics is much more robust to changes in specifications. Let S be a subset of the integers Z≥1 such that gcd(S) = 1, i.e., not all members of S are multiples of a common divisor d ≥ 2. In order to avoid trivialities, we also assume that S has at least two elements. The class C S of compositions with summands constrained to the set S then satisfies: X 1 C S = S EQ(S EQ S (Z)) H⇒ C S (z) = zs . , S(z) = 1 − S(z) s∈S

By assumption, S(z) is strongly aperiodic, so that Theorem V.1 (p. 294) applies directly. There is a well-defined number σ such that S(σ ) = 1,

0 < σ < 1,

and the number of S–restricted compositions satisfies 1 · σ −n 1 + O(An ) . ′ σ S (σ ) Among the already discussed cases, S = {1, 2} gives rise to Fibonacci numbers Fn and, more generally, S = {1, . . . , r } corresponds to partitions with summands at most r . In this case, the

(9)

CnS := [z n ]C S (z) =

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V. APPLICATIONS OF RATIONAL AND MEROMORPHIC ASYMPTOTICS

10 16 20 732 30 36039 40 1772207 50 87109263 60 4281550047 70 210444532770 80 10343662267187 90 508406414757253 100 24988932929490838

15 73 4 360 57 17722 61 871092 48 42815 49331 21044453 0095 1034366226 5182 5084064147 81706 24988932929 612479

Figure V.2. The pyramid relative to compositions into prime summands for n = 10 . . 100: (left: exact values; right: asymptotic formula rounded).

OGF, C {1,...,r } (z) =

1 r

1 − z 1−z 1−z

=

1−z 1 − 2z + zr +1

is a simple variant of the OGF associated to longest runs in strings, which is studied at length in Example V.4, p. 308. The treatment of the latter can be copied almost verbatim to the effect that the largest component in a random composition of n is found to be log2 n + O(1), both on average and with high probability. Compositions into primes. Here is a surprising application of the general theory. Consider the case where S is taken to be the set of prime numbers, Prime = {2, 3, 5, 7, 11, . . .}, thereby defining the class of compositions into prime summands. The sequence starts as 1, 0, 1, 1, 1, 3, 2, 6, 6, 10, 16, 20, 35, 46, 72, 105, corresponding to G(z) = z 2 +z 3 +z 5 +· · · , and is EIS A023360 in Sloane’s Encyclopedia. The formula (9) provides the asymptotic shape of the number of such compositions (Figure V.2). It is also worth noting that the constants appearing in (9) are easily determined to great accuracy, as we now explain. By (9) and the preceding equation, the dominant singularity of the OGF of compositions into primes is the positive root σ < 1 of the characteristic equation X S(z) ≡ z p = 1. p Prime

Fix a threshold value m 0 (for instance m 0 = 10 or 100) and introduce the two series X X zm0 z s , S + (z) := zs + S − (z) := . 1−z s∈S, s<m 0

s∈S, s<m 0

Clearly, for x ∈ (0, 1), one has S − (x) < S(x) < S + (x). Define then two constants σ − , σ + by the conditions

S − (σ − ) = 1,

S + (σ + ) = 1,

0 < σ − , σ + < 1.

These constants are algebraic numbers that are accessible to computation. At the same time, they satisfy σ + < σ < σ − . As the order of truncation, m 0 , increases, the values of σ + , σ − provide better and better approximations to σ , together with an interval in which σ provably lies. For instance, m 0 = 10 is enough to determine that 0.66 < σ < 0.69, and the choice

V. 2. THE SUPERCRITICAL SEQUENCE SCHEMA

80000

100000

60000

80000

40000

60000

299

40000

20000

20000 0 0 –20000

–20000 –40000

–40000

–60000

–60000

–80000

–80000

–100000

–100000

–120000

–120000

Figure V.3. Errors in the approximation of the number of compositions into primes for n = 70 . . 100: left, the values of CnPrime − g(n); right, the correction arising from the next two poles, which are complex conjugate, and its continuous extrapolation g2 (n), for n ∈ [70, 100]. . m 0 = 100 gives σ to 15 guaranteed digits of accuracy, namely, σ = 0.67740 17761 30660. Then, the asymptotic formula (9) instantiates as . . (10) CnPrime ∼ g(n), g(n) := λ · β n , λ = 0.30365 52633, β = 1.47622 87836. . (The constant β ≡ σ −1 = 1.47622 is akin to the family of Backhouse constants described in [211].) Once more, the asymptotic approximation is very good, as is exemplified by the “pyramid” of Figure V.2. The difference between CnPrime and its approximation g(n) from Equation (10) is plotted on the left-hand part of Figure V.3. The seemingly haphazard oscillations that manifest themselves are well explained by the principles discussed in Section IV. 6.1 (p. 263). It appears that the next poles of the OGF are complex conjugate and lie near −0.76 ± 0.44i, having modulus about 0.88. The corresponding residues then jointly contribute a quantity of the form . g2 (n) = c · An sin(ωn + ω0 ), A = 1.13290, for some constants c, ω, ω0 . Comparing the left-hand and right-hand parts of Figure V.3, we see that this next layer of poles explains quite well the residual error CnPrime − g(n). Here is finally a variant of compositions into primes that demonstrates in a striking way the scope of the method. Define the set Prime2 of “twinned primes” as the set of primes that belong to a twin prime pair, that is, p ∈ Prime2 if one of p − 2, p + 2 is prime. The set Prime2 starts as 3, 5, 7, 11, 13, 17, 19, 29, 31, . . . (prime numbers like 23 or 37 are thus excluded). The asymptotic formula for the number of compositions of the integer n into summands that are twinned primes is Prime2

Cn

∼ 0.18937 · 1.29799n ,

where the constants are found by methods analogous to the case of all primes. It is quite remarkable that the constants involved are still computable real numbers (and of low complexity, even), this despite the fact that it is not known whether the set of twinned primes is finite or

300

V. APPLICATIONS OF RATIONAL AND MEROMORPHIC ASYMPTOTICS

Prime2

infinite. Incidentally, a sequence that starts like Cn

,

1, 0, 0, 1, 0, 1, 1, 1, 2, 1, 3, 4, 3, 7, 7, 8, 14, 15, 21, 28, 33, 47, 58, . . . and coincides till index 22 included (!), but not beyond, was encountered by MacMahon1, as the authors discovered, much to their astonishment, from scanning Sloane’s Encyclopedia, where it appears as EIS A002124. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

V.2. Random generation of supercritical sequences. Let F = S EQ(G) be a supercritical sequence scheme. Consider a sequence of i.i.d. (independently identically distributed) random variables Y1 , Y2 , . . . each of them obeying the discrete law P(Y = k) = gk σ k ,

k ≥ 1.

A sequence is said to be hitting n if Y1 + · · · + Yr = n for some r ≥ 1. The vector (Y1 , . . . , Yr ) for a sequence conditioned to hit n has the same distribution as the sequence of the lengths of components in a random F –object of size n. For probabilists, this explains the shape of the formulae in Theorem V.1, which resemble renewal relations [205, Sec. XIII.10]. It also implies that, given a uniform random generator for G–objects, one can generate a random F –object of size n in O(n) steps on average [177]. This applies to surjections, alignments, and compositions in particular.

V.3. Largest components in supercritical sequences. Let F = S EQ(G) be a supercritical sequence. Assume that gk = [z k ]G(z) satisfies the asymptotic “smoothness” condition gk ∼ cρ −k k β , k→∞

c, ρ ∈ R>0 , β ∈ R.

Then the size L of the largest G component in a random F –object satisfies, for size n, 1 EFn (L) = (log n + β log log n) + o(log log n). log(ρ/σ ) This covers integer compositions (ρ = 1, β = 0) and alignments (ρ = 1, β = −1). [The analysis generalizes the case of longest runs in Example V.4 (p. 308) and is based on similar −1 P principles. The GF of F objects with L ≤ m is F hmi (z) = 1 − k≤m gk z k , according to Section III.7. For m large enough, this has a dominant singularity which is a simple pole at σm such that σm − σ ∼ c1 (σ/ρ)m m β . There follows a double-exponential approximation PFn (L ≤ m) ≈ exp −c2 nm β (σ/ρ)m

in the “central” region. See Example V.4 (p. 308) for a particular instance and Gourdon’s study [305] for a general theory.]

V. 3. Regular specifications and languages The purpose of this section is the general study of the (+, ×, S EQ) schema, which covers all regular specifications. As we show now, “pure” exponential–polynomial forms (ones with a single dominating exponential) can always be extracted. Theorems V.3 and V.4 below provide a universal framework for the asymptotic analysis of regular classes. Additional structural conditions to be introduced in later sections (nested sequences, irreducibility of the dependency graph and of transfer matrices) will then be seen to induce further simplifications in asymptotic formulae. 1See “Properties of prime numbers deduced from the calculus of symmetric functions”, Proc. London Math. Soc., 23 (1923), 290-316). MacMahon’s sequence corresponds to compositions into arbitrary odd primes, and 23 is the first such prime that is not twinned.

V. 3. REGULAR SPECIFICATIONS AND LANGUAGES

301

V. 3.1. Combinatorial aspects. For convenience and without loss of analytic generality, we consider here unlabelled structures. According to Chapter I (Definition I.10, p. 51, and the companion Proposition I.2, p. 52), a combinatorial specification is regular if it is non-recursive (“iterative”) and it involves only the constructions of Atom, Union, Product, and Sequence. A language L is S–regular if it is combinatorially isomorphic to a class M described by a regular specification. Alternatively, a language is S–regular if all the operations involved in its description (unions, catenation products and star operations) are unambiguous. The dictionary translating constructions into OGFs is (11)

F + G 7→ F + G,

F × G 7→ F × G,

S EQ(F) 7→ (1 − F)−1 ,

and for languages, under the essential condition of non-ambiguity (Appendix A.7: Regular languages, p. 733), (12)

L ∪ M 7→ L + M,

L · M 7→ L × M,

L⋆ 7→ (1 − L)−1 .

The rules (11) and (12) then give rise to generating functions that are invariably rational functions. Consequently, given a regular class C, the exponential–polynomial form of coefficients expressed by Theorem IV.9 (p. 256) systematically applies, and one has (13)

Cn ≡ [z n ]C(z) =

m X

5 j (n)α −n j ,

j=1

for a family of algebraic numbers α j (the poles of C(z)) and a family of polynomials 5 j . As we know from the discussion of periodicities in Section IV. 6.1 (p. 263), the collective behaviour of the sum in (13) depends on whether or not a single α dominates. In the case where several dominant singularities coexist, fluctuations of sorts (either periodic or irregular) may manifest themselves. In contrast, if a single α dominates, then the exponential–polynomial formula acquires a transparent asymptotic meaning. Accordingly, we set: P Definition V.2. An exponential–polynomial form mj=1 5 j (n)α −n j is said to be pure if |α1 | < |α j |, for all j ≥ 2. In that case, a single exponential dominates asymptotically all the other ones. As we see next for regular languages and specifications, the corresponding counting coefficients can always be described by a finite collection of pure exponential– polynomial forms. The fundamental reason is that we are dealing with a special subset of rational functions, one that enjoys strong positivity properties.

V.4. Positive rational functions. Define the class Rat+ of positive rational functions as

the smallest class containing polynomials with positive coefficients (R≥0 [z]) and closed under sum, product, and quasi-inverse, where Q( f ) = (1 − f )−1 is applied to elements f such that f (0) = 0. The OGF of any regular class with positive weights attached to neutral structures and atoms is in Rat+ . Conversely, any function in Rat+ is the OGF of a positively weighted regular class. The notion of a Rat+ function is for instance relevant to the analysis of weighted word models and Bernoulli trials (Section III. 6.1, p. 189).

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V. APPLICATIONS OF RATIONAL AND MEROMORPHIC ASYMPTOTICS

V. 3.2. Analytic aspects. First we need the notion of sections of a sequence. Definition V.3. Let ( f n ) be a sequence of numbers. Its section of parameters D, r , where D ∈ Z>0 and r ∈ Z≥0 is the subsequence ( f n D+r ). The numbers D and r are referred to as the modulus and the base, respectively. The main theorem describing the asymptotic behaviour of regular classes is a consequence of Proposition IV.3 (p. 267) and is originally due to Berstel. (See Soittola’s article [546] as well as the books by Eilenberg [189, Ch VII] and Berstel– Reutenauer [56] for context.) Theorem V.3 (Asymptotics of regular classes). Let S be a class described by a regular specification. Then there exists an integer D such that each section of modulus D of Sn that is not eventually 0 admits a pure exponential–polynomial form: for n larger than some n 0 , and any such section of base r , one has Sn = 5(n)β n +

m X

P j (n)β nj

j=1

n ≡ r mod D,

where the quantities β, β j , with β > |β j |, and the polynomials 5, P j , with 5(x) 6≡ 0, depend on the base r . Proof. (Sketch.) Let α1 be the dominant pole of S(z) that is positive. Proposition IV.3 (p. 267) asserts that any dominant pole, α is such that α/|α| is a root of unity. Let D0 D0 , where be such that the dominant singularities are all contained in the set {α1 ω j−1 } j=1 ω = exp(2iπ/D0 ). By collecting all contributions arising from dominant poles in the general expansion (13) and by restricting n to a fixed congruence class modulo D0 , namely n = ν D0 + r with 0 ≤ r < D0 , one gets (14)

−D0 ν

Sν D0 +r = 5[r ] (n)α1

+ O(A−n ).

There 5[r ] is a polynomial depending on r and the remainder term represents an exponential polynomial with growth at most O(A−n ) for some A > α1 . The sections with modulus D0 that are not eventually 0 can then be categorized into two classes. — Let R6=0 be the set of those values of r such that 5[r ] is not identically 0. The set R6=0 is non-empty (else the radius of convergence of S(z) would be larger than α1 .) For any base r ∈ R6=0 , the assertion of the theorem is then established with β = 1/α1 . — Let R0 be the set of those values of r such that 5[r ] (x) ≡ 0, with 5[r ] as given by (14). Then one needs to examine the next layer of poles of S(z), as detailed below. Consider a number r such that r ∈ R0 , so that the polynomial 5[r ] is identically 0. First, we isolate in the expansion of S(z) those indices that are congruent to r modulo D0 . ThisPis achieved by means two power series P of anHadamard product, which, givenP a(z) = an z n and b(z) = bn z , is defined as the series c(z) = cn z n such that

V. 3. REGULAR SPECIFICATIONS AND LANGUAGES

303

cn = an bn and is written c = a ⊙ b. In symbols: X X X (15) an z n ⊙ bn z n = an bn z n . n≥0

n≥0

We have: (16)

g(z) = S(z) ⊙

n≥0

zr 1 − z D0

.

A classical theorem [57, 189] from the theory of positive rational functions (in the sense of Note V.4) asserts that such functions are closed under Hadamard product. (A dedicated construction for (16) is also possible and is left as an exercise to the reader.) Then the resulting function G(z) is of the form g(z) = z r γ (z D0 ),

with the rational function γ (z) being analytic at 0. Note that we have [z ν ]γ (z) = Sν D0 +r , so that γ is exactly the generating function of the section of base r of S(z). One verifies next that γ (z), which is obtained by the substitution z 7→ z 1/D0 in g(z)z −r , is itself a positive rational function. Then, by a fresh application of Berstel’s Theorem (Proposition IV.3, p. 267), this function, if not a polynomial, has a radius of convergence ρ with all its dominant poles σ being such that σ/ρ is a root of unity of order D1 , for some D1 ≥ 1. The argument originally applied to S(z) can thus be repeated, with γ (z) replacing S(z). In particular, one finds at least one section (of modulus D1 ) of the coefficients of γ (z) that admits a pure exponential–polynomial form. The other sections of modulus D1 can themselves be further refined, and so on In other words, successive refinements of the sectioning process provide at each stage at least one pure exponential–polynomial form, possibly leaving a few congruence classes open for further refinements. Define the layer index of a rational function f as the integer κ( f ), such that κ( f ) = card |ζ | f (ζ ) = ∞ . (This index is thus the number of different moduli of poles of f .) It is seen that each successive refinement step decreases by at least 1 the layer index of the rational function involved, thereby ensuring termination of the whole refinement process. Finally, the collection of the iterated sectionings obtained can be reduced to a single sectioning according to a common modulus D, which is the least common multiple of the collection of all the finite products D0 D1 · · · that are generated by the algorithm. For instance the coefficients (Figure V.4) of the function (17)

L(z) =

1 z + , 2 4 (1 − z)(1 − z − z ) 1 − 3z 3

associated to the regular language a ⋆ (bb + cccc)⋆ + d(ddd + eee + f f f )⋆ , exhibit an apparently irregular behaviour, with the expansion of L(z) starting as 1 + 2z + 2z 2 + 2z 3 + 7z 4 + 4z 5 + 7z 6 + 16z 7 + 12z 8 + 12z 9 + 47z 10 + 20z 11 + · · · .

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V. APPLICATIONS OF RATIONAL AND MEROMORPHIC ASYMPTOTICS

50

8

40 6

30 4 20

2 10

0

0 0

5

10

15

20

25

0

20

40

60

80

100

120

140

Figure V.4. Plots of log Fn with Fn = [z n ]F(z) and F(z) as in (17) display fluctuations that disappear as soon as sections of modulus 6 are considered.

The first term in (17) has a periodicity modulo 2, while the second one has an obvious periodicity modulo 3. In accordance with the theorem, the sections modulo 6 each admit a pure exponential–polynomial form and, consequently, they become easy to describe (Note V.5).

V.5. √ Sections and asymptotic regimes. For the function L(z) of (17), one finds, with ϕ := (1 +

5)/2 and c1 , c2 ∈ R>0 ,

L n = 3−1/3 · 3n/3 + O(ϕ n/2 ) L n = c1 ϕ n/2 + O(1) L n = c2 ϕ n/2 + O(1)

(n ≡ 1, 4 mod 6), (n ≡ 0, 2 mod 6), (n ≡ 3, 5 mod 6),

in accordance with the general form predicted by Theorem V.3.

V.6. Extension to Rat+ functions. The conclusions of Theorem V.3 hold for any function in Rat+ in the sense of Note V.4. V.7. Soittola’s Theorem. This is a converse to Theorem V.3 proved in [546]. Assume that coefficients of an arbitrary rational function f (z) are non-negative and that there exists a sectioning such that each section admits a pure exponential–polynomial form. Then f (z) is in Rat+ in the sense of Note V.4; in particular, f is the OGF of a (weighted) regular class. Theorem V.3 is useful for interpreting the enumeration of regular classes and languages. It serves a similar purpose with regards to structural parameters of regular classes. Indeed, consider a regular specification C augmented with a mark u that is, as usual, a neutral object of size 0 (see Chapter III). We let C(z, u) be the corresponding BGF of C, so that Cn,k = [z n u k ]C(z, u) is the number of C–objects of size n that bear k marks. A suitable placement of marks makes it possible to record the number of times any given construction enters an object. For instance, in the augmented specification of binary words, C = (S EQ 0 and all θ satisfying |θ | ≤ π − φ, there holds

3(ceiθ u) = 1. u→+∞ 3(u) (Powers of logarithms and iterated logarithms are typically slowly varying functions.) Under uniformity assumptions on (22), the following estimate holds [248]: n α−1 1 ∼ 3(n). (23) [z n ](1 − z)−α 3 1−z Ŵ(α) For instance, we have: q p 1 log 1 exp log n exp z 1−z [z n ] . ∼ √ √ πn 1−z See also the discussion of Tauberian theory, p. 435. (22)

lim

VI.6. Iterated logarithms. For a general α 6∈ Z≤0 , the relation (23) admits as a special case

β δ 1 1 1 1 n α−1 1 log log log (log n)β (log log n)δ . ∼ z 1−z z z 1−z Ŵ(α) A full asymptotic expansion can be derived in this case. [z n ](1 − z)−α

Special cases. The conditions of Theorems VI.1 and VI.2 exclude explicitly the case when α is a negative integer: the formulae actually remain valid in this case, provided one interprets them as limit cases, making use of 1/ Ŵ(0) = 1/ Ŵ(−1) = · · · = 0 . Also, when β is a positive integer, the expansion of Theorem VI.2 terminates: in that situation, stronger forms are valid. Such cases are summarized in Figure VI.4 and discussed below. The case of integral α ∈ Z≤0 and general β 6∈ Z≥0 . When α is a negative integer, the coefficients of f (z) = (1 − z)−α eventually reduce to zero, so that the asymptotic coefficient expansion becomes trivial: this situation is implicitly covered by the statement of Theorem VI.1 since, in that case, 1/ Ŵ(α) = 0. When logarithms are present (with α ∈ Z≤0 still), the expansion of Theorem VI.2 regarding β 1 1 f (z) = (1 − z)−α log z 1−z

VI. 2. COEFFICIENT ASYMPTOTICS FOR THE STANDARD SCALE

387

remains valid provided we again take into account the equality 1/ Ŵ(α) = 0 in formula (21) after effecting simplifications by Gamma factors: it is only the first term of (21) that vanishes, and one has D2 D1 n α−1 β + (24) [z ] f (z) ∼ n + ··· , (log n) log n log2 n k 1 β d . For instance, we find where Dk is given by Dk = k ds k Ŵ(s) s=α [z n ]

z 2γ 1 1 + + O( ). =− 2 3 −1 log(1 − z) n log n n log n n log4 n

The case of general α 6∈ Z≤0 and integral β ∈ Z≥0 . When β = k is a nonnegative integer, the error terms can be further improved with respect to the ones predicted by the general statement of Theorem VI.2. For instance, we have: 1 1 log 1−z 1−z 1 1 [z n ] √ log 1 − z 1−z

[z n ]

= ∼

1 1 1 + O( 4 ) log n + γ + − 2 2n 12n n log n 1 log n + γ + 2 log 2 + O( ) . √ n πn

(In such a case, the expansion of Theorem VI.2 terminates since only its first (k + 1) terms are non-zero.) In fact, in the general case of non-integral α, there exists an expansion of the form E 1 (log n) n α−1 1 E 0 (log n) + ∼ + ··· , (25) [z n ](1 − z)−α logk 1−z Ŵ(α) n where the E j are polynomials of degree k, as can be proved by adapting the argument employed for general α (Note VI.8). The joint case of integral α ∈ Z≤0 and integral β ∈ Z≥0 . If α is a negative integer, the coefficients appear as finite differences of coefficients of logarithmic powers. Explicit formulae are then available elementarily from the calculus of finite differences when β is a positive integer. For instance, with α = −m for m ∈ Z≥0 , one has 1 m! (26) [z n ](1 − z)m log = (−1)m . 1−z n(n − 1) · · · (n − m) The case α = −m and β = k (with m, k ∈ Z≥0 ) is covered by (28) in Note VI.7 below: there is a formula analogous to (25), F1 (log n) 1 n m k −m−1 F0 (log n) + (27) [z ](1 − z) log ∼n + ··· , 1−z n but now with deg(F j ) = k − 1. Figure VI.5 provides the asymptotic form of coefficients of a few standard functions illustrating Theorems VI.1 and VI.2 as well as some of the “special cases”.

388

VI. SINGULARITY ANALYSIS OF GENERATING FUNCTIONS

Function

coefficients

(1 − z)3/2

1 1 3 45 1155 + O( 3 )) ( + + 512n 2 n π n 5 4 32n (0) 1 1 3 25 1 −√ ( + + + O( 3 )) 256n 2 n π n 3 2 16n 1 1 γ + 2 log 2 − 2 log n −√ ( log n + + O( )) 3 2 2 n πn 1 1 2 7 + O( 3 )) − (1 + + 2 2 4/3 9n 81n n 3Ŵ( )n

(1 − z) (1 − z)1/2 (1 − z)1/2 L(z) (1 − z)1/3 z/ L(z) 1 log(1 − z)−1 log2 (1 − z)−1 (1 − z)−1/3 (1 − z)−1/2 (1 − z)−1/2 L(z) (1 − z)−1

(1 − z)−1 L(z)

(1 − z)−1 L(z)2 (1 − z)−3/2 (1 − z)−3/2 L(z) (1 − z)−2

(1 − z)−2 L(z)

(1 − z)−2 L(z)2 (1 − z)−3

√

3

1 n log2 n

(−1 +

π 2 − 6γ 2 2γ 1 + + O( 3 )) log n 2 log2 n log n

(0) 1 n 1 1 1 1 (2 log n + 2γ − − 2 + O( 4 )) n n 6n n 1 1 (1 + O( )) n Ŵ( 13 )n 2/3 1 1 5 1 1 + + + O( 4 )) √ (1 − 8n πn 128n 2 1024n 3 n 1 log n + γ + 2 log 2 log n + O( 2 )) √ (log n + γ + 2 log 2 − 8n πn n 1 1 1 1 1 − + + O( 6 )) log n + γ + 2n 12n 2 120n 4 n log n π2 + O( ) log2 n + 2γ log n + γ 2 − 6 n r 1 n 3 7 + O( 3 )) (2 + − 4n 64n 2 n rπ n 3 log n 1 (2 log n + 2γ + 4 log 2 − 4 + + O( )) π 4n n n+1 1 1 n log n + (γ − 1)n + log n + + γ + O( ) 2 n log n π2 + O( )) n(log2 n + 2(γ − 1) log n + γ 2 − 2γ + 2 − 6 n 1 n2 + 3 n + 1 2 2

Figure VI.5. A table of some commonly encountered functions and the asymptotic forms of their coefficients. The following abbreviation is used: 1 L(z) := log . 1−z

VI. 3. TRANSFERS

389

VI.7. The method of Frobenius and Jungen. This is an alternative approach to the case β ∈ Z≥0 (see [360]). Start from the observation that k 1 ∂k −α log (1 − z) = (1 − z)−α , 1−z ∂α k then let the operators of differentiation ( ∂/∂α ) and coefficient extraction ( [z n ] ) commute (this can be justified by Cauchy’s coefficient formula upon differentiating under the integral sign). This yields k ∂k 1 Ŵ(n + α) , = (28) [z n ](1 − z)−α log k 1−z Ŵ(α)Ŵ(n + 1) ∂α which leads to an “exact” formula (Note VI.8 below).

VI.8. Shifted harmonic numbers. Define the α-shifted harmonic number by h n (α) :=

n−1 X j=0

1 . j +α

With L(z) := − log(1 − z), still, one has

n+α−1 h n (α) n n+α−1 [z n ](1 − z)−α L(z)2 = h ′n (α) + h n (α)2 . n (Note: h n (α) = ψ(α + n) − ψ(α), where ψ(s) := ∂s log Ŵ(s).) In particular, 1 1 1 2n [z n ] √ log [2 H2n − Hn ], = n 1−z 4 n 1−z [z n ](1 − z)−α L(z)

=

where Hn ≡ h n (1) is the usual harmonic number.

VI. 3. Transfers Our general objective is to translate an approximation of a function near a singularity into an asymptotic approximation of its coefficients. What is required at this stage is a way to extract coefficients of error terms (known usually in O(·) or o(·) form) in the expansion of a function near a singularity. This task is technically simple as a fairly coarse analysis suffices. As in the previous section, it relies on contour integration by means of Hankel-type paths; see for instance the summary in Equation (12), p. 381, above. A natural extension of the approach of the previous section is to assume the error terms to be valid in the complex plane slit along the real half line R≥1 . In fact, weaker conditions suffice: any domain whose boundary makes an acute angle with the half line R≥1 appears to be suitable. Definition VI.1. Given two numbers φ, R with R > 1 and 0 < φ < π2 , the open domain 1(φ, R) is defined as 1(φ, R) = {z |z| < R, z 6= 1, | arg(z − 1)| > φ}. A domain is a 1–domain at 1 if it is a 1(φ, R) for some R and φ. For a complex number ζ 6= 0, a 1–domain at ζ is the image by the mapping z 7→ ζ z of a 1–domain at 1. A function is 1–analytic if it is analytic in some 1–domain.

390

VI. SINGULARITY ANALYSIS OF GENERATING FUNCTIONS

............ ............. .................... ....... ...... . . . . . . .... .................................. ....... .....(1).......... .... ... ... ... ... ... ........ . ..... ..... ... ... ..φ R .... . ... . ... ..... 0 1............ .. .... ... ... . . . ... . ... ... ......... ... ........... ................ ..... .... ......... ..... ... ....... ...... . . . .......... . . . . ............................

............ ............. .................... ....... ...... . . . . . .... ... .... ... ..... .. .... 1/n ... . r ................. ......θ .... . ... .. ... ....1.......... 0 ... . . . . ... ... ... ... ... . . .... . ..... .... ....... ...... γ . . . . ........... . . . ..........................

Figure VI.6. A 1–domain and the contour used to establish Theorem VI.3.

Analyticity in a 1–domain (Figure VI.6, left) is the basic condition for transfer to coefficients of error terms in asymptotic expansions. Theorem VI.3 (Transfer, Big-Oh and little-oh). Let α, β be arbitrary real numbers, α, β ∈ R and let f (z) be a function that is 1–analytic. (i) Assume that f (z) satisfies in the intersection of a neighbourhood of 1 with its 1–domain the condition 1 β ) . f (z) = O (1 − z)−α (log 1−z Then one has: [z n ] f (z) = O(n α−1 (log n)β ). (ii) Assume that f (z) satisfies in the intersection of a neighbourhood of 1 with its 1–domain the condition 1 β f (z) = o (1 − z)−α (log ) . 1−z Then one has: [z n ] f (z) = o(n α−1 (log n)β ). Proof. (i) The starting point is Cauchy’s coefficient formula, Z dz 1 f (z) n+1 , f n ≡ [z n ] f (z) = 2iπ γ z

where γ is any simple loop around the origin which is internal to the 1–domain of f . We choose the positively oriented contour (Figure VI.6, right) γ = γ1 ∪ γ2 ∪ γ3 ∪ γ4 , with γ1 γ2 γ3 γ4

= = = =

1 z |z − 1| = , | arg(z − 1)| ≥ θ ] n 1 z ≤ |z − 1|, |z| ≤ r, arg(z − 1) = θ n z |z| = r, | arg(z − 1)| ≥ θ ] 1 ≤ |z − 1|, |z| ≤ r, arg(z − 1) = −θ z n

(inner circle) (top line segment) (outer circle) (bottom line segment).

VI. 3. TRANSFERS

391

If the 1 domain of f is 1(φ, R), we assume that 1 < r < R, and φ < θ < the contour γ lies entirely inside the domain of analyticity of f . For j = 1, 2, 3, 4, let Z 1 dz ( j) fn = f (z) n+1 . 2iπ γ j z

π 2,

so that

The analysis proceeds by bounding the absolute value of the integral along each of the four parts. In order to keep notations simple, we detail the proof in the case where β = 0. (1) Inner circle (γ1 ). From trivial bounds, the contribution from γ1 satisfies −α ! 1 1 = O n α−1 , | f n(1) | = O( ) · O n n

as the function is O(n α ) (by assumption on f (z)), the contour has length O(n −1 ), and z −n−1 remains O(1) on this part of the contour. (2) (2) Rectilinear parts (γ2 , γ4 ). Consider the contribution f n arising from the iθ part γ2 of the contour. Setting ω = e , and performing the change of variable z = 1 + ωt n , we find Z ∞ −α t 1 ωt −n−1 (2) K | fn | ≤ dt, 1 + n 2π 1 n for some constant K > 0 such that | f (z)| < K (1−z)−α over the 1–domain, which is granted by the growth assumption on f . From the relation 1 + ωt ≥ 1 + ℜ( ωt ) = 1 + t cos θ, n n n

there results the inequality | f n(2) |

K ≤ Jn n α−1 , 2π

where

Jn =

Z

∞

1

t

−α

t cos θ 1+ n

−n

dt.

For a given α, the integrals Jn are all bounded above by some constant since they admit a limit as n tends to infinity: Z ∞ Jn → t −α e−t cos θ dt. 1

The condition on θ that 0 < θ < π/2 precisely ensures convergence of the integral. Thus, globally, on the part γ2 of the contour, we have | f n(2) | = O(n α−1 ). (4)

A similar bound holds for f n relative to γ4 . (3) Outer circle (γ3 ). There, f (z) is bounded while z −n is of the order of r −n . (3) Thus, the integral f n is exponentially small.

392

VI. SINGULARITY ANALYSIS OF GENERATING FUNCTIONS

In summary, each of the four integrals of the split contour contributes O(n α−1 ). The statement of part (i) of the theorem thus follows, when β = 0. Entirely similar bounding techniques cover the case of logarithmic factors (β 6= 0). (ii) An adaptation of the proof shows that o(.) error terms may be translated similarly. All that is required is a further break-up of the rectilinear part at a distance log2 n/n from 1 (see the discussion surrounding Equation (20), p. 383 or [248] for details). An immediate corollary of Theorem VI.3 is the possibility of transferring asymptotic equivalence from singular forms to coefficients: Corollary VI.1 (sim–transfer). Assume that f (z) is 1–analytic and f (z) ∼ (1 − z)−α ,

as z → 1,

z ∈ 1,

with α 6∈ {0, −1, −2, · · · }. Then, the coefficients of f satisfy [z n ] f (z) ∼

n α−1 . Ŵ(α)

Proof. It suffices to observe that, with g(z) = (1 − z)−α , one has f (z) ∼ g(z)

iff

f (z) = g(z) + o(g(z)),

then apply Theorem VI.1 to the first term, and Theorem VI.3 (little-oh transfer) to the remainder.

VI.9. Transfer of nearly polynomial functions. Let f (z) be 1–analytic and satisfy the singular expansion f (z) ∼ (1 − z)r , where r ∈ Z≥0 . Then, f n = o(n −r −1 ). [This is a direct consequence of the little-oh transfer.]

VI.10. Transfer of large negative exponents. The 1–analyticity condition can be weakened for functions that are large at their singularity. Assume that f (z) is analytic in the open disc |z| < 1, and that in the whole of the open disc it satisfies Then, provided α > 1, one has

f (z) = O((1 − z)−α ).

[z n ] f (z) = O(n α−1 ).

[Hint. Integrate on the circle of radius 1 − n1 ; see also [248].]

VI. 4. The process of singularity analysis In Sections VI. 2 and VI. 3, we have developed a collection of statements granting us the existence of correspondences between properties of a function f (z) singular at an isolated point (z = 1) and the asymptotic behaviour of its coefficients f n = [z n ] f (z). Using the symbol ‘−→’ to represent such a correspondence4 , we 4 The symbol “H⇒” represents an unconditional logical implication and is accordingly used in this

book to represent the systematic correspondence between combinatorial specifications and generating function equations. In contrast, the symbol ‘−→’ represents a mapping from functions to coefficients, under suitable analytic conditions, like those of Theorems VI.1–VI.3.

VI. 4. THE PROCESS OF SINGULARITY ANALYSIS

393

can summarize some of our results relative to the scale {(1 − z)−α , α ∈ C \ Z≤0 } as follows: n α−1 −α f (z) = (1 − z) −→ f = + · · · (Theorem VI.1) n Ŵ(α) f (z) = O((1 − z)−α ) −→ f n = O(n α−1 ) (Theorem VI.3 (i)) f (z) = o((1 − z)−α ) f (z) ∼ (1 − z)−α

−→

f n = o(n α−1 )

−→

fn ∼

(Theorem VI.3 (ii))

n α−1

(Corollary VI.1). Ŵ(α) The important requirement is that the function should have an isolated singularity (the condition of 1–analyticity) and that the asymptotic property of the function near its singularity should be valid in an area of the complex plane extending beyond the disc of convergence of the original series, (in a 1–domain). Extensions to logarithmic powers and special cases like α ∈ Z≤0 are also, as we know, available. We let S denote the set of such singular functions: 1 1 1 ≡ L(z). (29) S = (1 − z)−α λ(z)β α, β ∈ C , λ(z) := log z 1−z z At this stage, we thus have available tools by which, starting from the expansion of a function at its singularity, also called singular expansion, one can justify the termby-term transfer from an approximation of the function to an asymptotic estimate of the coefficients5. We state the following theorem. Theorem VI.4 (Singularity analysis, single singularity). Let f (z) be function analytic at 0 with a singularity at ζ , such that f (z) can be continued to a domain of the form ζ · 10 , for a 1–domain 10 , where ζ · 10 is the image of 10 by the mapping z 7→ ζ z. Assume that there exist two functions σ, τ , where σ is a (finite) linear combination of functions in S and τ ∈ S, so that f (z) = σ (z/ζ ) + O (τ (z/ζ ))

as

z→ζ

Then, the coefficients of f (z) satisfy the asymptotic estimate

in ζ · 10 .

f n = ζ −n σn + O(ζ −n τn⋆ ),

where σn = [z n ]σ (z) has its coefficients determined by Theorems VI.1, VI.2 and τn⋆ = n a−1 (log n)b , if τ (z) = (1 − z)−a λ(z)b . We observe that the statement is equivalent to τn⋆ = [z n ]τ (z), except when a ∈ Z≤0 , where the 1/ Ŵ(a) factor should be omitted. Also, generically, we have τn⋆ = o(σn ), so that orders of growth of functions at singularities are mapped to orders of growth of coefficients. Proof. The normalized function g(z) = f (z/ζ ) is singular at 1. It is 1–analytic and satisfies the relation g(z) = σ (z) + O(τ (z)) as z → 1 within 10 . Theorem VI.3, (i) (the big-Oh transfer) applies to the O-error term. The statement follows finally since [z n ] f (z) = ζ −n [z n ]g(z). 5 Functions with a singularity of type (1 − z)−α , possibly with logarithmic factors, are sometimes called algebraic–logarithmic.

394

VI. SINGULARITY ANALYSIS OF GENERATING FUNCTIONS

Let f (z) be a function analytic at 0 whose coefficients are to be asymptotically analysed. 1. Preparation. This consists in locating dominant singularities and checking analytic continuation. 1a. Locate singularities. Determine the dominant singularities of f (z) (assumed not to be entire). Check that f (z) has a single singularity ζ on its circle of convergence. 1b. Check continuation. Establish that f (z) is analytic in some domain of the form ζ 10 . 2. Singular expansion. Analyse the function f (z) as z → ζ in the domain ζ · 10 and determine in that domain an expansion of the form f (z) = σ (z/ζ ) + O(τ (z/ζ ))

with

z→1

τ (z) = o(σ (z)).

For the method to succeed, the functions σ and τ should belong to the standard scale of functions S = {(1 − z)−α λ(z)β }, with λ(z) := z −1 log(1 − z)−1 . 3. Transfer Translate the main term term σ (z) using the catalogues provided by TheoremsVI.1 and VI.2. Transfer the error term (Theorem VI.3) and conclude that [z n ] f (z) = ζ −n σn + O ζ −n τn⋆ , n→+∞

where σn = [z n ]σ (z) and τn⋆ = [z n ]τ (z) provided the corresponding exponent α 6∈ Z≤0 (otherwise, the factor 1/ Ŵ(α) = 0 should be dropped).

Figure VI.7. A summary of the singularity analysis process (single dominant singularity).

The statement of Theorem VI.4 can be concisely expressed by the correspondence: (30)

f (z) = σ (z/ζ ) + O (τ (z/ζ )) z→1

−→

f n = ζ −n σn + O(ζ −n τn⋆ ). n→∞

The conditions of analytic continuation and validity of the expansion in a 1–domain are essential. Similarly, we have (31)

f (z) = σ (z/ζ )) + o (τ (z/ζ )) z→1

−→

f n = ζ −n σn + o(ζ −n τn⋆ ), n→∞

as a simple consequence of Theorem VI.3, part (ii) (little-oh transfer). The mappings (30) and (31) supplemented by the accompanying analysis constitute the heart of the singularity analysis process summarized in Figure VI.7. Many of the functions commonly encountered in analysis are found to be 1– √ analytic. This fact results from the property of the elementary functions (such as , log, tan) to be continuable to larger regions than what their expansions at 0 imply, as well as to the rich set of composition properties that analytic functions satisfy. Furthermore, asymptotic expansions at a singularity initially determined along the real axis by elementary real analysis often hold in much wider regions of the complex plane. The singularity analysis process is then likely to be applicable to a large number of generating functions that are provided by the symbolic method—most notably the iterative structures described in Section IV. 4 (p. 249). In such cases, singularity analysis greatly refines the exponential growth estimates obtained in Theorem IV.8

VI. 4. THE PROCESS OF SINGULARITY ANALYSIS

395

(p. 251). The condition is that singular expansions should be of a suitably moderate6 growth. We illustrate this situation now by treating combinatorial generating functions obtained by the symbolic methods of Chapters I and II, for which explicit expressions are available. Example VI.2. Asymptotics of 2–regular graphs. This example completes the discussion of Example VI.1, p. 379 relative to the EGF 2

R(z) =

e−z/2−z /4 . √ 1−z

We follow step by step the singularity analysis process, as summarized in Figure VI.7. 2

1. Preparation. The function R(z) being the product of e−z/2−z /4 (that is entire) and of (1 − z)−1/2 (that is analytic in the unit disc) is itself analytic in the unit disc. Also, since (1 − z)−1/2 is 1–analytic (it is well-defined and analytic in the complex plane slit along R≥1 ), R(z) is itself 1–analytic, with a singularity at z = 1. 2. Singular expansion. The asymptotic expansion of R(z) near z = 1 is obtained starting 2 from the standard (analytic) expansion of e−z/2−z /4 at z = 1, 2 e−3/4 e−3/4 e−z/2−z /4 = e−3/4 + e−3/4 (1 − z) + (1 − z)2 − (1 − z)3 + · · · . 4 12

The factor (1 − z)−1/2 is its own asymptotic expansion, clearly valid in any 1–domain. Performing the multiplication yields a complete expansion, (32)

√ e−3/4 e−3/4 e−3/4 + e−3/4 1 − z + R(z) ∼ √ (1 − z)3/2 − (1 − z)5/2 + · · · , 4 12 1−z

out of which terminating forms, with an O–error term, can be extracted. 3. Transfer. Take for instance the expansion of (32) limited to two terms plus an error term. The singularity analysis process allows the transfer of (32) to coefficients, which we can present in tabular form as follows: R(z) e−3/4 √

1

1−z √ + e−3/4 1 − z + O((1 − z)3/2 )

cn ≡ [z n ]R(z) e−3/4 1 n − 1/2 1 + · · · ∼ √ e−3/4 1− + −1/2 8n πn 128n 2 −3/4 −e n − 3/2 3 −3/4 ∼ √ +e 1+ + ··· −3/2 8n 2 π n3 1 . +O n 5/2

Terms are then collected with expansions suitably truncated to the coarsest error term, so that here a three-term expansion results. In the sequel, we shall no longer need to detail such computations and we shall content ourselves with putting in parallel the function’s expansion and the coefficient’s expansion, as in the following correspondence: √ e−3/4 e−3/4 5e−3/4 1 R(z) = √ . −→ cn = √ − √ +e−3/4 1 − z+O (1 − z)3/2 +O π n 8 π n3 n 5/2 1−z 6 For functions with fast growth at a singularity, the saddle-point method developed in Chapter VIII

becomes effectual.

396

VI. SINGULARITY ANALYSIS OF GENERATING FUNCTIONS

√ (1) (2) Here is a numerical check. Set cn := e−3/4 / π n and let cn represent the sum of the first two terms of the expansion of cn . One finds: n (1)

n!cn (2) n!cn n!cn

5

50

500

14.30212 12.51435 12

1.1462888618 · 1063 1.1319602511 · 1063 1.1319677968 · 1063

1.4542120372 · 101132 1.4523942721 · 101132 1.4523943224 · 101132

Clearly, a complete asymptotic expansion in descending powers of n can be obtained in this way. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Example VI.3. Asymptotics of unary–binary trees and Motzkin numbers. Unary–binary trees are unlabelled plane trees that admit the specification and OGF: √ 1 − z − (1 + z)(1 − 3z) U = Z(1 + U + U × U) H⇒ U (z) = . 2z (See Note I.39 (p. 68) and Subsection V. 4 (p. 318) for the lattice path version.) The GF U (z) is singular at z = −1 and z = 1/3, the dominant singularity being at z = 1/3. By branching properties of the square-root function, U (z) is analytic in a 1–domain like the one depicted below:

−1

0

1 3

Around the point 1/3, a singular expansion is obtained by multiplying (1 − 3z)1/2 and the analytic expansion of the factor (1 + z)1/2 /(2z). The singularity analysis process then applies and yields automatically: r √ 3 U (z) = 1 − 31/2 1 − 3z + O((1 − 3z)) −→ Un = 3n + O(3n n −2 ). 4π n 3 Further terms in the singular expansion of U (z) at z = 1/3 provide additional terms in the asymptotic expression of the Motzkin numbers Un ; for instance, the form r 1 3 n 1 − 15 + 505 − 8085 + 505659 + O Un = 3 3 2 3 4 16 n 4π n 512 n 8192 n 524288n n5 results from an expansion of U (z) till O((1 − 3z)11/2 ). The approximation provided by the first three terms is quite good: for n = 10, it estimates f 10 = 835. with an error less than 1. . . . .

VI.11. The population of Noah’s Ark. The number of one-source directed lattice animals (pyramids, Example I.18, p. 80) satisfies ! r 3n 1 1 1+z 1 n −1 = √ +O Pn ≡ [z ] . 1− 2 1 − 3z 16n n2 3π n

VI. 4. THE PROCESS OF SINGULARITY ANALYSIS

The expected size of the base of a random animal in An is ∼ number of animals with a compact source of size k?

q

397

4n 27π . What is the asymptotic

Example VI.4. Asymptotics of children’s rounds. Stanley [550] has introduced certain combinatorial configurations that he has nicknamed “children’s rounds”: a round is a labelled set of directed cycles, each of which has a centre attached. The specification and EGF are 1 R = S ET(Z ⋆ C YC(Z)) H⇒ R(z) = exp z log = (1 − z)−z . 1−z

The function R(z) is analytic in the C-plane slit along R≥1 , as is seen by elementary properties of the composition of analytic functions. The singular expansion at z = 1 is then mapped to an expansion for the coefficients: 1 + log(1 − z) + O((1 − z)1/2 ) 1−z A more detailed analysis yields R(z) =

−→

[z n ]R(z) = 1 −

1 1 [z n ]R(z) = 1 − − 2 (log n + γ − 1) + O n n

log2 n n3

!

1 + O(n −3/2 ). n

,

and an expansion to any order can be easily obtained. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

VI.12. The asymptotic shape of the rounds numbers. A complete asymptotic expansion has the form [z n ]R(z) ∼ 1 −

X P j (log n) , nj j≥1

where P j is a polynomial of degree j − 1. (The coefficients of P j are rational combinations of powers of γ , ζ (2), . . . , ζ ( j − 1).) The successive terms in this expansion are easily obtained by a computer algebra program. Example VI.5. Asymptotics of coefficients of an elementary function. Our final example is meant to show the way rather arbitrary compositions of basic functions can be treated by singularity analysis, much in the spirit of Section IV. 4, p. 249. Let C = Z ⋆ S EQ(C) be the class of general labelled plane trees. Consider the labelled class defined by substitution F = C ◦ C YC(C YC(Z))

H⇒

F(z) = C(L(L(z))).

√ 1 . Combinatorially, F is the class of trees There, C(z) = 21 (1 − 1 − 4z) and L(z) = log 1−z in which nodes are replaced by cycles of cycles, a rather artificial combinatorial object, and s 1 1 F(z) = 1 − 1 − 4 log . 1 2 1 − log 1−z

The problem is first to locate the dominant singularity of F(z), then to determine its nature, which can be done inductively on the structure of F(z). The dominant positive singularity ρ of F(z) satisfies L(L(ρ)) = 1/4 and one has −1/4 −1 . ρ = 1 − ee = 0.198443, given that C(z) is singular at 1/4 and L(z) has positive coefficients. Since L(L(z)) is analytic at ρ, a local expansion of F(z) is obtained next by composition of the singular expansion of C(z) at 1/4 with the standard Taylor expansion of L(L(z)) at ρ. We find 1 C1 ρ −n+1/2 1 1/2 3/2 n F(z) = −C1 (ρ−z) +O (ρ − z) 1+O , −→ [z ]F(z) = √ 3 2 n 2 πn

398

VI. SINGULARITY ANALYSIS OF GENERATING FUNCTIONS 5

1 −1/4

with C1 = e 8 − 2 e

. = 1.26566. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

VI.13. The asymptotic number of trains. Combinatorial trains were introduced in Section IV. 4 (p. 249) as a way to exemplify the power of complex asymptotic methods. One finds that, at its dominant singularity ρ, the EGF Tr(z) is of the form Tr(z) ∼ C/(1 − z/ρ), and, by singularity analysis, [z n ]Tr(z) ∼ 0.11768 31406 15497 · 2.06131 73279 40138n . (This asymptotic approximation is good to 15 significant digits for n = 50, in accordance with the fact that the dominant singularity is a simple pole.)

VI. 5. Multiple singularities The previous section has described in detail the analysis of functions with a single dominant singularity. The extension to functions that have finitely many (by necessity isolated) singularities on their circle of convergence follows along entirely similar lines. It parallels the situation of rational and meromorphic functions in Chapter IV (p. 263) and is technically simple, the net result being: In the case of multiple singularities, the separate contributions from each of the singularities, as given by the basic singularity analysis process, are to be added up. As in (29), p. 393, we let S be the standard scale of functions singular at 1, namely 1 1 S = (1 − z)−α λ(z)β α, β ∈ C , λ(z) := log . z 1−z

Theorem VI.5 (Singularity analysis, multiple singularities). Let f (z) be analytic in |z| < ρ and have a finite number of singularities on the circle |z| = ρ at points ζ j = ρeiθ j , for j = 1 . . r . Assume that there exists a 1–domain 10 such that f (z) is analytic in the indented disc r \ (ζ j · 10 ), D= j=1

with ζ · 10 the image of 10 by the mapping z 7→ ζ z. Assume that there exists r functions σ1 , . . . , σr , each a linear combination of elements from the scale S, and a function τ ∈ S such that f (z) = σ j (z/ζ j ) + O τ (z/ζ j ) as z → ζ j in D. Then the coefficients of f (z) satisfy the asymptotic estimate fn = [z n ]σ

r X j=1

ζ j−n σ j,n + O ρ −n τn⋆ ,

where each σ j,n = j (z) has its coefficients determined by Theorems VI.1, VI.2 and τn∗ = n a−1 (log n)b , if τ (z) = (1 − z)−a λ(z)b .

A function analytic in a domain like D is sometimes said to be star-continuable, a notion that naturally generalizes 1–analyticity for functions with several dominant singularities. Furthermore, a similar statement holds with o–error terms replacing Os.

VI. 5. MULTIPLE SINGULARITIES

399

γ

D: 0

0

Figure VI.8. Multiple singularities (r = 3): analyticity domain (D, left) and composite integration contour (γ , right).

Proof. Just as in the case of a single singularity, the proof bases itself on Cauchy’s coefficient formula Z dz f n = [z n ] f (z) n+1 , z γ where a composite contour γ depicted on Figure VI.8 is used. Estimates on each part of the contour obey exactly the same principles as in the proofs of Theorems VI.1– VI.3. Let γ ( j) be the open loop around ζ j that comes from the outer circle, winds about ζ j and joins again the outer circle; let r be the radius of the outer circle. (i) The contribution along the arcs of the outer circle is O(r −n ), that is, exponentially small. (ii) The contribution along the loop γ (1) (say) separates into Z dz 1 f (z) n+1 = I ′ + I ′′ 2iπ γ (1) Z z Z 1 1 dz dz I ′ := σ1 (z/ζ1 ) n+1 , I ′′ := ( f (z) − σ1 (z/ζ1 )) n+1 . 2iπ γ (1) 2iπ γ (1) z z

The quantity I ′ is estimated by extending the open loop to infinity by the same method as in the proof of Theorems VI.1 and VI.2: it is found to equal ζ1−n σ1,n plus an exponentially small term. The quantity I ′′ , corresponding to the error term, is estimated by the same bounding technique as in the proof of Theorem VI.3 and is found to be O(ρ −n τn⋆ ).

Collecting the various contributions completes the proof of the statement.

Theorem VI.5 expresses that, in the case of multiple singularities, each dominant singularity can be analysed separately; the singular expansions are then each transferred to coefficients, and the corresponding asymptotic contributions are finally collected. Two examples illustrating the process follow. Example VI.6. An artificial example. Let us demonstrate the modus operandi on the simple function ez (33) g(z) = p . 1 − z2

400

VI. SINGULARITY ANALYSIS OF GENERATING FUNCTIONS

There are two singularities at z = +1 and z = −1, with

e e−1 g(z) ∼ √ √ z → +1 and g(z) ∼ √ √ z → −1. 2 1−z 2 1+z The function is clearly star-continuable with the singular expansions being valid in an indented disc. We have e e [z n ] √ √ ∼ √ 2π n 2 1−z

and

e−1 e−1 (−1)n [z n ] √ √ . ∼ √ 2π n 2 1+z

To obtain the coefficient [z n ]g(z), it suffices to add up these two contributions (by Theorem VI.5), so that 1 [z n ]g(z) ∼ √ [e + (−1)n e−1 ]. 2π n If expansions at +1 (respectively −1) are written with an error term, which is of the form O((z − 1)1/2 ) (respectively, O((z + 1)1/2 ), there results an estimate of the coefficients gn = [z n ]g(z), which can be put under the form cosh(1) sinh(1) g2n = √ + O n −3/2 , g2n+1 = √ + O n −3/2 . πn πn

This makes explicit the dependency of the asymptotic form of gn on the parity of the index n. Clearly a full asymptotic expansion can be obtained. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Example VI.7. Permutations with cycles of odd length. Consider the specification and EGF r 1+z 1 1+z = log . F = S ET(C YCodd (Z)) H⇒ F(z) = exp 2 1−z 1−z

The singularities of f are at z = +1 and z = −1, the function being obviously star-continuable. By singularity analysis (Theorem VI.5), we have automatically: 1/2 √2 + O (1 − z)1/2 (z → 1) 21/2 F(z) = −→ [z n ]F(z) = √ + O n −3/2 . 1 − z πn O (1 + z)1/2 (z → −1)

For the next asymptotic order, the singular expansions √ 21/2 − 2−3/2 1 − z + O((1 − z)3/2 ) √ F(z) = 1 −√ z −1/2 1 + z + O((1 + z)3/2 ) 2

(z → 1) (z → −1)

yield

21/2 (−1)n 2−3/2 [z n ]F(z) = √ − + O(n −5/2 ). √ πn π n3 This example illustrates the occurrence of singularities that have different weights, in the sense of being associated with different exponents. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

The discussion of multiple dominant singularities ties well with the earlier discussion of Subsection IV. 6.1, p. 263. In the periodic case where the dominant singularities are at roots of unity, different regimes manifest themselves cyclically depending on congruence properties of the index n, like in the two examples above. When the dominant singularities have arguments that are not commensurate to π (a comparatively rare situation), irregular fluctuations appear, in which case the situation is

VI. 6. INTERMEZZO: FUNCTIONS AMENABLE TO SINGULARITY ANALYSIS

401

similar to what was already discussed, regarding rational and meromorphic functions, in Subsection IV. 6.1. VI. 6. Intermezzo: functions amenable to singularity analysis Let us say that a function is amenable to singularity analysis, or SA for short, if its satisfies the conditions of singularity analysis, as expressed by Theorem VI.4 (single dominant singularity) or Theorem VI.5 (multiple dominant singularities). The property of being of SA is preserved by several basic operations of analysis: we have already seen this feature in passing, when determining singular expansions of functions obtained by sums, products, or compositions in Examples VI.2–VI.5. As a starting example, it is easily recognized that the assumptions of 1–analyticity for two functions f (z), g(z) accompanied by the singular expansions f (z) ∼ c(1 − z)−α , z→1

g(z) ∼ d(1 − z)−δ , z→1

and the condition α, δ 6∈ Z≤0 imply for the coefficients of the sum n α−1 α>δ c Ŵ(α) α−1 n [z n ] ( f (z) + g(z)) ∼ (c + d) α = δ, c + d 6= 0 Ŵ(α) δ−1 n d α < δ. Ŵ(δ) Similarly, for products, we have

[z n ] ( f (z)g(z)) ∼ cd

n α+δ−1 , Ŵ(α + δ)

provided α + δ 6∈ Z≤0 . The simple considerations above illustrate the robustness of singularity analysis. They also indicate that properties are easy to state in the generic case where no negative integral exponents are present. However, if all cases are to be covered, there can easily be an explosion of the number of particular situations, which may render somewhat clumsy the enunciation of complete statements. Accordingly, in what follows, we shall largely confine ourselves to generic cases, as long as these suffice to develop the important mathematical technique at stake for each particular problem. In the remainder of this chapter, we proceed to enlarge the class of functions recognized to be of SA, keeping in mind the needs of analytic combinatorics. The following types of functions are treated in later sections. (i) Inverse functions (Section VI. 7). The inverse of an analytic function is, under mild conditions, of SA type. In the case of functions attached to simple varieties of trees (corresponding to the inversion of y/φ(y)), the singular expansion invariably has an exponent of 21 attached to it (a square-root singularity). This applies in particular to the Cayley tree function, in terms of which many combinatorial structures and parameters can be analysed.

402

VI. SINGULARITY ANALYSIS OF GENERATING FUNCTIONS

(ii) Polylogarithms (Section VI. 8). These functions are the generating functions of simple arithmetic sequences such as (n θ ) for an arbitrary θ ∈ C. The fact that polylogarithms are SA opens the possibility of estimating a large number of sums, which involve √ both combinatorial terms (e.g., binomial coefficients) and elements like n and log n. Such sums appear recurrently in the analysis of cost functionals of combinatorial structures and algorithms. (iii) Composition (Section VI. 9). The composition of SA functions often proves to be itself SA This fact has implications for the analysis of composition schemas and makes possible a broad extension of the supercritical sequence schema treated in Section V. 2, (p. 293). (iv) Differentiation, integration, and Hadamard products (Section VI. 10). These are three operations on analytic functions that preserve the property for a function to be SA. Applications are given to tree recurrences and to multidimensional walk problems. A main theme of this book is that elementary combinatorial classes tend to have generating functions whose singularity structure is strongly constrained—in most cases, singularities are isolated. The singularity analysis process is then a prime technique for extracting asymptotic information from such generating functions. VI. 7. Inverse functions Recursively defined structures lead to functional equations whose solutions may often be analysed locally near singularities. An important case is the one of functions defined by inversion. It includes the Cayley tree function as well as all generating functions associated to simple varieties of trees (Subsections I. 5.1 (p. 65), II. 5.1 (p. 126), and III. 6.2 (p. 193)). A common pattern in this context is the appearance of singularities of the square-root type, which proves to be universal among a broad class of problems involving trees and tree-like structures. Accordingly, by singularity analysis, the square-root singularity induces subexponential factors of the asymptotic form n −3/2 in expansions of coefficients—we shall further develop this theme in Chapter VII, pp. 452–493. Inverse functions. Singularities of functions defined by inversion have been located in Subsection IV. 7.1 (p. 275) and our treatment will proceed from there. The goal is to estimate the coefficients of a function defined implicitly by an equation of the form y(z) . (34) y(z) = zφ(y(z)) or equivalently z= φ(y(z)) The problem of solving (34) is one of functional inversion: we have seen (Lemmas IV.2 and IV.3, pp. 275–277) that an analytic function admits locally an analytic inverse if and only if its first derivative is non-zero. We operate here under the following assumptions: Condition (H1 ). The function φ(u) is analytic at u = 0 and satisfies (35)

φ(0) 6= 0,

[u n ]φ(u) ≥ 0,

φ(u) 6≡ φ0 + φ1 u.

VI. 7. INVERSE FUNCTIONS

403

(As a consequence, the inversion problem is well defined around 0. The nonlinearity of φ only excludes the case φ(u) = φ0 + φ1 u, corresponding to y(z) = φ0 z/(1 − φ1 z).) Condition (H2 ). Within the open disc of convergence of φ at 0, |z| < R, there exists a (then necessarily unique) positive solution to the characteristic equation: (36)

∃τ, 0 < τ < R,

φ(τ ) − τ φ ′ (τ ) = 0.

(Existence is granted as soon as lim xφ ′ (x)/φ(x) > 1 as x → R − ,with R the radius of convergence of φ at 0; see Proposition IV.5, p. 278.) Then (by Proposition IV.5, p. 278), the radius of convergence of y(z) is the corresponding positive value ρ of z such that y(ρ) = τ , that is to say, ρ=

(37)

τ 1 = ′ . φ(τ ) φ (τ )

We start with a calculation indicating in a plain context the occurrence of a square-root singularity. Example VI.8. A simple analysis of the Cayley tree function. The situation corresponding to the function φ(u) = eu , so that y(z) = ze y(z) (defining the Cayley tree function T (z)), is typical of general analytic inversion. From (36), the radius of convergence of y(z) is ρ = e−1 corresponding to τ = 1. The image of a circle in the y–plane, centred at the origin and having radius r < 1, by the function ye−y is a curve of the z–plane that properly contains the circle |z| = r e−r (see Figure VI.9) as φ(y) = e y , which has non-negative coefficients, satisfies for all θ ∈ [−π, +π ], φ(r eiθ ) ≤ φ(r )

the inequality being strict for all θ 6= 0. The following observation is the key to analytic continuation: Since the first derivative of y/φ(y) vanishes at 1, the mapping y 7→ y/φ(y) is angle-doubling, so that the image of the circle of radius 1 is a curve C that has a cusp at ρ = e−1 . (See Figure VI.9; Notes VI.18 and 19 provide interesting generalizations.) This geometry indicates that the solution of z = ye−y is uniquely defined for z inside C, so that y(z) is 1–analytic (see the proof of Theorem VI.6 below). A singular expansion for y(z) is then derived from reversion of the power series expansion of z = ye−y . We have (38)

ye−y = e−1 −

1 1 e−1 (y − 1)2 + (y − 1)3 − (y − 1)4 + · · · . 2e 3e 8

Observe both the absence of a linear term and the presence of a quadratic term (boxed). Then, solving z = ye−y for y gives y−1=

√

2 2(1 − ez)1/2 + (1 − ez) + O((1 − ez)3/2 ), 3

where the square root arises precisely from inversion of the quadratic term. (A full expansion can furthermore be obtained.) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

404

VI. SINGULARITY ANALYSIS OF GENERATING FUNCTIONS

1

1

0.5

0.5

-1

y:

-0.5

0

0.5 0

1

−→

0

z:

-1.5

-1

-0.5

0

0.5

-0.5

-0.5

-1

-1

1

Figure VI.9. The images of concentric circles by the mapping y 7→ z = ye−y . It is seen that y 7→ z = ye−y is injective on |y| ≤ 1 with an image extending beyond the circle |z| = e−1 [in grey], so that the inverse function y(z) is analytically continuable in a 1–domain around z = e−1 . Since the direct mapping ye−y is quadratic at 1 (with value e−1 , see (38)), the inverse function has a square-root singularity at e−1 (with value 1).

Analysis of inverse functions. The calculation of Example VI.8 now needs to be extended to the general case, y = zφ(y). This involves three steps: (i) all the dominant singularities are to be located; (ii) analyticity of y(z) in a 1–domain must be established; (iii) the singular expansion, obtained formally so far and involving a square-root singularity, needs to be determined. Step (i) requires a special discussion and is related to periodicities. A basic example like φ(u) = 1 + u 2 (binary trees), for which y(z) =

1−

√

1 − 4z 2 , 2z

shows that y(z) may have several dominant singularities—here, two conjugate singularities at − 12 and + 12 . The conditions for this to happen are related to our discussion of periodicities in Definition IV.5, p. 266. As a consequence of this definition, φ(u), which satisfies φ(0) 6= 0, is p–periodic if φ(u) = g(u p ) for some power series g (see p. 266) and p ≥ 2; it is aperiodic otherwise. An elementary argument developed in Note VI.17, p. 407, shows that the aperiodicity assumption entails no loss of analytic generality (periodicity does not occur for y(z) unless φ(u) is itself periodic, a case which, in addition, turns out to be reducible to the aperiodic situation). Theorem VI.6 (Singular Inversion). Let φ be a nonlinear function satisfying the conditions (H1 ) and (H2 ) of Equations (35) and (36), and let y(z) be the solution of y = zφ(y) satisfying y(0) = 0. Then, the quantity ρ = τ/φ(τ ) is the radius of convergence of y(z) at 0 (with τ the root of the characteristic equation), and the singular

VI. 7. INVERSE FUNCTIONS

405

expansion of y(z) near ρ is of the form X p y(z) = τ − d1 1 − z/ρ + (−1) j d j (1 − z/ρ) j/2 ,

d1 :=

j≥2

s

2φ(τ ) , φ ′′ (τ )

with the d j being some computable constants. Assume that, in addition, φ is aperiodic7. Then, one has s ! ∞ X φ(τ ) ρ −n ek n 1+ [z ]y(z) ∼ , √ 2φ ′′ (τ ) π n 3 nk k=1

for a family ek of computable constants. Proof. Proposition IV.5, p. 278, shows that ρ is indeed the radius of convergence of y(z). The Singular Inversion Lemma (Lemma IV.3, p. 277) also shows that y(z) can be continued to a neighbourhood of ρ slit along the ray R≥ρ . The singular expansion at ρ is determined as in Example VI.8. Indeed, the relation between z and y, in the vicinity of (z, y) = (ρ, τ ), may be put under the form τ y , (39) ρ − z = H (y), where H (y) := − φ(τ ) φ(y) the function H (y) in the right-hand side being such that H (τ ) = H ′ (τ ) = 0. Thus, the dependency between y and z is locally a quadratic one: ρ−z =

1 1 ′′ H (τ )(y − τ )2 + H ′′′ (τ )(y − τ )3 + · · · . 2! 3!

When this relation is locally inverted: a square-root appears: r h i √ H ′′ (τ ) − ρ−z = (y − τ ) 1 + c1 (y − τ ) + c2 (y − τ )2 + ... . 2 √ The determination with a − should be chosen there as y(z) increases to τ − as z → − ρ . This implies, by solving with respect to y − τ , the relation y − τ ∼ −d1⋆ (ρ − z)1/2 + d2⋆ (ρ − z) − d3⋆ (ρ − z)3/2 + · · · , p where d1⋆ = 2/H ′′ (τ ) with H ′′ (τ ) = τ φ ′′ (τ )/φ(τ )2 . The singular expansion at ρ results. It now remains to exclude the possibility for y(z) to have singularities other than ρ on the circle |z| = ρ, in the aperiodic case. Observe that y(ρ) is well defined (in fact y(ρ) = τ ), so that the series representing y(z) converges at ρ as well as on the whole circle (given positivity of the coefficients). If φ(z) is aperiodic, then so is y(z). Consider any point ζ such that |ζ | = ρ and ζ 6= ρ and set η = y(ζ ). We then have |η| < τ (by the Daffodil Lemma: Lemma IV.1, p. 266). The function y(z) is analytic 7If φ has maximal period p, then one must restrict n to n ≡ 1 mod p; in that case, there is an extra

factor of p in the estimate of yn : see Note VI.17 and Equation (40).

406

VI. SINGULARITY ANALYSIS OF GENERATING FUNCTIONS

Type

φ(u)

binary

(1 + u)2

unary–binary

1 + u + u2

q 1 − 3 13 − z + · · ·

general

(1 − u)−1

1 − 2

Cayley

eu

1−

singular expansion of y(z) q 1 − 4 14 − z + · · · q

coefficient [z n ]y(z)

1 −z 4

√ p 2e e−1 − z + · · ·

4n + O(n −5/2 ) √ π n3 3n+1/2 + O(n −5/2 ) √ 2 π n3 4n−1 + O(n −5/2 ) √ π n3 en + O(n −5/2 ) √ 2π n 3

Figure VI.10. Singularity analysis of some simple varieties of trees.

at ζ by virtue of the Analytic Inversion Lemma (Lemma IV.2, p. 275) and the property that d y 6= 0. dy φ(y) y=η

(This last property is derived from the fact that the numerator of the quantity on the left, φ(η) − ηφ ′ (η) = φ0 − φ2 η2 − 2φ3 η3 − 3φ4 η4 − · · · ,

cannot vanish, by the triangle inequality since |η| < τ .) Thus, under the aperiodicity assumption, y(z) is analytic on the circle |z| = ρ punctured at ρ. The expansion of the coefficients then results from basic singularity analysis. Figure VI.10 provides a table of the most basic varieties of simple trees and the corresponding asymptotic estimates. With Theorem VI.6, we now have available a powerful method that permits us to analyse not only implicitly defined functions but also expressions built upon them. This fact will be put to good use in Chapter VII, when analysing a number of parameters associated to simple varieties of trees.

VI.14. All kinds of graphs. In relation with the classes of graphs listed in Figure II.14, p. 134, one has the following correspondence between an EGF f (z) and the asymptotic form of n![z n ] f (z): function:

2 e T −T /2

coefficient:

e1/2 n n−2

log

1 1−T

1√ 2πn n−1/2 2

√

1 1−T

C1 n n−1/4

1 (1 − T )m C2 n n+(m−1)/2

(m ∈ Z≥1 ; C1 , C2 represent computable constants). In this way, the estimates of Subsection II. 5.3, p. 132, are justifiable by singularity analysis.

VI.15. Computability of singular expansions. Define h(w) :=

s

τ/φ(τ ) − w/φ(w) , (τ − w)2

VI. 7. INVERSE FUNCTIONS

407

√ so that y(z) satisfies ρ − z = (τ −y)h(y). The singular expansion of y can then be deduced by Lagrange inversion from the expansion of the negative powers of h(w) at w = τ . This technique yields for instance explicit forms for coefficients in the singular expansion of y = ze y .

VI.16. Stirling’s formula via singularity analysis. The solution to T = ze T analytic at 0 is

the Cayley tree function. It satisfies [z n ] = n n−1 /n! (by Lagrange inversion) and, at the same time, its singularity is known from Theorem VI.6 and Example VI.8. As a consequence: 139 1 en 1 n n−1 + − · · · . 1− ∼ √ + n! 12 n 288 n 2 51840 n 3 2π n 3

Thus Stirling’s formula also results from singularity analysis.

VI.17. Periodicities. Assume that φ(u) = ψ(u p )with ψ analytic at 0 and p ≥ 2. Let y =

y(z) be the root of y = zφ(y). Set Z = z p and let Y (Z ) be the root of Y = Z ψ(Y ) p . One has by construction y(z) = Y (z p )1/ p , given that y p = z p φ(y) p . Since Y (Z ) = Y1 Z +Y2 Z 2 +· · · , we verify that the non-zero coefficients of y(z) are among those of index 1, 1 + p, 1 + 2 p, . . . . If p is chosen maximal, then ψ(u) p is aperiodic. Then Theorem VI.6 applies to Y (Z ): the function Y (Z ) is analytically continuable beyond its dominant singularity at Z = ρ p ; it has a square root singularity at ρ p and no other singularity on |Z | = ρ p . Furthermore, since Y = Z ψ(Y ) p , the function Y (Z ) cannot vanish on |Z | ≤ ρ p , Z 6= 0. Thus, Y (Z )1/ p is √ analytic in |Z | ≤ ρ p , except at ρ p where it has a branch point. All computations done, we find that d1 ρ −n (40) [z n ]y(z) ∼ p · √ when n ≡ 1 (mod p). 2 π n3 The argument also shows that y(z) has p conjugate roots on its circle of convergence. (This is a kind of Perron–Frobenius property for periodic tree functions.)

VI.18. Boundary cases I. The case when τ lies on the boundary of the disc of convergence of φ may lead to asymptotic estimates differing from the usual ρ −n n −3/2 prototype. Without loss of generality, take φ aperiodic to have radius of convergence equal to 1 and assume that φ is of the form (41)

φ(u) = u + c(1 − u)α + o((1 − u)α ),

with

1 < α ≤ 2,

as u tends to 1 within |u| < 1. (Thus, continuation of φ(u) beyond |u| < 1 is not assumed.) The solution of the characteristic equation φ(τ ) − τ φ ′ (τ ) = 0 is then τ = 1. The function y(z) defined by y = zφ(y) is 1–analytic (by a mapping argument similar to the one exemplified by Figure VI.9 and related to the fact that φ “multiplies” angles near 1). The singular expansion of y(z) and the coefficients then satisfy n −1/α−1 . (42) y(z) = 1 − c−1/α (1 − z)1/α + o (1 − z)1/α −→ yn ∼ c−1/α −Ŵ(−1/α) [The case α = 2 was first observed by Janson [350]. Trees with α ∈ (1, 2) have been investigated in connection with stable L´evy processes [180]. The singular exponent α = 3/2 occurs for instance in planar maps (Subsection VII. 8.2, p. 513), so that GFs with coefficients of the form ρ −n n −5/3 would arise, if considering trees whose nodes are themselves maps.]

VI.19. Boundary cases II. Let φ(u) be the probability generating function of a random variable X with mean equal to 1 and such that φn ∼ λn −α−1 , with 1 < α < 2. Then, by a complex version of an Abelian theorem (see, e.g., [69, §1.7] and [232]), the singular expansion (41) holds when u → 1, |u| < 1, within a cone, so that the conclusions of (42) hold in that case. Similarly, if φ ′′ (1) exists, meaning that X has a second moment, then the estimate (42) holds with α = 2, and then coincides with what Theorem VI.6 predicts [350]. (In probabilistic terms, the condition of Theorem VI.6 is equivalent to postulating the existence of exponential moments for the one-generation offspring distribution.)

408

VI. SINGULARITY ANALYSIS OF GENERATING FUNCTIONS

VI. 8. Polylogarithms √ Generating functions involving sequences such as ( n) or (log n) can be subjected to singularity analysis. The starting point is the definition of the generalized polylogarithm, commonly denoted8 by Liα,r , where α is an arbitrary complex number and r a non-negative integer: X zn Liα,r (z) := (log n)r α , n n≥1

The series converges for |z| < 1, so that the function Liα,r is a priori analytic in the unit disc. The quantity Li1,0 (z) is the usual logarithm, log(1 − z)−1 , hence the established name, polylogarithm, assigned to these functions [406]. In what follows, we make use of the abbreviation Liα,0 (z) ≡ Liα (z), so that Li1 (z) ≡ Li1,0 (z) ≡ log(1−z)−1 is the GF of the sequence (1/n). Similarly, Li0,1 is the GF of the sequence √ (log n) and Li−1/2 (z) is the GF of the sequence ( n). Polylogarithms are continuable to the whole of the complex plane slit along the ray R≥1 , a fact established early in the twentieth century by Ford [268], which results from the integral representation (48), p. 409. They are amenable to singularity analysis [223] and their singular expansions involve the Riemann zeta function defined by ζ (s) =

∞ X 1 , ns n=1

for ℜ(s) > 1, and by analytic continuation elsewhere [578]. Theorem VI.7 (Singularities of polylogarithms). For all α ∈ Z and r ∈ Z≥0 , the function Liα,r (z) is analytic in the slit plane C \ R≥1 . For α 6∈ {1, 2, . . .}, there exists an infinite singular expansion (with logarithmic terms when r > 0) given by the two rules: ∞ X (−1) j X (1 − z)ℓ ζ (α − j)w j , w := Liα (z) ∼ Ŵ(1 − α)w α−1 + j! ℓ (43) j≥0 ℓ=1 r ∂ Liα,r (z) = (−1)r Liα (z) (r ≥ 0). ∂αr The expansion of Liα is conveniently described by the composition of two expansions (Figure VI.11, p. 410): the expansion of w = log z at z = 1, namely, w = (1 − z) + 1 2 2 (1 − z) + · · · , is to be substituted inside the formal power series involving powers of w. The exponents of (1− z) involved in the resulting expansion are {α −1, α, . . .}∪ {0, 1, . . .}. For α < 1, the main asymptotic term of Liα,r is, as z → 1, 1 , Liα,r (z) ∼ Ŵ(1 − α)(1 − z)α−1 L(z)r , L(z) := log 1−z 8The notation Li (z) is nowadays well established. It is evocative of the fact that polylogarithms of α

integer order m ≥ 2 are expressible by a logarithmic integral: Z dt (−1)m−1 1 log(1 − xt) logm−2 t Lim,0 (x) = (m − 1)! 0 t R dt (not to be confused with the unrelated “logarithmic integral function” li(z) := 0z log t ; see [3, p. 228]).

VI. 8. POLYLOGARITHMS

409

while, for α > 1, we have Liα,r (z) ∼ (1−)r ζ (r ) (α), since the sum defining Liα,r converges at 1. Proof. The analysis crucially relies on the Mellin transform (see Appendix B.7: Mellin transforms, p. 762). We start with the case r = 0 and consider several ways in which z may approach the singularity 1. Step (i) below describes the main ingredient needed in obtaining the expansion, the subsequent steps being only required for justifying it in larger regions of the complex plane. (i) When z → 1− along the real line. Set w = − log z and introduce X e−nw (44) 3(w) := Liα (e−w ) = . nα n≥1

This is a harmonic sum in the sense of Mellin transform theory, so that the Mellin transform of 3 satisfies (ℜ(s) > max(0, 1 − α)) Z ∞ (45) 3⋆ (s) ≡ 3(w)w s−1 dw = ζ (s + α)Ŵ(s). 0

The function 3(w) can be recovered from the inverse Mellin integral, Z c+i∞ 1 (46) 3(w) = ζ (s + α)Ŵ(s)w −s ds, 2iπ c−i∞

with c taken in the half-plane in which 3⋆ (s) is defined. There are poles at s = 0, −1, −2, . . . due to the Gamma factor and a pole at s = 1 − α due to the zeta function. Take d to be of the form −m − 21 and smaller than 1 − α. Then, a standard residue calculation, taking into account poles to the left of c and based on X 3(w) = Res ζ (s + α)Ŵ(s)w −s s=s (47)

s0 ∈{0,−1,...,−m}∪{1−α}

+

1 2iπ

Z

0

d+i∞

d−i∞

ζ (s + α)Ŵ(s)w −s ds,

then yields a finite form of the estimate (43) of Liα (as w → 0, corresponding to z → 1− ).

(ii) When z → 1− in a cone of angle less than π inside the unit disc. In that case, we observe that the identity in (46) remains valid by analytic continuation, since the integral there is still convergent (this property owes to the fast decay of Ŵ(s) towards ±i∞). Then the residue calculation (47), on which the expansion of 3(w) is based in the real case w > 0, still makes sense. The extension of the asymptotic expansion of Liα within the unit disc is thus granted. (iii) When z tends to 1 vertically. Details of the proof are given in [223]. What is needed is a justification of the validity of expansion (43), when z is allowed to tend to 1 from the exterior of the unit disc. The key to the analysis is a Lindel¨of integral representation of the polylogarithm (Notes IV.8 and IV.9, p. 237), which provides analytic continuation; namely, Z 1/2+i∞ s z π 1 ds. (48) Liα (−z) = − α 2iπ 1/2−i∞ s sin π s

410

VI. SINGULARITY ANALYSIS OF GENERATING FUNCTIONS

Li−1/2 (z) = Li0 (z)

=

X√ nz n

=

zn

≡

n≥1

X

n≥1

X

√ 3 π 1 π 1/2 − + ζ (− ) + O (1 − z) 2 2(1 − z)3/2 8(1 − z)1/2 √

1 −1 1−z

√ L(z) − γ 1 γ −1 − L(z) + + log 2π + O ((1 − z) L(z)) 1−z 2 2 n≥1 r X zn π 1 1√ √ π 1 − z + O (1 − z)3/2 = Li1/2 (z) = + ζ( ) − √ 1−z 2 4 n n≥1 X log n √ √ L(z) − γ − 2 log 2 1 γ π Li1/2,1 (z) = − ζ( ) + + log 8π + · · · √ zn = π √ 2 2 4 n 1−z n≥1 n Xz ≡ L(z) Li1 (z) = n Li0,1 (z)

=

log n z n =

n≥1

Li2 (z)

=

X zn n2

n≥1

=

π2 1 1 − (L(z) + 1)(1 − z) − ( + L(z))(1 − z)2 + · · · 6 4 2

Figure VI.11. Sample expansions of polylogarithms (L(z) := log(1 − z)−1 ).

The proof then proceeds with the analysis of the polylogarithm when z = ei(w−π ) and s = 1/2 + it, the integral (48) being estimated asymptotically as a harmonic integral (a continuous analogue of harmonic sums [614]) by means of Mellin transforms. The extension to a cone with vertex at 1, having a vertical symmetry and angle less than π , then follows by an analytic continuation argument. By unicity of asymptotic expansions (the horizontal cone of parts (i) and (ii) and the vertical cone have a non-empty intersection), the resulting expansion must coincide with the one calculated explicitly in part (i), above. To conclude, regarding the general case r ≥ 0, we may proceed along similar lines, with each log n factor introducing a derivative of the Riemann zeta function, hence a multiple pole at s = 1. It can then be checked that the resulting expansion coincides with what is given by formally differentiating the expansion of Liα a number of times equal to r . (See also Note VI.20 below.) Figure VI.11 provides a table of expansions relative to commonly encountered polylogarithms (the function Li2 is also known as a dilogarithm). Example VI.9 illustrates the use of polylogarithms for establishing a class of asymptotic expansions of which Stirling’s formula appears as a special case. Further uses of Theorem VI.7 will appear in the following sections. Example VI.9. Stirling’s formula, polylogarithms, and superfactorials. One has X log n! z n = (1 − z)−1 Li0,1 (z), n≥1

VI. 9. FUNCTIONAL COMPOSITION

411

to which singularity analysis is applicable. Theorem VI.7 then yields the singular expansion 1 − L(z) + γ − 1 + log 2π 1 L(z) − γ + Li0,1 (z) ∼ + ··· , 1−z 2 1−z (1 − z)2 from which Stirling’s formula reads off: √ 1 log n! ∼ n log n − n + log n + log 2π + · · · . 2 √ (Stirling’s constant log 2π comes out as neatly −ζ ′ (0).) Similarly, define the superfactorial function to be 11 22 · · · n n . One has X 1 Li−1,1 (z), log(12 22 · · · n n )z n = 1−z n≥1

to which singularity analysis is mechanically applicable. The analogue of Stirling’s formula then reads: 11 22 · · · n n

∼

A

=

1 2 1 1 2 1 An 2n + 2 n+ 12 e− 4n , ′ ζ (2) log(2π ) + γ 1 + . − ζ ′ (−1) = exp − exp 12 12 2π 2

The constant A is known as the Glaisher–Kinkelin constant [211, p. 135]. Higher order factorials can be treated similarly. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

VI.20. Polylogarithms of integral index and a general formula. Let α = m ∈ Z≥1 . Then: Lim (z) =

(−1)m m−1 w (log w − Hm−1 ) + (m − 1)!

X

j≥0, j6=m−1

(−1) j ζ (m − j)w j , j!

where Hm is the harmonic number and w = − log z. [The line of proof is the same as in Theorem VI.7, only the residue calculation at s = 1 differs.] The general formula, X ∂r Liα,r (z) ∼ (−1)r r Res ζ (s + α)Ŵ(s)w−s , w := − log z, ∂α z→1 s∈Z≥0 ∪{1−α}

holds for all α ∈ C and r ∈ Z≥0 and is amenable to symbolic manipulation.

VI. 9. Functional composition Let f and g be functions analytic at the origin that have non-negative coefficients. We consider the composition h = f ◦ g,

h(z) = f (g(z)),

assuming g(0) = 0. Let ρ f , ρg , ρh be the corresponding radii of convergence, and let τ f = f (ρ f ), and so on. We shall assume that f and g are 1–continuable and that they admit singular expansions in the scale of powers. There are three cases to be distinguished depending on the value of τg in comparison with ρ f . — Supercritical case, when τg > ρ f . In that case, when z increases from 0, there is a value r strictly less than ρg such that g(r ) attains the value ρ f , which triggers a singularity of f ◦ g. In other words r ≡ ρh = g (−1) (ρ f ). Around this point, g is analytic and a singular expansion of f ◦ g is obtained by combining the singular expansion of f with the regular expansion of g at r . The singularity type is that of the external function ( f ).

412

VI. SINGULARITY ANALYSIS OF GENERATING FUNCTIONS

— Subcritical case, when τg < ρ f . In this dual situation, the singularity of f ◦g is driven by that of the inside function g. We have ρh = ρg , τh = f (ρg ) and the singular expansion of f ◦ g is obtained by combining the regular expansion of f with the singular expansion of g at ρg . The singularity type is that of the internal function (g). — Critical case, when τg = ρ f . In this boundary case, there is a confluence of singularities. We have ρh = ρg , τh = τ f , and the singular expansion is obtained by applying the composition rules of the singular expansions involved. The singularity type is a mix of the types of the internal and external functions ( f, g). This classification extends the notion of a supercritical sequence schema in Section V. 2, p. 293, for which the external function reduces to f (z) = (1 − z)−1 , with ρ f = 1. In this chapter, we limit ourselves to discussing examples directly, based on the guidelines above supplemented by the plain algebra of generalized power series expansions. Finer probabilistic properties of composition schemas are studied at several places in Chapter IX starting on p. 629. Example VI.10. “Supertrees”. Let G be the class of general Catalan trees: √ 1 G = Z × S EQ(G) H⇒ G(z) = (1 − 1 − 4z). 2 The radius of convergence of G(z) is 1/4 and the singular value is G(1/4) = 1/2. The class ZG consists of planted trees, which are such that to the root is attached a stem and an extra node, with OGF equal to zG(z). We then introduce two classes of supertrees defined by substitution: H = G[ZG] K = G[(Z + Z)G]

H⇒ H⇒

H (z) = G(zG(z)) K (z) = G(2zG(z)).

These are “trees of trees”: the class H is formed of trees such that, on each node there is grafted a planted tree (by the combinatorial substitution of Section I. 6, p. 83); the class K similarly corresponds to the case when the stems can be of any two colours. Incidentally, combinatorial sum expressions are available for the coefficients, Hn =

⌊n/2⌋ X k=1

⌊n/2⌋ X 2k 2k − 22n − 3k − 1 1 2k − 2 2n − 3k − 1 , Kn = , n−k k−1 n−k−1 n−k k−1 n−k−1 k=1

the initial values being given by H (z) = z 2 + z 3 + 3z 4 + 7z 5 + 21z 6 + · · · ,

K (z) = 2z 2 + 2z 3 + 8z 4 + 18z 5 + 64z 6 + · · · .

Since ρG = 1/4 and τG = 1/2, the composition scheme is subcritical in the case of H and critical in the case of K. In the first case, the singularity is of square-root type and one finds easily: r √ 2− 2 1 1 4n H (z) ∼ −√ − z, −→ Hn ∼ √ . 4 8 4 8 2π n 3/2 z→ 1 4

In the second case, the two square-roots combine to produce a fourth root: 1/4 1 1 1 4n . −√ −z K (z) ∼ −→ Kn ∼ 2 4 8Ŵ( 43 )n 5/4 z→ 14 2

VI. 9. FUNCTIONAL COMPOSITION

413

Figure VI.12. A binary supertree is a “tree of trees”, with component trees all binary. The number of binary supertrees with 2n nodes has the unusual asymptotic form c4n n −5/4 . On a similar register, consider the class B of complete binary trees: p 1 − 1 − 4z 2 B = Z + Z × B × B H⇒ B(z) = , 2z and define the class of binary supertrees (Figure VI.12) by q p 1 − 2 1 − 4z 2 − 1 + 4z 2 p . S = B (Z × B) H⇒ S(z) = 1 − 1 − 4z 2

The composition is critical since z B(z) = 21 at the dominant singularity z = 21 . It is enough to consider the reduced function √ S(z) = S( z) = z + z 2 + 3z 3 + 8z 4 + 25z 5 + 80z 6 + 267z 7 + 911z 8 + · · · , whose coefficients constitute EIS A101490 and occur in Bousquet-M´elou’s study of integrated superbrownian excursion [83]. We find ! √ √ 4n 2 1 1/4 1/2 + ··· . S(z) ∼ 1− 2(1−4z) +(1−4z) +· · · −→ S n = 5/4 − √ n 4Ŵ( 3 ) 2 π n 1/4 4

For instance, a seven-term expansion yields a relative accuracy better than 10−4 for n ≥ 100, so that such approximations are quite usable in practice. The occurrence of the exponent − 54 in the enumeration of bicoloured and binary supertrees is noteworthy. Related constructions have been considered by Kemp [364] who obtained more generally exponents of the form −1 − 2−d by iterating the substitution construction (in connection with so-called “multidimensional trees”). It is significant that asymptotic terms of the form n p/q with q 6= 1, 2 appear in elementary combinatorics, even in the context of simple algebraic functions. Such exponents tend to be associated with non-standard limit laws, akin to the stable distributions of probability theory: see our discussion in Section IX. 12, p. 715. . . . . . . . . . . .

VI.21. Supersupertrees. Define supersupertrees by S [2] (z) = B(z B(z B(z))). We find automatically (with the help of B. Salvy’s program) −1 7 2n+1 [2] −13/4 4n n −9/8 , [z ]S (z) ∼ 2 Ŵ 8

414

VI. SINGULARITY ANALYSIS OF GENERATING FUNCTIONS

and further extensions involving an asymptotic term n −1−2

−d

are possible [364].

VI.22. Valuated trees. Consider the family of (rooted) general plane trees, whose vertices are decorated by integers from Z≥0 (called “values”) and such that the values of two adjacent vertices differ by ±1. Size is taken to be the number of edges. Let T j be the class of valuated trees whose root has value j and T = ∪T j . The OGFs T j (z) satisfy the system of equations T j = 1 + z(T j−1 + T j+1 )T j ,

so that T (z) solves T = 1 + 2zT 2 and is a simple variant of the Catalan OGF: √ 1 − 1 − 8z T (z) = . 4z Bouttier, Di Francesco, and Guitter [90, 91] found an amazing explicit form for the T j ; namely, (1 − Y j+1 )(1 − Y j+5 ) (1 + Y )4 , with Y = z . j+2 j+4 (1 − Y )(1 − Y ) 1 + Y2 In particular, each T j is an algebraic function. The function T0 counts maps (p. 513) that are Eulerian triangulations, or dually bipartite trivalent maps. The coefficients of the T j as well as the distributions of labels in such trees can be analysed asymptotically: see Bousquet-M´elou’s article [83] for a rich set of combinatorial connections. Tj = T

Schemas. Singularity analysis also enables us to discuss at a fair level of generality the behaviour of schemas, in a way that parallels the discussion of the supercritical sequence schema, based on a meromorphic analysis (Section V. 2, p. 293). We illustrate this point here by means of the supercritical cycle schema. Deeper examples relative to recursively defined structures are developed in Chapter VII. Example VI.11. Supercritical cycle schema. The schema H = C YC(G) forms labelled cycles from basic components in G: H = C YC(G)

H⇒

H (z) = log

1 . 1 − G(z)

Consider the case where G attains the value 1 before becoming singular, that is, τG > 1. This corresponds to a supercritical composition schema, which can be discussed in a way that closely parallels the supercritical sequence schema (Section V. 2, p. 293): a logarithmic singularity replaces a polar singularity. Let σ := ρ H , which is determined by G(σ ) = 1. First, one finds: H (z) ∼ log z→σ

1 − log(σ G ′ (σ )) + A(z), 1 − z/σ

where A(z) is analytic at z = σ . Thus:

σ −n . n (The error term implicit in this estimate is exponentially small). The BGF H (z, u) = log(1 − uG(z))−1 has the variable u marking the number of components in H–objects. In particular, the mean number of components in a random H–object of size n is ∼ λn, where λ = 1/(σ G ′ (σ )), and the distribution is concentrated around its mean. Similarly, the mean number of components with size k in a random Hn object is found to be asymptotic to λgk σ k , where gk = [z k ]G(z). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . [z n ]H (z) ∼

VI. 9. FUNCTIONAL COMPOSITION

415

Weights fk

1 k

f (z)

1 log 1−z

(49)

1 2k

4k k √1 1−z

1

Hk

k

k2

1 1−z

1 1 1−z log 1−z

z (1−z)2

z+z 2 . (1−z)3

Triangular arrays (k)

(50)

gn

g(z)

n−1

k n−k

k n−k

k−1

(n−k)!

z 1−z

ze z

z(1 + z)

k 2n−k−1 n n−1 √ 1− 1−4z 2

k 2n n n−k √ 1−2z− 1−4z 2z

n−k−1

k n(n−k)! T (z)

Figure VI.13. Typical weights (top) and triangular arrays (bottom) illustrating the P (k) discussion of combinatorial sums Sn = nk=1 f k gn .

Combinatorial sums. Singularity analysis permits us to discuss the asymptotic behaviour of entire classes of combinatorial sums at a fair level of generality, with asymptotic estimates coming out rather automatically. We examine here combinatorial sums of the form n X f k gn(k) , Sn = k=0

where f k is a sequence of numbers, usually of a simple form and called the weights, (k) while the gn are a triangular array of numbers, for instance Pascal’s triangle. As weights f k we shall consider sequences such that f (z) is 1–analytic with a singular expansion involving functions of the standard scale of Theorems VI.1, VI.2, VI.3. Typical examples9 for f (z) and ( f k ) are displayed in Figure VI.13, Equation (49). The triangular arrays discussed here are taken to be coefficients of the powers of some fixed function, namely, gn(k)

n

= [z ](g(z))

k

where

g(z) =

∞ X

gn z n ,

n=1

with g(z) an analytic function at the origin having non-negative coefficients and satisfying g(0) = 0. Examples are given in Figure VI.13, Equation (50). An interesting class of such arrays arises from the Lagrange Inversion Theorem (p. 732). Indeed, if g(z) is implicitly defined by g(z) = zG(g(z)), one has gn,k = nk [w n−k ]G(w)n ; the last three cases of (50) are obtained in this way (by taking G(w) as 1/(1 − w), (1 + w)2 , ew ). By design, the generating function of the Sn is simply S(z) =

∞ X n=0

Sn z n = f (g(z))

with

f (z) =

∞ X

fk zk .

k=0

Consequently, the asymptotic analysis of Sn results by inspection from the way singularities of f (z) and g(z) get transformed by composition. 9Weights such as log k and

√ k, also satisfy these conditions, as seen in Section VI. 8.

416

VI. SINGULARITY ANALYSIS OF GENERATING FUNCTIONS

Example VI.12. Bernoulli sums. Let φ be a function from Z≥0 to R and write f k := φ(k). Consider the sums n X 1 n Sn := φ(k) n . 2 k

k=0 10 If X n is a binomial random variable , X n ∈ Bin(n, 21 ), then Sn = E(φ(X n )) is exactly the expectation of φ(X n ). Then, by the binomial theorem, the OGF of the sequence (Sn ) is:

2 z . f 2−z 2−z Considering weights whose generating function, as in (49), has radius of convergence 1, what we have is a variant of the composition schema, with an additional prefactor. The composition scheme is of the supercritical type since the function g(z) = z/(2 − z), which has radius of convergence equal to 2, satisfies τg = ∞. The singularities of S(z) are then of the same type as those of the weight generating function f (z) and one verifies, in all cases of (49), that, to first asymptotic order, Sn ∼ φ(n/2): this is in agreement with the fact that the binomial distribution is concentrated near its mean n/2. Singularity analysis furthermore provides complete asymptotic expansions; for instance, 1 6 2 2 E Xn > 0 = + 2 + 3 + O(n −4 ) Xn n n n n 1 1 E HXn + O(n −3 ). = log + γ + − 2 2n 12n 2 See [208, 223] for more along these lines. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . S(z) =

Example VI.13. Generalized Knuth–Ramanujan Q-functions. For reasons motivated by analysis of algorithms, Knuth has encountered repeatedly sums of the form n(n − 1) n n(n − 1)(n − 2) Q n ({ f k }) = f 0 + f 1 + f 2 + f3 + ··· . n n2 n3 (See, e.g., [384, pp. 305–307].) There ( f k ) is a sequence of coefficients (usually of at most polynomial growth). For instance, the case f k ≡ 1 yields the expected time until the first collision in the birthday paradox problem (Section II. 3, p. 114). A closer examination shows that the analysis of such Q n is reducible to singularity analysis. Writing n! X n n−k−1 Q n ({ f k }) = f 0 + n−1 fk (n − k)! n k≥1

reveals the closeness with the last column of (50). Indeed, setting X fk F(z) = zk , k k≥1

one has (n ≥ 1)

n! Q n = f 0 + n−1 [z n ]S(z) where S(z) = F(T (z)), n and T (z) is the Cayley tree function (T = ze T ). For weights f k = φ(k) of polynomial growth, the schema is critical. Then, the singular expansion of S is obtained by composing the singular expansion of f with the expansion of T , 10 A binomial random variable (p. 775) is a sum of Bernoulli variables: X = Pn n j=1 Y j , where the

Y j are independent and distributed as a Bernoulli variable Y , with P(Y = 1) = p, P(Y = 0) = q = 1 − p.

VI. 9. FUNCTIONAL COMPOSITION

417

√ √ namely, T (z) ∼ 1− 2 1 − ez as z → e−1 . For instance, if φ(k) = k r for some integer r ≥ 1 then F(z) has an r th order pole at z = 1. Then, the singularity type of F(T (z)) is Z −r/2 where Z = (1 − ez), which is reflected by Sn ≍ en nr/2−1 (we use ‘≍’ to represent order-of-growth information, disregarding multiplicative constants). After the final normalization, we see that Q n ≍ n (r√+1)/2√ . Globally, for many weights of the form f k = φ(k), we expect Q n to be of the form nφ( n), in accordance with √ the fact that the expectation of the first collision in the birthday problem is on average near π n/2. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

VI.23. General Bernoulli sums. Let X n ∈ Bin(n; p) be a binomial random variable with general parameters p, q:

P(X n = k) =

n k n−k p q , k

Then with f k = φ(k), one has E(φ(X n )) = [z n ]

1 f 1 − qz

q = 1 − p.

pz 1 − qz

,

so that the analysis develops as in the case Bin(n; 12 ).

VI.24. Higher moments of the birthday problem. Take the model where there are n days in the year and let B be the random variable representing the first birthday collision. Then Pn (B > k) = k!n −k nk , and En (8(B)) = 8(1) + Q n ({18(k)}),

where

18(k) := 8(k + 1) − 8(k).

For instance En (B) = 1 + Q n (h1, 1, . . .i). We thus get moments of various functionals (here stated to two asymptotic terms) 8(x)

x q

E n (8(B)) via singularity analysis.

πn + 2 2 3

x2 + x 2n + 2

x3 + x2 q 3 3 π2n − 2n

x4 + x3 q 3 8n 2 − 7 π2n

VI.25. How to weigh an urn? The “shake-and-paint” algorithm. You are given an urn containing an unknown number N of identical looking balls. How to estimate this number in much fewer than O(N ) operations? A probabilistic solution due to Brassard and Bratley [92] uses a brush and some paint. Shake the urn, pull out a ball, then mark it with paint and replace it into the urn. Repeat until you find an already painted ball. Let X be the number of operations. √ One has E(X ) ∼ π N /2. Furthermore the quantity Y := X 2 /2 constitutes, by the previous note, an asymptotically unbiased estimator of N , in the sense that E(Y ) ∼ N . In other words, count the time√till an already painted ball is first found, and return half of the square of this time. One also has V(Y ) ∼ N . By performing the experiment m times (using m different colours of paint) and by taking the arithmetic average √ of the m estimates, one obtains an unbiased estimator whose typical relative accuracy is 1/m. For instance, m = 16 gives an accuracy of 25%. (Similar principles are used in the design of data mining algorithms.)

VI.26. Catalan sums. These are defined by Sn :=

X

k≥0

2n , fk n−k

S(z) = √

1 1 − 4z

f

! √ 1 − 2z − 1 − 4z . 2z

The case when ρ f = 1 corresponds to a critical composition, which can be discussed much in the same way as Ramanujan sums.

418

VI. SINGULARITY ANALYSIS OF GENERATING FUNCTIONS

VI. 10. Closure properties At this stage11, we have available composition rules for singular expansions under operations such as ±, ×, ÷: these are induced by corresponding rules for extended formal power series, where generalized exponents and logarithmic factors are allowed. Also, from Section VI. 7, inversion of analytic functions normally gives rise to squareroot singularities, and, from Section VI. 9, functions amenable to singularity analysis are essentially closed under composition. In this section we show that functions amenable to singularity analysis (SA functions) satisfy explicit closure properties under differentiation, integration, and Hadamard product. (The contents are liberally borrowed from an article of Fill, Flajolet, and Kapur [208], to which we refer for details.) In order to keep the developments simple, we shall mostly restrict attention to functions that are 1–analytic and admit a simple singular expansion of the form f (z) =

(51)

J X j=0

c j (1 − z)α j + O((1 − z) A ),

or a simple singular expansion with logarithmic terms (52)

f (z) =

J X j=0

c j (L(z)) (1 − z)α j + O((1 − z) A ),

L(z) := log

1 , 1−z

where each c j is a polynomial. These are the cases most frequently occurring in applications (the proof techniques are easily extended to more general situations). Subsection VI. 10.1 treats differentiation and integration; Subsection VI. 10.2 presents the closure of functions that admit simple expansions under Hadamard product. Finally, Subsection VI. 10.3 concludes with an examination of several interesting classes of tree recurrences, where all the closure properties previously established are put to use in order to quantify precisely the asymptotic behaviour of recurrences that are attached to tree models. VI. 10.1. Differentiation and integration. Functions that are SA happen to be closed under differentiation, this is in sharp contrast with real analysis. In the simple cases12 of (51) and (52), closure under integration is also granted. The general principle (Theorems VI.8 and VI.9 below) is the following: Derivatives and primitives of functions that are amenable to singularity analysis admit singular expansions obtained term by term, via formal differentiation and integration. The following statement is a version, tuned to our needs, of well-known differentiability properties of complex asymptotic expansions (see, e.g., Olver’s book [465, p. 9]). 11This section represents supplementary material not needed elsewhere in the book, so that it may be

omitted on first reading. 12 It is possible but unwieldy to treat a larger class, which then needs to include arbitrarily nested R R logarithms, since, for instance, d x/x = log x, d x/(x log x) = log log x, and so on.

VI. 10. CLOSURE PROPERTIES

419

radius: κ |1 − z| z

φ′

φ

1 Figure VI.14. The geometry of the contour γ (z) used in the proof of the differentiation theorem.

Theorem VI.8 (Singular differentiation). Let f (z) be 1–analytic with a singular expansion near its singularity of the simple form f (z) =

J X j=0

c j (1 − z)α j + O((1 − z) A ). r

d Then, for each integer r > 0, the derivative dz r f (z) is 1–analytic. The expansion of the derivative at the singularity is obtained through term-by-term differentiation: J

X Ŵ(α j + 1) dr r f (z) = (−1) (1 − z)α j −r + O((1 − z) A−r ). cj r dz Ŵ(α j + 1 − r ) j=0

Proof. All that is required is to establish the effect of differentiation on error terms, which is expressed symbolically as d O((1 − z) A ) = O((1 − z) A−1 ). dz By bootstrapping, only the case of a single differentiation (r = 1) needs to be considered. Let g(z) be a function that is regular in a domain 1(φ, η) where it is assumed to satisfy g(z) = O((1 − z) A ) for z ∈ 1. Choose a subdomain 1′ := 1(φ ′ , η′ ), where φ < φ ′ < π2 and 0 < η′ < η. By elementary geometry, for a sufficiently small κ > 0, the disc of radius κ|z −1| centred at a value z ∈ 1′ lies entirely in 1; see Figure VI.14. We fix such a small value κ and let γ (z) represent the boundary of that disc oriented positively. The starting point is Cauchy’s integral formula Z 1 dw , (53) g ′ (z) = g(w) 2πi C (w − z)2 a direct consequence of the residue theorem. Here C should encircle z while lying inside the domain of regularity of g, and we opt for the choice C ≡ γ (z). Then trivial

420

VI. SINGULARITY ANALYSIS OF GENERATING FUNCTIONS

bounds applied to (53) give |g ′ (z)| =

=

O ||γ (z)|| · (1 − z) A |1 − z|−2 O |1 − z| A−1 .

The estimate involves the length of the contour, ||γ (z)||, which is O(1 − z) by construction, as well as the bound on g itself, which is O((1 − z) A ) since all points of the contour are themselves at a distance exactly of the order of |1 − z| from 1.

VI.27. Differentiation and logarithms. Let g(z) satisfy g(z) = O (1 − z) A L(z)k ,

L(z) = log

for k ∈ Z≥0 . Then, one has

1 , 1−z

dr g(z) = O (1 − z) A−r L(z)k . r dz (The proof is similar to that of Theorem VI.8.)

It is well known that integration of asymptotic expansions is usually easier than differentiation. Here is a statement custom-tailored to our needs. Theorem VI.9 (Singular integration). Let f (z) be 1–analytic and admit an expansion near its singularity of the form f (z) = Rz

J X j=0

c j (1 − z)α j + O((1 − z) A ).

Then 0 f (t) dt is 1–analytic. Assume further that none of the quantities α j and A equal −1. R (i) If A < −1, then the singular expansion of f is Z z J X cj (1 − z)α j +1 + O (1 − z) A+1 . (54) f (t) dt = − αj + 1 0 j=0

R (ii) If A > −1, then the singular expansion of f is Z z J X cj (1 − z)α j +1 + L 0 + O (1 − z) A+1 , (55) f (t) dt = − αj + 1 0 j=0

where the “integration constant” L 0 has the value Z 1h i X X cj L 0 := + f (t) − c j (1 − t)α j dt. αj + 1 0 α j 1) = α n+α = (α < 1) α

costs ( f n ) α−1 α+1 n+α nα −n+1 ∼ α α+1 α − 1 Ŵ(α + 1) 1−α−1 1+α n+α n+1− ∼ n 1+α α 1−α α+1 α n + O(n α−1 ) fn = α−1 α+1 α n + O(n) fn = α−1 α K α n + O(n )

tn = n α

(2 < α)

tn = n α

(1 < α < 2)

tn = n α

(0 < α < 1)

K 0′ n − log n + O(1)

tn = log n

Figure VI.17. Tolls and costs for the binary search tree recurrence, with t0 = 0. Thus, the singular element (1 − z)β corresponds to a contribution β n−β −1 −c , β + 2 −β − 1

which is of order O(n −β−1 ). This chain of operations suffices to determine the leading order of f n when tn = n α and α > 1. The derivation above is representative of the main lines of the analysis, but it has left aside the determination of integration constants, which play a dominant rˆole when tn = n α and α < 1 (because a term of the form K /(1 − z)2 then dominates in f (z)). Introduce, in accordance with the statement of the Singular Integration Theorem (Theorem VI.9, p. 420) the quantity Z 1 ′ 2 ′ 2 K[t] := t (w)(1 − w) − t (w)(1 − w) dw, −

0

where f − represents the sum of singular terms of exponent < −1 in the singular expansion of f (z). Then, for tn = n α with 0 < α < 1, taking into account the integration constant (which gets multiplied by (1 − z)−2 , given the shape of L), we find for α < 1: f n ∼ K α n,

K α = K[Li−α ] = 2

∞ X

n=1

nα . (n + 1)(n + 2)

Similarly, the toll tn = log n gives rise to f n ∼ K 0′ n,

K 0′ = 2

∞ X

n=1

log n . = 1.2035649167. (n + 1)(n + 2)

This last estimate quantifies the entropy of the distribution of binary search trees, which is studied by Fill in [207], and discussed in the reference book by Cover and Thomas on information theory [134, p. 74-76]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Example VI.16. The binary tree recurrence. Consider a procedure that, given a (pruned) binary tree, performs certain calculations (without affecting the tree itself) at a cost of tn , for size n, then recursively calls itself on the left and right subtrees. If the binary tree to which the

VI. 10. CLOSURE PROPERTIES

Tolls (tn ) nα

costs ( f n )

( 23 < α)

Ŵ(α − 21 ) α+1/2 n + O(n α−1/2 ) Ŵ(α)

( 21 < α < 32 )

2 √ n 2 + O(n log n) π Ŵ(α − 21 ) α+1/2 n + O(n) Ŵ(α)

n 3/2 nα

1 √ n log n + O(n) π

n 1/2 nα

431

(0 < α < 21 )

log n

K α n + O(1) √ ′ K 0 n + O( n

Figure VI.18. Tolls and costs for the binary tree recurrence.

procedure is applied is drawn uniformly among all binary trees of size n the expectation of the total cost of the procedure satisfies the recurrence (69)

f n = tn +

n−1 X k=0

Ck Cn−1−k ( f k + f n−k ) Cn

with Cn =

1 2n . n+1 n

Indeed, the quantity pn,k =

Ck Cn−1−k Cn

represents the probability that a random tree of size n has a left subtree of size k and a right subtree of size n − k. It is then natural to introduce the generating functions X X t (z) = tn C n z n , f (z) = f n Cn z n , n≥0

n≥0

and the recurrence (69) translates into a linear equation: f (z) = t (z) + 2zC(z) f (z), with C(z) the OGF of Catalan numbers. Now, given a toll sequence (tn ) with ordinary generation function X τ (z) := tn z n , n≥0

the function t (z) is a Hadamard product: t (z) = τ (z)⊙C(z). Furthermore, C(z) is well known, so that the fundamental relation is √ 1 − 1 − 4z τ (z) ⊙ C(z) , C(z) = . (70) f (z) = L[τ (z)], where L[τ (z)] = √ 2z 1 − 4z This transform relates the ordinary generating function of tolls to the normalized generating function of the total costs via a Hadamard product.

432

VI. SINGULARITY ANALYSIS OF GENERATING FUNCTIONS

Tolls (tn ) nα

( 23 < α)

n 3/2 nα

( 21 < α < 32 )

n 1/2 nα log n

(0 < α < 21 )

costs ( f n ) Ŵ(α − 12 ) α+1/2 n + O(n α−1/2 ) √ 2Ŵ(α) r 2 2 n + O(n log n) π Ŵ(α − 12 ) α+1/2 n + O(n) √ 2Ŵ(α) 1 √ n log n + O(n) 2π b K α n + O(1) √ b′ n + O( n) K 0

Figure VI.19. Tolls and costs for the Cayley tree recurrence. The calculation for simple tolls like nr with r ∈ Z≥0 can be carried out elementarily. For the tolls tnα = n α what is required is the singular expansion of ∞ z X z n α 2n z n = Li−α (z) ⊙ C = . τ (z) ⊙ C 4 4 n+1 n 4 n=1

This is precisely covered by Theorems VI.7 (p. 408), VI.10 (p. 422), and VI.11 (p. 423). The results of Figure VI.18 follow, after routine calculations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Example VI.17. The Cayley tree recurrence. Consider n vertices labelled 1, . . . , n. There are (n − 1)!n n−2 sequences of edges, hu 1 , v 1 , i, hu 2 , v 2 , i, · · · , hu n−1 , v n−1 i,

that give rise to a tree over {1, . . . , n}, and the number of such sequences is (n − 1)!n n−2 since there are n n−2 unrooted trees of size n. At each stage k, the edges numbered 1 to k determine a forest. Each addition of an edge connects two trees [that then become rooted] and reduces the number of trees in the forest by 1, so that the forest evolves from the totally disconnected graph (at time 0) to an unrooted tree (at time n − 1). If we consider each of the sequences to be equally likely, the probability that u n−1 and v n−1 belong to components of size k and (n − k) is k−1 (n − k)n−k−1 n k 1 . 2(n − 1) k n n−2

(The reason is that there are k k−1 rooted trees of size k; the last added edge has n−1 possibilities and 2 possible orientations.) Assume that the aggregation of two trees into a tree of size equal to ℓ incurs a toll of tℓ . The total cost of the aggregation process for a final tree of size n satisfies the recurrence k−1 X n k (n − k)n−k−1 1 (71) f n = tn + pn,k ( f k + f n−k ), pn,k = . 2(n − 1) k n n−2 0 0, one has

VI. 11. TAUBERIAN THEORY AND DARBOUX’S METHOD

435

3(cx)/3(x) → 1 as x → +∞. (Examples of slowly varying functions are provided by powers of logarithms or iterated logarithms.) Theorem VI.13 (The HLK Tauberian theorem). Let f (z) be a power series with radius of convergence equal to 1, satisfying 1 1 (73) f (z) ∼ 3( ), (1 − z)α 1 − z for some α ≥ 0 with 3 a slowly varying function. Assume that the coefficients f n = [z n ] f (z) are all non-negative (this is the “side condition”). Then n X

(74)

k=0

nα 3(n). Ŵ(α + 1)

fk ∼

The conclusion (74) is consistent with the result given by singularity analysis: under the conditions, and if in addition analytic continuation is assumed, then n α−1 3(n), Ŵ(α) which by summation yields the estimate (74). It must be noted that a Tauberian theorem requires very little on the part of the function. However, it gives little, since it does not include error estimates. Also, the result it provides is valid in the more restrictive sense of mean values, or Ces`aro averages. (If further regularity conditions on the f n are available, for instance monotonicity, then the conclusion of (75) can then be deduced from (74) by purely elementary real analysis.) The method applies only to functions that are large enough at their singularity (the assumption α ≥ 0), and despite numerous efforts to improve the conclusions, it is the case that Tauberian theorems do not have much to offer in terms of error estimates. Appeal to a Tauberian theorem may be justified when a function has, apart from the positive half line, a very irregular behaviour near its circle of convergence, for instance when each point of the unit circle is a singularity. (The function is then said to admit the unit circle as a natural boundary.) An interesting example of this situation is discussed by Greene and Knuth [309] who consider the function ∞ Y zk , 1+ (76) f (z) = k fn ∼

(75)

k=1

which is the EGF of permutations having cycles all of different lengths. A little computation shows that ∞ k ∞ ∞ ∞ X Y z zk 1 X z 2k 1 X z 3k 1+ = − log + − ··· k k 2 3 k2 k3 k=1 k=1 k=1 k=1 1 − γ + o(1). ∼ log 1−z (Only the last line requires some care, see [309].) Thus, we have f (z) ∼

e−γ 1−z

−→

1 ( f 0 + f 1 + · · · + f n ) ∼ e−γ , n

436

VI. SINGULARITY ANALYSIS OF GENERATING FUNCTIONS

by virtue of Theorem VI.12. In fact, Greene and Knuth were able to supplement this argument by a “bootstrapping” technique and show a stronger result, namely f n → e−γ .

VI.31. Fine asymptotics of the Greene–Knuth problem. With f (z) as in (76), we have e−γ e−γ e−γ + + 2 (− log n − 1 − γ + log 2) n n i 1 1 h −γ 2 , + 3 e log n + c1 log n + c2 + 2(−1)n + (n) + O n n4 where c1 , c2 are computable constants and (n) has period 3. (The paper [227] derives a complete expansion based on a combination of Darboux’s method and singularity analysis.) [z n ] f (z)

=

Darboux’s method. The method of Darboux (also known as the Darboux–P´olya method) requires, as regularity condition, that functions be sufficiently differentiable (“smooth”) on their circle of convergence. What lies at the heart of the method is a simple relation between the smoothness of a function and the decrease of its Taylor coefficients. Theorem VI.14 (Darboux’s method). Assume that f (z) is continuous in the closed disc |z| ≤ 1 and is, in addition, k times continuously differentiable (k ≥ 0) on |z| = 1. Then 1 n (77) [z ] f (z) = o . nk Proof. Start from Cauchy’s coefficient formula Z 1 dz fn = f (z) n+1 . 2iπ C z Because of the continuity assumption, one may take as integration contour C the unit circle. Setting z = eiθ yields the Fourier version of Cauchy’s coefficient formula, Z 2π 1 (78) fn = f (eiθ )e−niθ dθ. 2π 0 The integrand in (78) is strongly oscillating. The Riemann–Lebesgue lemma of classical analysis [577, p. 403] shows that the integral tends to 0 as n → ∞. The argument above covers the case k = 0. For a general k, successive integrations by parts give Z 2π 1 [z n ] f (z) = f (k) (eiθ )e−niθ dθ, 2π(in)k 0 a quantity that is o(n k ), by Riemann–Lebesgue again.

Various consequences of Theorem VI.14 are given in reference texts also under the name of Darboux’s method. See for instance [129, 309, 329, 608]. We shall only illustrate the mechanism by rederiving in this framework the analysis of the EGF of 2–regular graphs (Example VI.2, p. 395). We have 2

(79)

√ e−3/4 e−z/2−z /4 =√ + e−3/4 1 − z + R(z). f (z) = √ 1−z 1−z

VI. 12. PERSPECTIVE

437

There R(z) is the product of (1 − z)3/2 with a function analytic at z = 1 that is 2 a remainder in the Taylor expansion of e−z/2−z /4 . Thus, R(z) is of class C1 , i.e., continuously differentiable once. By Theorem VI.14, we have 1 [z n ]R(z) = o , n so that 1 e−3/4 . +o (80) [z n ] f (z) = √ n πn Darboux’s method bears some resemblance to singularity analysis in that the estimates are derived from translating error terms in expansions. However, smoothness conditions, rather than plain order of growth information, are required by it. The method is often applied, in situations similar to (79)–(80), to functions that are products of the type h(z)(1−z)α with h(z) analytic at 1. In such particular cases, Darboux’s method is however subsumed by singularity analysis. It is inherent in Darboux’s method that it cannot be applied to functions whose singular expansion only involves terms that become infinite, while singularity analysis can. A clear example arises in the analysis of the common subexpression problem [257] where there occurs a function with a singular expansion of the form " # 1 1 c1 q 1+ + ··· . √ 1 1 − z log 1 log 1−z 1−z

VI.32. Darboux versus singularity analysis. This note provides an instance where Darboux’s method applies whereas singularity analysis does not. Let Fr (z) =

n ∞ X z2 . (2n )r

n=0

The function F0 (z) is singular at every point of the unit circle, and the same property holds for any Fr with r ∈ Z≥0 . [Hint: F0 , which satisfies the functional equation F(z) = z + F(z 2 ), grows unboundedly near 2n th roots of unity.] Darboux’s method can be used to derive 1 32 c 1 +o , c := F5 (z) = √ . [z n ] √ n 31 πn 1−z What is the best error term that can be obtained?

VI. 12. Perspective The method of singularity analysis expands our ability to extract coefficient asymptotics to a far wider class of functions than the meromorphic and rational functions of Chapters IV and V. This ability is the fundamental tool for analysing many of the generating functions provided by the symbolic method of Part A, and it is applicable at a considerable level of generality. The basic method is straightforward and appealing: we locate singularities, establish analyticity in a domain around them, expand the functions around the singularities, and apply general transfer theorems to take each term in the function expansion to a term in the asymptotic expansion of its coefficients. The method applies directly

438

VI. SINGULARITY ANALYSIS OF GENERATING FUNCTIONS

to a large variety of explicitly given functions, for instance combinations of rational functions, square roots, and logarithms, as well as to functions that are implicitly defined, like generating functions for tree structures, which are obtained by analytic inversion. Functions amenable to singularity analysis also enjoy rich closure properties, and the corresponding operations mirror the natural operations on generating functions implied by the combinatorial constructions of Chapters I–III. This approach again sets us in the direction of the ideal situation of having a theory where combinatorial constructions and analytic methods fully correspond, but, again, the very essence of analytic combinatorics is that the theorems that provide asymptotic results cannot be so general as to be free of analytic side conditions. In the case of singularity analysis, these side conditions have to do with establishing analyticity in a domain around singularities. Such conditions are automatically satisfied by a large number of functions with moderate (at most polynomial) growth near their dominant singularities, justifying precisely what we need: the term-by-term transfer from the expansion of a generating function at its singularity to an asymptotic form of coefficients, including error terms. The calculations involved in singularity analysis are rather mechanical. (Salvy [528] has indeed succeeded in automating the analysis of a large class of generating functions in this way.) Again, we can look carefully at specific combinatorial constructions and then apply singularity analysis to general abstract schemas, thereby solving whole classes of combinatorial problems at once. This process, along with several important examples, is the topic of Chapter VII, to come next. After that, we introduce, in Chapter VIII, the saddle-point method, which is appropriate for functions without singularities at a finite distance (entire functions) as well as those whose growth is rapid (exponential) near their singularities. Singularity analysis will surface again in Chapter IX, given its crucial technical rˆole in obtaining uniform expansions of multivariate generating functions near singularities. Bibliographic notes. Excellent surveys of asymptotic methods in enumeration have been given by Bender [36] and more recently Odlyzko [461]. A general reference to asymptotic analysis that has a remarkably concrete approach is De Bruijn’s book [143]. Comtet’s [129] and Wilf’s [608] books each devote a chapter to these questions. This chapter is largely based on the theory developed by Flajolet and Odlyzko in [248], where the term “singularity analysis” originates. An important early (and unduly neglected) reference is the study by Wong and Wyman [615]. The theory draws its inspiration from classical analytic number theory, for instance the prime number theorem where similar contours are used (see the discussion in [248] for sources). Another area where Hankel contours are used is the inversion theory of integral transforms [168], in particular in the case of algebraic and logarithmic singularities. Closure properties developed here are from the articles [208, 223] by Flajolet, Fill, and Kapur. Darboux’s method can often be employed as an alternative to singularity analysis. Although it is still a widely used technique in the literature, the direct mapping of asymptotic scales afforded by singularity analysis appears to us to be much more transparent. Darboux’s method is well explained in the books by Comtet [129], Henrici [329], Olver [465], and Wilf [608]. Tauberian theory is treated in detail in Postnikov’s monograph [494] and Korevaar’s encyclopaedic treatment [389], with an excellent introduction to be found in Titchmarsh’s book [577].

VII

Applications of Singularity Analysis Mathematics is being lazy. Mathematics is letting the principles do the work for you so that you do not have to do the work for yourself 1. ´ — G EORGE P OLYA I wish to God these calculations had been executed by steam. — C HARLES BABBAGE (1792–1871)

— The Bhagavad Gita XV.12 VII. 1. VII. 2. VII. 3. VII. 4. VII. 5. VII. 6. VII. 7. VII. 8. VII. 9. VII. 10. VII. 11.

A roadmap to singularity analysis asymptotics Sets and the exp–log schema Simple varieties of trees and inverse functions Tree-like structures and implicit functions Unlabelled non-plane trees and P´olya operators Irreducible context-free structures The general analysis of algebraic functions Combinatorial applications of algebraic functions Ordinary differential equations and systems Singularity analysis and probability distributions Perspective

441 445 452 467 475 482 493 506 518 532 538

Singularity analysis paves the way to the analysis of a large quantity of generating functions, as provided by the symbolic method expounded in Chapters I–III. In accordance with P´olya’s aphorism quoted above, it makes it possible to “be lazy” and “let the principles work for you”. In this chapter we illustrate this situation with numerous examples related to languages, permutations, trees, and graphs of various sorts. As in Chapter V, most analyses are organized into broad classes called schemas. First, we develop the general exp–log schema, which covers the set construction, either labelled or unlabelled, applied to generators whose dominant singularity is of logarithmic type. This typically non-recursive schema parallels in generality the supercritical schema of Chapter V, which is relative to sequences. It permits us to quantify various constructions of permutations, derangements, 2–regular graphs, mappings, and functional graphs, and provides information on factorization properties of polynomials over finite fields. 1Quoted in M Walter, T O’Brien, Memories of George P´olya, Mathematics Teaching 116 (1986) 2“There is an imperishable tree, it is said, that has its roots upward and its branches down and whose

leaves are the Hymns [Vedas]. He who knows it possesses knowledge.” 439

440

VII. APPLICATIONS OF SINGULARITY ANALYSIS

Next, we deal with recursively defined structures, whose study constitutes the main theme of this chapter. In that case, generating functions are accessible by means of equations or systems that implicitly define them. A distinctive feature of many such combinatorial types is that their generating functions have a square-root singularity, that is, the singular exponent equals 1/2. As a consequence, the counting sequences characteristically involve asymptotic terms of the form An n −3/2 , where the latter asymptotic exponent, −3/2, precisely reflects the singular exponent 1/2 in the function’s singular expansion, in accordance with the general principles of singularity analysis presented in Chapter VI. Trees are the prototypical recursively defined combinatorial type. Square-root singularities automatically arise for all varieties of trees constrained by a finite set of allowed node degrees, including binary trees, unary–binary trees, ternary trees, and many more. The counting estimates involve the characteristic n −3/2 subexponential factor, a property that holds in the labelled and unlabelled frameworks alike. Simple varieties of trees have many properties in common, beyond the subexponential growth factor of tree counts. Indeed, in √a random tree of some large size n, n, path length grows on average like almost all nodes are found to be at level about √ √ n n, and height is of order n, with high probability. These results serve to unify classical tree types—we say that such properties of random trees are universal3 among all simply generated families sharing the square-root singularity property. (This notion of universality, borrowed from physics, is also nowadays finding increasing popularity among probabilists, for reasons much similar to ours.) In this perspective, the motivation for organizing the theory along the lines of major schemas fits perfectly with the quest of universal laws in analytic combinatorics. In the context of simple varieties of trees, the square-root singularity arises from general properties of the inverse of an analytic function. Under suitable conditions, this characteristic feature can be extended to functions defined implicitly by a functional equation. Consequences are the general enumeration of non-plane unlabelled trees, including isomers of alkanes in theoretical chemistry, as well as secondary structures of molecular biology. Much of this chapter is devoted to context-free specifications and languages. In that case, a priori, generating functions are algebraic functions, meaning that they satisfy a system of polynomial equations, itself optionally reducible (by elimination) to a single equation. For solutions of positive polynomial systems, square-root singularities are found to be the rule under a simple technical condition of irreducibility that is evocative of the Perron–Frobenius conditions encountered in Chapter V in relation to finite-state and transfer-matrix models. As an illustration, we show how to develop a

3The following quotation illustrates well the notion of universality in physics: “[. . . ] this echoes the notion of universality in statistical physics. Phenomena that appear at first to be unconnected, such as magnetism and the phase changes of liquids and gases, share some identical features. This universal behaviour pays no heed to whether, say, the fluid is argon or carbon dioxide. All that matters are broad-brush characteristics such as whether the system is one-, two- or three-dimensional and whether its component elements interact via long- or short-range forces. Universality says that sometimes the details do not matter.” [From “Utopia Theory”, in Physics World, August 2003].

VII. 1. A ROADMAP TO SINGULARITY ANALYSIS ASYMPTOTICS

441

coherent theory of topological configurations in the plane (trees, forests, graphs) that satisfy a non-crossing constraint. For arbitrary algebraic functions (the ones that are not necessarily associated with positive coefficients and equations, or irreducible positive systems), a richer set of singular behaviours becomes possible: singular expansions involve fractional exponents (not just 1/2, corresponding to the square-root paradigm above). Singularity analysis is invariably applicable: algebraic functions are viewed as plane algebraic curves, and the famous Newton–Puiseux theorem of elementary algebraic geometry completely describes the types of singularities thay may occur. Algebraic functions also surface as solutions of various types of functional equations: this turns out to be the case for many classes of walks that generalize Dyck and Motzkin paths, via what is known as the kernel method, as well as for many types of planar maps (embedded planar graphs), via the so-called quadratic method. In all these cases, singular exponents of a predictable (rational) form are bound to occur, implying in turn numerous quantitative properties of random discrete structure and universality phenomena.. Differential equations and systems are associated to recursively defined structure, when either pointing constructions or order constraints appear. For counting generating functions, the equations are nonlinear, while the GFs associated to additive parameters lead to linear versions. Differential equations are also central in connection with the holonomic framework4 , which intervenes in the enumeration of many classes of “hard” objects, like regular graphs and Latin rectangles. Singularity analysis is once more instrumental in working out precise asymptotic estimates—the appearance of singular exponents that are algebraic (rather than rational) numbers is a characteristic feature of many such estimates. We examine here applications relative to quadtrees and to varieties of increasing trees, some of which are closely related to permutations as well as to algorithms and data structures for sorting and searching. VII. 1. A roadmap to singularity analysis asymptotics The singularity analysis theorems of Chapter VI, which may be coarsely summarized by the correspondence (1)

f (z) ∼ (1 − z/ρ)−α

−→

fn ∼

1 −n α−1 ρ n , Ŵ(α)

serve as our main asymptotic engine throughout this chapter. Singularity analysis is instrumental in quantifying properties of non-recursive as well as recursive structures. Our reader might be surprised not to encounter integration contours anymore in this chapter. Indeed, it now suffices to work out the local analysis of functions at their singularities, then the general theorems of singularity analysis (Chapter VI) effect the translation to counting sequences and parameters automatically.

4Holonomic functions (Appendix B.4: Holonomic functions, p. 748) are defined as solutions of linear

differential equations with coefficients that are rational functions.

442

VII. APPLICATIONS OF SINGULARITY ANALYSIS

The exp–log schema. This schema, examined in Section VII. 2, is relative to the labelled set construction, (2)

F = S ET(G)

H⇒

F(z) = exp (G(z)) ,

as well as its unlabelled counterparts, MS ET and PS ET: an F–structure is thus constructed (non-recursively) as an unordered assembly of G–components. In the case where the GF of components is logarithmic at its dominant singularity, 1 (3) G(z) ∼ κ log + λ, 1 − z/ρ an immediate computation shows that F(z) has a singularity of the power type, F(z) ∼ eλ (1 − z/ρ)−κ , which is clearly in the range of singularity analysis. The construction (2), supplemented by simple technical conditions surrounding (3), defines the exp–log schema. Then, for such F–structures that are assemblies of logarithmic components, the asymptotic counting problem is systematically solvable (Theorem VII.1, p. 446): the number of G–components in a large random F–structure is O(log n), both in the mean and in probability, while more refined estimates describe precisely the likely shape of profiles. This schema has a generality comparable to the supercritical schema examined in Section V. 2, p. 293, but the probabilistic phenomena at stake appear to be in sharp contrast: the number of components is typically small, being logarithmic for exp–log sets, as opposed to a linear growth in the case of supercritical sequences. The schema can be used to analyse properties of permutations, functional graphs, mappings, and polynomial over finite fields. Recursion and the universality of square-root singularity. A major theme of this chapter is the study of asymptotic properties of recursive structures. In a large number of cases, functions with a square root singularity are encountered, and given the usual correspondence, 1 ; f (z) ∼ −(1 − z)1/2 −→ fn ∼ √ 2 π n3 the corresponding coefficients are of the asymptotic form Cρ −n n −3/2 . Several schemas can be described to capture this phenomenon; we develop here, in order of increasing structural complexity, the ones corresponding to simple varieties of trees, implicit structures, P´olya operators, and irreducible polynomial systems. Simple varieties of trees and inverse functions. Our treatment of recursive combinatorial types starts with simple varieties of trees, studied in Section VII. 3. In the basic situation, that of plane unlabelled trees, the equation is Y = Z × S EQ (Y) H⇒ Y (z) = zφ(Y (z)), P ω with, as usual, φ(w) = ω∈ w . Thus, the OGF Y (z) is determined as the inverse of w/φ(w), where the function φ reflects the collection of all allowed node degrees (). From analytic function theory, we know that singularities of the inverse of an analytic function are generically of the square-root type (Subsection IV. 7.1, p. 275 and Section VI. 7, p. 402), and such is the case whenever is a “well-behaved” set (4)

VII. 1. A ROADMAP TO SINGULARITY ANALYSIS ASYMPTOTICS

443

of integers, in particular, a finite set. Then, the number of trees invariably satisfies an estimate of the form (5)

Yn = [z n ]Y (z) ∼ C An n −3/2 .

Square-root singularity is also attached to several universality phenomena, as evoked in the general introduction to this chapter. Tree-like structures and implicit functions. Functions defined implicitly by an equation of the form (6)

Y (z) = G(z, Y (z))

where G is bivariate analytic, has non-negative coefficients, and satisfies a natural set of conditions also lead to square-root singularity (Section VII. 4 and Theorem VII.3, p. 468)). The schema (6) obviously generalizes (4): simply take G(z, y) = zφ(y). Again, such functions invariably satisfy an estimate (5). Trees under symmetries and P´olya operators. The analytic methods mentioned above can be further extended to P´olya operators, which translate unlabelled set and cycle constructions; see Section VII. 5. A typical application is to the class of nonplane unlabelled trees whose OGF satisfies the infinite functional equation, ! H (z) H (z 2 ) H (z) = z exp + + ··· . 1 2 Singularity analysis applies more generally to varieties of non-plane unlabelled trees (Theorem VII.4, p. 479), which covers the enumeration of various types of interesting molecules in combinatorial chemistry. Context-free structures and polynomial systems. The generating function of any context-free class or language is known to be a component of a system of positive polynomial equations y1 = P1 (z, y1 , . . . , yr ) .. .. .. . . . yr = Pr (z, y1 , . . . , yr ).

The n −3/2 counting law is once more universal among such combinatorial classes under a basic condition of “irreducibility” (Section VII. 6 and Theorem VII.5, p. 483). In that case, the GFs are algebraic functions satisfying a strong positivity constraint; the corresponding analytic statement constitutes the important Drmota–Lalley–Woods Theorem (Theorem VII.6, p. 489). Note that there is a progression in the complexity of the schemas leading to square-root singularity. From the analytic standpoint, this can be roughly rendered by a chain inverse functions −→ implicit functions −→ systems.

It is, however, often meaningful to treat each combinatorial problem at its minimal level of generality, since expressions tend to become less and less explicit as complexity increases.

444

VII. APPLICATIONS OF SINGULARITY ANALYSIS

General algebraic functions. In essence, the coefficients of all algebraic functions can be analysed asymptotically (Section VII. 7). There are only minor limitations arising from the possible presence of several dominant singularities, like in the rational function case. The starting point is the characterization of the local behaviour of an algebraic function at any of its singularities, which is provided by the Newton– Puiseux theorem: if ζ is a singularity, then the branch Y (z) of an algebraic function admits near ζ a representation of the form X (7) Y (z) = Z r/s Z := (1 − z/ζ ), ck Z k/s , k≥0

for some r/s ∈ Q, so that the singular exponent is invariably a rational number. Singularity analysis is systematically applicable, so that the nth coefficient of Y is expressible as a finite linear combination of terms, each of the asymptotic form p ∈ Q \ {−1, −2, . . .}; (8) ζ −n n p/q , q see also Figure VII.1. The various quantities (like ζ, r, s) entering the asymptotic expansion of the coefficients of an algebraic function turn out to be effectively computable. Beside providing a wide-encompassing conceptual framework of independent interest, the general theory of algebraic coefficient asymptotics is applicable whenever the combinatorial problems considered are not amenable to any of the special schemas previously described. For instance, certain kinds of supertrees (these are defined as trees composed with trees, Example VII.10, p. 412) lead to the singular type Z 1/4 , which is reflected by an unusual subexponential factor of n −5/4 present in asymptotic counts. Maps, which are planar graphs drawn in the plane (or on the sphere), satisfy a universality law with a singular exponent equal to 3/2, which is associated to counting sequences involving an asymptotic n −5/2 factor. Differential equations and systems. When recursion is combined with pointing or with order constraints, enumeration problems translate into integro-differential equations. Section VII. 9 examines the types of singularities that may occur in two important cases: (i) linear differential equations; (ii) nonlinear differential equations. Linear differential equations arise from the analysis of parameters of splitting processes that extend the framework of tree recurrences (Subsection VI. 10.3, p. 427), and we treat the geometric quadtree structure in this perspective. An especially notable source of linear differential equations is the class of holonomic functions (solutions of linear equations with rational coefficients, cf Appendix B.4: Holonomic functions, p. 748), which includes GFs of Latin rectangles, regular graphs, permutations constrained by the length of their longest increasing subsequence, Young tableaux and many more structures of combinatorial theory. In an important case, that of a “regular” singularity, asymptotic forms can be systematically extracted. The singularities that may occur extend the algebraic ones (7), and the corresponding coefficients are then asymptotically composed of elements of the form (9)

ζ −n n θ (log n)ℓ ,

VII. 2. SETS AND THE EXP–LOG SCHEMA

Rational

Irred. linear system

ζ −n

—

General rational

ζ −n n ℓ

Algebraic

Irred. positive sys.

ζ −n n −3/2

—

General algebraic

ζ −n n p/q

Holonomic

Regular sing.

ζ −n n θ logℓ n

—

Irregular sing.

ζ −n e P(n

445

Perron–Frob., merom. fns, Ch. V meromorphic functions, Ch. V

1/r )

DLW Th., sing. analysis, this chapter, §VII. 6, p. 482 Puiseux, sing. analysis, this chapter, §VII. 7, p. 493

n θ logℓ n

ODE, sing. analysis, this chapter, §VII. 9.1, p. 518 ODE, saddle-point, §VIII. 7, p. 581

Figure VII.1. A telegraphic summary of a hierarchy of special functions by increasing level of generality: asymptotic elements composing coefficients and the coefficient extraction method (with ℓ, r ∈ Z≥0 , p/q ∈ Q, ζ and θ algebraic, and P a polynomial).

(θ an algebraic quantity, ℓ ∈ Z≥0 ), a type which is much more general than (8). Nonlinear differential equations are typically attached to the enumeration of trees satisfying various kinds of order constraints. A global treatment is intrinsically not possible, given the extreme diversity of singular expansions that may occur. Accordingly, we restrict attention to first-order nonlinear equations of the form d Y (z) = φ(Y (z)), dz which covers varieties of increasing trees and certain urn processes, including several models closely related to permutations. Figure VII.1 summarizes three classes of special functions encountered in this book, namely, rational, algebraic, and holonomic. When structural complexity increases, a richer set of asymptotic coefficient behaviours becomes possible. (The complex asymptotic methods employed extend much beyond the range summarized in the figure. For instance, the class of irreducible positive systems of polynomial equations are part of the general square-root singularity paradigm, also encountered with P´olya operators, as well as inverse and implicit functions in non-algebraic cases.) VII. 2. Sets and the exp–log schema We begin by examining a schema that is structurally comparable to the supercritical sequence schema of Section V. 2, p. 293, but one that requires singularity analysis for coefficient extraction. The starting point is the construction of permutations (P) as labelled sets of cyclic permutations (K): (10)

P = S ET(K)

H⇒

P(z) = exp (K (z)) , where K (z) = log

1 , 1−z

446

VII. APPLICATIONS OF SINGULARITY ANALYSIS

which gives rise to many easy explicit calculations. For instance, the probability that a random permutation consists of a unique cycle is 1/n (since it equals K n /Pn ); the number of cycles is asymptotic to log n, both on average (p. 122) and in probability (Example III.4, p. 160); the probability that a random permutation has no singleton cycle is ∼ e−1 (the derangement problem; see pp. 123 and 228). Similar properties hold true under surprisingly general conditions. We start with definitions that describe the combinatorial classes of interest. Definition VII.1. A function G(z) analytic at 0, having non-negative coefficients and finite radius of convergence ρ is said to be of (κ, λ)-logarithmic type, where κ 6= 0, if the following conditions hold: (i) the number ρ is the unique singularity of G(z) on |z| = ρ; (ii) G(z) is continuable to a 1–domain at ρ; (iii) G(z) satisfies 1 1 +λ+O , as z → ρ in 1. (11) G(z) = κ log 1 − z/ρ (log(1 − z/ρ))2 Definition VII.2. The labelled construction F = S ET(G) is said to be a labelled exp–log schema (“exponential–logarithmic schema”) if the exponential generating function G(z) of G is of logarithmic type. The unlabelled construction F = MS ET(G) is said to be an unlabelled exp–log schema if the ordinary generating function G(z) of G is of logarithmic type, with ρ < 1. In each case, the quantities (κ, λ) of (11) are referred to as the parameters of the schema. By the fact that G(z) has positive coefficients, we must have κ > 0, while the sign of λ is arbitrary. The definitions and the main properties to be derived for unlabelled multisets easily extend to the powerset construction: see Notes VII.1 and VII.5 below. Theorem VII.1 (Exp–log schema). Consider an exp–log schema with parameters (κ, λ). (i) The counting sequences satisfy κ −n 1 + O (log n)−2 , ρ [z n ]G(z) = n eλ+r0 κ−1 −n n [z ]F(z) = 1 + O (log n)−2 , n ρ Ŵ(κ) P where r0 = 0 in the labelled case and r0 = j≥2 G(ρ j )/j in the case of unlabelled multisets. (ii) The number X of G–components in a random F–object satisfies d (ψ(s) ≡ ds EFn (X ) = κ(log n − ψ(κ)) + λ + r1 + O (log n)−1 Ŵ(s)), P j where r1 = 0 in the labelled case and r1 = j≥2 G(ρ ) in the case of unlabelled multisets. The variance satisfies VFn (X ) = O(log n), and, in particular, the distribution5 of X is concentrated around its mean. 5 We shall see in Subsection IX. 7.1 (p. 667) that, in addition, the asymptotic distribution of X is

invariably Gaussian under such exp–log conditions.

VII. 2. SETS AND THE EXP–LOG SCHEMA

447

Proof. This result is from an article by Flajolet and Soria [258], with a correction to the logarithmic type condition given by Jennie Hansen [318]. We first discuss the labelled case, F = S ET(G), so that F(z) = exp G(z). (i) The estimate for [z n ]G(z) follows directly from singularity analysis with logarithmic terms (Theorem VI.4, p. 393). Regarding F(z), we find, by exponentiation, eλ 1 . (12) F(z) = 1 + O (1 − z/ρ)κ (log(1 − z/ρ))2

Like G, the function F = e G has an isolated singularity at ρ, and is continuable to the 1–domain in which the expansion (11) is valid. The basic transfer theorem then provides the estimate of [z n ]F(z). (ii) Regarding the number of components, the BGF of F with u marking the number of G–components is F(z, u) = exp(uG(z)), in accordance with the general developments of Chapter III. The function ∂ = F(z)G(z), f 1 (z) := F(z, u) ∂u u=1

is the EGF of the cumulated values of X . It satisfies near ρ eλ 1 1 f 1 (z) = κ log + λ 1 + O , (1 − z/ρ)κ 1 − z/ρ (log(1 − z/ρ))2 whose translation, by singularity analysis theory is immediate: eλ −n κ log n − κψ(κ) + λ + O (log n)−1 . [z n ] f 1 (z) ≡ EFn (X ) = ρ Ŵ(κ) This provides the mean value estimate of X as [z n ] f 1 (z)/[z n ]F(z). The variance analysis is conducted in the same way, using a second derivative. For the unlabelled case, the analysis of [z n ]G(z) can be recycled verbatim. First, given the assumptions, we must have ρ < 1 (since otherwise [z n ]G(z) could not be an integer). The classical translation of multisets (Chapter I) rewrites as F(z) = exp (G(z) + R(z)) ,

R(z) :=

∞ X G(z j ) , j j=2

G(z 2 ), . . .,

where R(z) involves terms of the form each being analytic in |z| < ρ 1/2 . Thus, R(z) is itself analytic, as a uniformly convergent sum of analytic functions, in |z| < ρ 1/2 . (This follows the usual strategy for treating P´olya operators in asymptotic theory.) Consequently, F(z) is 1–analytic. As z → ρ, we then find ∞ X 1 eλ+r0 G(ρ j ) 1 + O . (13) F(z) = , r ≡ 0 κ 2 (1 − z/ρ) j (log(1 − z/ρ)) j=2

[z n ]F(z)

The asymptotic expansion of then results from singularity analysis. The BGF F(z, u) of F, with u marking the number of G–components, is ! uG(z) u 2 G(z 2 ) F(z, u) = exp + + ··· . 1 2

448

VII. APPLICATIONS OF SINGULARITY ANALYSIS

F

κ

n = 100

n = 272

n = 739

Permutations

1

5.18737

6.18485

7.18319

Derangements

1

4.19732

5.18852

6.18454

2–regular

1 2 1 2

2.53439

3.03466

3.53440

2.97898

3.46320

3.95312

Mappings

Figure VII.2. Some exp–log structures (F ) and the mean number of G–components for n = 100, 272 ≡ ⌈100 · e⌋, 739 ≡ ⌈100 · e2 ⌋: the columns differ by about κ, as expected.

Consequently, ∂ = F(z) (G(z) + R1 (z)) , F(z, u) f 1 (z) := ∂u u=1

R1 (z) =

∞ X

G(z j ).

j=2

Again, the singularity type is that of F(z) multiplied by a logarithmic term, (14)

f 1 (z) ∼ F(z)(G(z) + r1 ), z→ρ

r1 ≡

∞ X

G(ρ j ).

j=2

The mean value estimate results. Variance analysis follows similarly.

VII.1. Unlabelled powersets. For the powerset construction F = PS ET(G), the statement of Theorem VII.1 holds with

r0 =

X

(−1) j−1

j≥2

G(ρ j ) , j

as seen by an easy adaptation of the proof technique of Theorem VII.1.

As we see below, beyond permutations, mappings, unlabelled functional graphs, polynomials over finite fields, 2–regular graphs, and generalized derangements belong to the exp–log schema; see Figure VII.2 for representative numerical data. Furthermore, singularity analysis gives precise information on the decomposition of large F objects into G components. Example VII.1. Cycles in derangements. The case of all permutations, 1 , 1−z is immediately seen to satisfy the conditions of Theorem VII.1: it corresponds to the radius of convergence ρ = 1 and parameters (κ, λ) = (1, 0). Let be a finite set of integers and consider next the class D ≡ D of permutations without any cycle of length in . This includes standard derangements (where = {1}). The specification is then D(z) = exp(K (z)) D = S ET(K) X zω 1 H⇒ G(z) = log − . G = C YCZ>0 \ (Z) 1−z ω P(z) = exp(K (z)),

K (z) = log

ω∈

VII. 2. SETS AND THE EXP–LOG SCHEMA

449

P The theorem applies, with κ = 1, λ := − ω∈ ω−1 . In particular, the mean number of cycles in a random generalized derangement of size n is log n + O(1). . . . . . . . . . . . . . . . . . . . . . . . . . Example VII.2. Connected components in 2–regular graphs. The class of (undirected) 2– regular graphs is obtained by the set construction applied to components that are themselves undirected cycles of length ≥ 3 (see p. 133 and Example VI.2, p. 395). In that case: F = S ET(G) F(z) = exp(G(z)) H⇒ 1 1 z z2 G = UC YC (Z) G(z) = log − − . ≥3 2 1−z 2 4

This is an exp–log scheme with κ = 1/2 and λ = −3/4. In particular the number of components is asymptotic to 12 log n, both in the mean and in probability. . . . . . . . . . . . . . . . . . . . . . .

Example VII.3. Connected components in mappings. The class F of mappings (functions from a finite set to itself) was introduced in Subsection II. 5.2, p. 129. The associated digraphs are described as labelled sets of connected components (K), themselves (directed) cycles of trees (T ), so that the class of all mappings has an EGF given by F(z) = exp(K (z)),

K (z) = log

1 , 1 − T (z)

T (z) = ze T (z) ,

with T the Cayley tree function. The analysis of inverse functions (Section VI. 7 and Exam−1 ple VI.8, p. 403) has shown √ √ that T (z) is singular at z = e , where it admits the singular √ expansion T (z) ∼ 1 − 2 1 − ez. Thus G(z) is logarithmic with κ = 1/2 and λ = − log 2. As a consequence, the number of connected mappings satisfies r π 1 + O(n −1/2 ) . K n ≡ n![z n ]K (z) = n n 2n

In other q words: the probability for a random mapping of size n to consist of a single component π . Also, the mean number of components in a random mapping of size n is is ∼ 2n

√ 1 log n + log 2eγ + O(n −1/2 ). 2 Similar properties hold for mappings without fixed points, which are analogous to derangements and were discussed in Chapter II, p. 130. We shall establish below, p. 480, that unlabelled functional graphs also belong to the exp–log schema. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Example VII.4. Factors of polynomials over finite fields. Factorization properties of random polynomials over finite fields are of importance in various areas of mathematics and have applications to coding theory, symbolic computation, and cryptography [51, 599, 541]. Example I.20, p. 90, offers a preliminary discussion. Let F p be the finite field with p elements and P ⊂ F p [X ] the set of monic polynomials with coefficients in the field. We view these polynomials as (unlabelled) combinatorial objects with size identified to degree. Since a polynomial is specified by the sequence of its coefficients, one has, with A the “alphabet” of coefficients, A = F p treated as a collection of atomic objects: (15)

P = S EQ(A)

H⇒

P(z) =

1 , 1 − pz

On the other hand, the unique factorization property of polynomials entails that the class I of all monic irreducible polynomials and the class P of all polynomials are related by P = MS ET(I).

450

VII. APPLICATIONS OF SINGULARITY ANALYSIS (X + 1) X 10 + X 9 + X 8 + X 6 + X 4 + X 3 + 1 X 14 + X 11 + X 10 + X 3 + 1 2 X 3 (X + 1) X 2 + X + 1 X 17 + X 16 + X 15 + X 11 + X 9 + X 6 + X 2 + X + 1 5 5 3 2 12 8 7 6 5 3 2 X (X + 1) X + X + X + X + 1 X + X + X + X + X + X + X + X + 1 X 2 + X + 1 2 X2 X2 + X + 1 X3 + X2 + 1 X8 + X7 + X6 + X4 + X2 + X + 1 X8 + X7 + X5 + X4 + 1 X 7 + X 6 + X 5 + X 3 + X 2 + X + 1 X 18 + X 17 + X 13 + X 9 + X 8 + X 7 + X 6 + X 4 + 1

Figure VII.3. The factorizations of five random polynomials of degree 25 over F2 . One out of five polynomials in this sample has no root in the base field (the asymptotic probability is 14 by Note VII.4).

As a consequence of M¨obius inversion, one then gets (Equation (94) of Chapter I, p. 91): X µ(k) 1 1 + R(z), R(z) := log . (16) I (z) = log 1−z k 1 − pz k k≥2

Regarding complex asymptotics, the function R(z) of (16) is analytic in |z| < p−1/2 . Thus I (z) is of logarithmic type with radius of convergence 1/ p and parameters X µ(k) 1 κ = 1, λ= log . k 1 − p1−k k≥2

As already noted in Chapter I, a consequence is the asymptotic estimate In ∼ p n /n, which constitutes a “Prime Number Theorem” for polynomials over finite fields: a fraction asymptotic to 1/n of the polynomials in F p [X ] are irreducible. Furthermore, since I (z) is logarithmic and P is obtained by a multiset construction, we have an unlabelled exp–log scheme, to which Theorem VII.1 applies. As a consequence: The number of factors of a random polynomial of degree n has mean and variance each asymptotic to log n; its distribution is concentrated. (See Figure VII.3 for an illustration; the mean value estimate appears in [378, Ex. 4.6.2.5].) We shall revisit this example in Chapter IX, p. 672, and establish a companion Gaussian limit law for the number of irreducible factors in a random polynomial of large degree. This and similar developments lead to a complete analysis of some of the basic algorithms known for factoring polynomials over finite fields; see [236]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

VII.2. The divisor function for polynomials. Let δ(̟ ) for ̟ ∈ P be the total number of e

e

monic polynomials (not necessarily irreducible) dividing ̟ : if ̟ = ι11 · · · ιkk , where the ι j are distinct irreducibles, then δ(̟ ) = (e1 + 1) · · · (ek + 1). One has Q [z n ] j≥1 (1 + 2z j + 3z 2 j + · · · ) [z n ]P(z)2 = n EPn (δ) = , Q n j 2 j [z ]P(z) [z ] j≥1 (1 + z + z + · · · )

so that the mean value of δ over Pn is exactly (n + 1). This evaluation is relevant to polynomial factorization over Z since it gives an upper bound on the number of irreducible factor combinations that need to be considered in order to lift a factorization from F p (X ) to Z(X ); see [379, 599].

VII.3. The cost of finding irreducible polynomials. Assume that it takes expected time t (n) to test a random polynomial of degree n for irreducibility. Then it takes expected time ∼ nt (n) to find a random irreducible polynomial of degree n: simply draw a polynomial at random and test it for irreducibility. (Testing for irreducibility can itself be achieved by developing a polynomial

VII. 2. SETS AND THE EXP–LOG SCHEMA

451

factorization algorithm which is stopped as soon as a non-trivial factor is found. See works by Panario et al. for detailed analyses of this strategy [468, 469].)

Profiles of exp–log structures. Under the exp–log conditions, it is also possible to analyse the profile of structures, that is, the number of components of size r for each fixed r . The Poisson distribution (Appendix C.4: Special distributions, p. 774) of parameter ν is the law of a discrete random variable Y such that νk . k! A variable Y is said to be negative binomial of parameter (m, α) if its probability generating function and its individual probabilities satisfy: m+k−1 k 1−α m α (1 − α)m . , P(Y = k) = E(u Y ) = k 1 − αu (The quantity P(Y = k) is the probability that the mth success in a sequence of independent trials with individual success probability α occurs at time m + k; see [206, p. 165] and Appendix C.4: Special distributions, p. 774.) Proposition VII.1 (Profiles of exp–log structures). Assume the conditions of Theorem VII.1 and let X (r ) be the number of G–components of size r in an F–object. In the labelled case, X (r ) admits a limit distribution of the Poisson type: for any fixed k, E(u Y ) = e−ν(1−u) ,

(17)

P(Y = k) = e−ν

νk , ν = gr ρ r , gr ≡ [z r ]G(z). k! admits a limit distribution of the negative-binomial type:

lim PFn (X (r ) = k) = e−ν

n→∞

In the unlabelled case, X (r ) for any fixed k, (18) Gr + k − 1 k (r ) α (1 − α)G r , lim PFn (X = k) = n→∞ k

α = ρ r , G r ≡ [z r ]G(z).

Proof. In the labelled case, the BGF of F with u marking the number X (r ) of r – components is F(z, u) = exp (u − 1)gr z r F(z).

Extracting the coefficient of u k leads to

φk (z) := [u k ]F(z, u) = exp −gr z r

(gr z r )k

F(z). k! The singularity type of φk (z) is that of F(z) since the prefactor (an exponential multiplied by a polynomial) is entire, so that singularity analysis applies directly. As a consequence, one finds (gr ρ r )k

· [z n ]F(z) , k! which provides the distribution of X (r ) under the form stated in (17). In the unlabelled case, the starting BGF equation is 1 − zr Gr F(z, u) = F(z), 1 − uz r [z n ]φk (z) ∼ exp −gr ρ r

452

VII. APPLICATIONS OF SINGULARITY ANALYSIS

and the analytic reasoning is similar to the labelled case.

Proposition VII.1 will be revisited in Example IX.23, p. 675, when we examine continuity theorems for probability generating functions. Its unlabelled version covers in particular polynomials over finite fields; see [236, 372] for related results.

VII.4. Mean profiles. The mean value of X (r ) satisfies EFn (X (r ) ) ∼ gr ρ r ,

EFn (X (r ) ) ∼ G r

ρr , 1 − ρr

in the labelled and unlabelled (multiset) case, respectively. In particular: the mean number of p roots of a random polynomial over F p that lie in the base field F p is asymptotic to p−1 . Also: the probability that a polynomial has no root in the base field is asymptotic to (1 − 1/ p) p . (For random polynomials with real coefficients, a famous result of Kac (1943) asserts that the mean number of real roots is ∼ π2 log n; see [185].)

VII.5. Profiles of powersets. In the case of unlabelled powersets F = PS ET(G) (no repetitions of elements allowed), the distribution of X (r ) satisfies Gr k α (1 − α)G r −k , lim PFn (X (r ) = k) = n→∞ k

i.e., the limit is a binomial law of parameters (G r , ρ r /(1 + ρ r )).

α=

ρr ; 1 + ρr

VII. 3. Simple varieties of trees and inverse functions A unifying theme in this chapter is the enumeration of rooted trees determined by restrictions on the collection of allowed node degrees (Sections I. 5, p. 64 and II. 5, p. 125). Some set ⊆ Z≥0 containing 0 (for leaves) and at least another number d ≥ 2 (to avoid trivialities) is fixed; in the trees considered, all outdegrees of nodes are constrained to lie in . Corresponding to the four combinations, unlabelled/labelled and plane/non-plane, there are four types of functional equations summarized by Figure VII.4. In three of the four cases, namely, unlabelled plane, labelled plane, and labelled non-plane, the generating function (OGF for unlabelled, EGF for labelled) satisfies an equation of the form (19)

y(z) = zφ(y(z)).

In accordance with earlier conventions (p. 194), we name simple variety of trees any family of trees whose GF satisfies an equation of the form (19). (The functional equation satisfied by the OGF of a degree-restricted variety of unlabelled non-plane trees furthermore involves a P´olya operator 8, which implies the presence of terms of the form y(z 2 ), y(z 3 ), . . .: such cases are discussed below in Section VII. 5.) The relation y = zφ(y) has already been examined in Section VI. 7, p. 402, from the point of view of singularity analysis. For convenience, we encapsulate into a definition the conditions of the main theorem of that section, Theorem VI.6, p. 404.

VII. 3. SIMPLE VARIETIES OF TREES AND INVERSE FUNCTIONS

Unlabelled (OGF)

plane

non-plane

V = Z × S EQ (V)

V = Z × MS ET (V)

V (z) = zφ(V (z)) P φ(u) := ω∈ u ω

V (z) = z8(V (z))) (8 a P´olya operator)

b(z) = zφ(V b(z)) V P φ(u) := ω∈ u ω

b(z) = zφ(V b(z)) V P ω φ(u) := ω∈ uω!

V = Z ⋆ S EQ (V)

Labelled (EGF)

453

V = Z ⋆ S ET (V)

Figure VII.4. Functional equations satisfied by generating functions (OGF V (z) or b(z)) of degree-restricted families of trees. EGF V

Definition VII.3. Let y(z) be a function analytic at 0. It is said to belong to the smooth inverse-function schema if there exists a function φ(u) analytic at 0, such that, in a neighbourhood of 0, one has y(z) = zφ(y(z)), and φ(u) satisfies the following conditions. (H1 ) The function φ(u) is such that (20)

φ(0) 6= 0,

[u n ]φ(u) ≥ 0,

φ(u) 6≡ φ0 + φ1 u.

(H2 ) Within the open disc of convergence of φ at 0, |z| < R, there exists a (necessarily unique) positive solution to the characteristic equation: (21)

∃τ, 0 < τ < R,

φ(τ ) − τ φ ′ (τ ) = 0.

A class Y whose generating function y(z) (either ordinary or exponential) satisfies these conditions is also said to belong to the smooth inverse-function schema. The schema is said to be aperiodic if φ(u) is an aperiodic function of u (Definition IV.5, p. 266). VII. 3.1. Asymptotic counting. As we saw on general grounds in Chapters IV and VI, inversion fails to be analytic when the first derivative of the function to be inverted vanishes. The heart of the matter is that, at the point of failure y = τ , corresponding to z = τ/φ(τ ) (the radius of convergence of y(z) at 0), the dependency y 7→ z becomes quadratic, so that its inverse z 7→ y gives rise to a square-root singularity (hence the characteristic equation). From here, the typical n −3/2 term in coefficient asymptotics results (Theorem VI.6, p. 404). In view of our needs in this chapter, we rephrase Theorem VI.6 as follows. Theorem VII.2. Let y(z) belong to the smooth inverse-function schema in the aperiodic case. Then, with τ the positive root of the characteristic equation and ρ =

454

VII. APPLICATIONS OF SINGULARITY ANALYSIS

τ/φ(τ ), one has n

[z ]y(z) =

s

φ(τ ) ρ −n 1 . 1+O √ ′′ 3 2φ (τ ) π n n

As we also know from Theorem√VI.6 (p. 404), a complete (and locally convergent) expansion of y(z) in powers of 1 − z/ρ exists, starting with s p 2φ(τ ) γ := (22) y(z) = τ − γ 1 − z/ρ + O (1 − z/ρ) , , φ ′′ (τ )

n which √ implies a complete asymptotic expansion for yn = [z ]y(z) in odd powers of 1/ n. (The statement extends to the aperiodic case, with the necessary condition that n ≡ 1 mod p, when φ has period p.) We have seen already that this framework covers binary, unary–binary, general Catalan, as well as Cayley trees (Figure VI.10, p. 406). Here is another typical application.

Example VII.5. Mobiles. A (labelled) mobile, as defined by Bergeron, Labelle, and Leroux [50, p. 240], is a (labelled) tree in which subtrees dangling from the root are taken up to cyclic shift:

1

2

3! + 3 = 9

4! + 4 × 2 + 4 × 3 + 4 × 3 × 2 = 68

(Think of Alexander Calder’s creations.) The specification and EGF equation are 1 . M = Z ⋆ (1 + C YC M) H⇒ M(z) = z 1 + log 1 − M(z) (By definition, cycles have at least one components, so that the neutral structure must be added 2 3 4 5 to allow for leaf creation.) The EGF starts as M(z) = z + 2 z2! + 9 z3! + 68 z4! + 730 z5! + · · · , whose coefficients constitute EIS A038037. The verification of the conditions of the theorem are immediate. We have φ(u) = 1 + log(1 − u)−1 , whose radius of convergence is 1. The characteristic equation reads 1 + log

τ 1 − = 0, 1−τ 1−τ

. which has a unique positive root at τ = 0.68215. (In fact, one has τ = 1 − 1/T (e−2 ), with T the Cayley tree function.) The radius of convergence is ρ ≡ 1/φ ′ (τ ) = 1 − τ . The asymptotic formula for the number of mobiles then results: 1 Mn ∼ C · An n −3/2 , n!

. where C = 0.18576,

. A = 3.14461.

(This example is adapted from [50, p. 261], with corrections.) . . . . . . . . . . . . . . . . . . . . . . . . . . .

VII. 3. SIMPLE VARIETIES OF TREES AND INVERSE FUNCTIONS

455

VII.6. Trees with node degrees that are prime numbers. Let P be the class of all unlabelled plane trees such that the (out)degrees of internal nodes belong to the set of prime numbers, {2, 3, 5, . . .}. One has P(z) = z + z 3 + z 4 + 2 z 5 + 6 z 6 + 8 z 7 + 29 z 8 + 50 z 9 + · · · , and . Pn ∼ C An n −3/2 , with A = 2.79256 84676. The asymptotic form “forgets” many details of the distribution of primes, so that it can be obtained to great accuracy. (Compare with Example V.2, p. 297 and Note VII.24, p. 480.) VII. 3.2. Basic tree parameters. Throughout this subsection, we consider a simple variety of trees V, whose generating function (OGF or EGF, as the case may be) will be denoted by y(z), satisfying the inverse relation y = zφ(y). In order to place all cases under a single umbrella, we shall write yn = [z n ]y(z), so that the number of trees of size n is either Vn = yn (unlabelled case) or Vn = n!yn (labelled case). We postulate throughout that y(z) belongs to the smooth inverse-function schema and is aperiodic. As already seen on several occasions in Chapter III (Section III. 5, p. 181), additive parameters lead to generating functions that are expressible in terms of the basic tree generating function y(z). Now that singularity analysis is available, such generating functions can be exploited systematically, with a wealth of asymptotic estimates relative to trees of large sizes coming within easy reach. The universality of the square-root singularity among varieties of trees that satisfy the smoothness assumption of Definition VII.3 then implies universal behaviour for many tree parameters, which we now list. (i) Node degrees. The degree of the root in a large random tree is O(1) on average and with high probability, and its asymptotic distribution can be generally determined (Example VII.6). A similar property holds for the degree of a random node in a random tree (Example VII.8). (ii) Level profiles can also be determined. The quantity of interest is the mean number of nodes in the kth layer from the root in a random tree. It is seen for instance that, near the root, a tree from a simple variety tends to grow linearly (Example VII.7), this in sharp contrast with other random tree models (for instance, increasing trees, Subsection VII. 9.2, p. 526), where the growth is exponential. This property is one of the numerous indications that random trees taken from simple varieties are skinny and far from having a well-balanced√shape. A related property is the fact that path length is on average O(n n) (Example VII.9), which means that the typical depth of a √ random node in a random tree is O( n). These basic properties are only the tip of an iceberg. Indeed, Meir and Moon, who launched the study of simple varieties of trees (the seminal paper [435] can serve as a good starting point) have worked out literally several dozen analyses of parameters of trees, using a strategy similar to the one presented here6. We shall have occasion, in Chapter IX, to return to probabilistic properties of simple varieties of trees satisfying the smooth inverse-function schema—we only indicate here for completeness that 6The main difference is that Meir and Moon appeal to the Darboux–P´olya method discussed in Sec-

tion VI. 11 (p. 433) instead of singularity analysis.

456

VII. APPLICATIONS OF SINGULARITY ANALYSIS

Tree

φ(w)

τ, ρ

PGF of root degree

simple variety

—

—

uφ ′ (τ u)/φ ′ (τ )

binary

(1 + w)2

1, 41

1 u + 1 u2 2 2 1 u + 2 u2 3 3 u/(2 − u)2

unary–binary general Cayley

1 + w + w2 (1 − w)−1 ew

1, 31 1 1 2, 4 1, e−1

ueu−1

(type)

(Bernoulli) (Bernoulli) (sum of two geometric) (shifted Poisson)

Figure VII.5. The distribution of root degree in simple varieties of trees of the smooth inverse-function schema.

√ height is known generally to scale as n and is associated to a limiting theta distribution (see Proposition V.4, p. 329 for the case of Catalan trees and Subsection VII. 10.2, p. 535, for general results), with similar properties holding true for width as shown by Odlyzko–Wilf and Chassaing–Marckert–Yor [112, 463]. Example VII.6. Root degrees in simple varieties. Here is an immediate application of singularity analysis, one that exemplifies the synthetic type of reasoning that goes along with the method. Take for notational simplicity a simple family V that is unlabelled, with OGF V (z) ≡ y(z). Let V [k] be the subset of V composed of all trees whose root has degree equal to k. Since a tree in V [k] is formed by appending a root to a collection of k trees, one has V [k] (z) = φk zy(z)k ,

φk := [wk ]φ(w).

For any fixed k, a singular expansion results from raising both members of (22) to the kth power; in particular, r z z (23) V [k] (z) = φk z τ k − kγ τ k−1 1 − + O 1 − . ρ ρ This is to be compared with the basic estimate (22): the ratio Vn[k] /Vn is then asymptotic to √ the ratio of the coefficients of 1 − z/ρ in the corresponding generating functions, V [k] (z) and V (z) ≡ y(z). Thus, for any fixed k, we have found that (24)

Vn[k] = ρkφk τ k−1 + O(n −1/2 ). Vn

(The error term can be strengthened to O(n −1 ) by pushing the expansion one step further.) The ratio Vn[k] /Vn is the probability that the root of a random tree of size n has degree k. Since ρ = 1/φ ′ (τ ), one can rephrase (24) as follows: In a smooth simple variety of trees, the random variable 1 representing root-degree admits a discrete limit distribution given by (25)

lim PVn (1 = k) =

n→∞

kφk τ k−1 . φ ′ (τ )

(By general principles expounded in Chapter IX, convergence is uniform.) Accordingly, the probability generating function (PGF) of the limit law admits the simple expression EVn u 1 = uφ ′ (τ u)/φ ′ (τ ).

VII. 3. SIMPLE VARIETIES OF TREES AND INVERSE FUNCTIONS

457

The distribution is thus characterized by the fact that its PGF is a scaled version of the derivative of the basic tree constructor φ(w). Figure VII.5 summarizes this property together with its specialization to our four pilot examples. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Additive functionals. Singularity analysis applies to many additive parameters of trees. Consider three tree parameters, ξ, η, σ satisfying the basic relation, deg(t)

ξ(t) = η(t) +

(26)

X

σ (t j ),

j=1

which can be taken to define ξ(t) in terms of the simpler parameter η(t) (a “toll”, cf Subsection VI. 10.3, p. 427) and the sum of values of σ over the root subtrees of t (with deg(t) the degree of the root and t j the jth root-subtree of t). In the Pcase of a recursive parameter, ξ ≡ σ , unwinding the recursion shows that ξ(t) := st η(s), where the sum is extended to all subtrees s of t. As we are interested in average-case analysis, we introduce the cumulative GFs, X X X (27) 4(z) = ξ(t)z |t| , H (z) = η(t)z |t| , 6(z) = σ (t)z |t| , t

t

t

assuming again an unlabelled variety of trees for simplicity. We first state a simple algebraic result which formalizes several of the calculations of Section III. 5, p. 181, dedicated to recursive tree parameters.

Lemma VII.1 (Iteration lemma for trees). For tree parameters from a simple variety with GF y(z) that satisfy the additive relation (26), the cumulative generating functions (27), are related by 4(z) = H (z) + zφ ′ (y(z))6(z).

(28)

In particular, if ξ is defined recursively in terms of η, that is, σ ≡ ξ , one has 4(z) =

(29)

zy ′ (z) H (z) = H (z). 1 − zφ ′ (y(z)) y(z)

Proof. We have e(z), 4(z) = H (z) + 4

where

e(z) := 4

X t∈V

z |t|

deg(t)

X j=1

σ (t j ) .

e(z) according to the values r of root degree, we find Spitting the expression of 4 X e(z) = 4 φr z 1+|t1 |+···+|tr | (σ (t1 ) + σ (t2 ) + · · · + σ (tr )) r ≥0

X

φr 6(z)y(z)r −1 + y(z)6(z)y(z)r −2 + · · · y(z)r −1 6(z)

=

z

=

z6(z) ·

r ≥0

X r φr y(z)r −1 , r ≥0

which yields the linear relation expressing 4 in (28).

458

VII. APPLICATIONS OF SINGULARITY ANALYSIS

In the recursive case, the function 4 is determined by a linear equation, namely 4(z) = H (z) + zφ ′ (y(z))4(z), which, once solved, provides the first form of (29). Differentiation of the fundamental relation y = zφ(y) yields the identity y y y ′ (1 − zφ ′ (y)) = φ(y) = , i.e., 1 − zφ ′ (y) = ′ , z zy

from which the second form results.

VII.7. A symbolic derivation. For a recursive parameter, we can view 4(z) as the GF of trees with one subtree marked, to which is attached a weight of η. Then (29) can be interpreted as follows: point to an arbitrary node at a tree in V (the GF is zy ′ (z)), remove the tree attached to this node (a factor of y(z)−1 ), and replace it by the same tree but now weighted by η (the GF is H (z)). VII.8. Labelled varieties. Formulae (28) and (29) hold verbatim for labelled trees (either of the plane or non-plane type), provided we interpret y(z), 4(z), H (z) as EGFs: 4(z) := P |t| t∈V ξ(t)z /|t|!, and so on. Example VII.7. Mean level profile in simple varieties. The question we address here is that of determining the mean number of nodes at level k (i.e., at distance k from the root) in a random tree of some large size n. (An explicit expression for the joint distribution of nodes at all levels has been developed in Subsection III. 6.2, p. 193, but this multivariate representation is somewhat hard to interpret asymptotically.) Let ξk (t) be the number of nodes at level k in tree t. Define the generating function of cumulated values, X X k (z) := ξk (t)z |t| . t∈V

Clearly, X 0 (z) ≡ y(z) since each tree has a unique root. Then, since the parameter ξk is the sum over subtrees of parameter ξk−1 , we are in a situation exactly covered by (28), with η(t) ≡ 0. The recurrence X k (z) = zφ ′ (y(z))4k−1 (z), is then immediately solved, to the effect that k (30) X k (z) = zφ ′ (y(z)) y(z).

Making use of the (analytic) expansion of φ ′ at τ , namely, φ ′ (y) ∼ φ ′ (τ ) + φ ′′ (τ )(y − τ ) and of ρφ ′ (τ ) = 1, one obtains, for any fixed k: r r r z z z τ −γ 1− ∼ τ − γ (τρφ ′′ (τ )k + 1) 1 − . X k (z) ∼ 1 − kγρφ ′′ (τ ) 1 − ρ ρ ρ Thus comparing the singular part of X k (z) to that of y(z), we find: For fixed k, the mean number of nodes at level k in a tree is of the asymptotic form EVn [ξk ] ∼ Ak + 1,

A := τρφ ′′ (τ ).

This result was first given by Meir and Moon [435]. The striking fact is that, although the number of nodes at level k can at least double at each level, growth is only linear on average. In figurative terms, the immediate vicinity of the root starts like a “cone”, and trees of simple varieties tend to be rather skinny near their base. When used in conjunction with saddle-point bounds (p. 246), the exact GF expression of (30) additionally provides a probabilistic upper bound on the height of trees of the form O(n 1/2+δ ) for any δ > 0. Indeed restrict z to the interval (0, ρ) and assume that k = n 1/2+δ . Let χ be the height parameter. First, we have (31)

PVn (χ ≥ k) ≡ EVn ([[ξk ≥ 1]]) ≤ EVn (ξk ).

VII. 3. SIMPLE VARIETIES OF TREES AND INVERSE FUNCTIONS

459

Figure VII.6. Three random 2–3 trees ( = {0, 2, 3}) of size n = 500 have√height, respectively, 48, 57, 47, in agreement with the fact that height is typically O( n).

Next by saddle-point bounds, for any legal positive x (that is, 0 < x < Rconv (φ)), k k (32) EVn (ξk ) ≤ xφ ′ (y(x)) y(x)x −n ≤ τ xφ ′ (y(x)) x −n .

δ Fix now x = ρ − nn . Local expansions then show that k (33) log xφ ′ (y(x)) x −n ≤ −K n 3δ/2 + O n δ ,

for some positive constant K . Thus, by (31) and (33): In a smooth simple variety of trees, the probability of height exceeding n 1/2+δ is exponentially small, being of the rough form exp(−n 3δ/2 ). Accordingly, the mean height is O(n 1/2+δ ) for√any δ > 0. The moments of height were characterized in [246]: the mean is asymptotic to λ n and the limit distribution is of the Theta type encountered in Example V.8, p. 326, in the particular case of general Catalan trees, where explicit expressions are available. (Further local limit and large deviation estimates appear in [230]; we shall return to the topic of tree height in Subsection VII. 10.1, p. 532.) Figure VII.6 displays three random trees of size n = 500. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

VII.9. The variance of level profiles. The BGF of trees with u marking nodes at level k has an explicit expression, in accordance with the developments of Chapter III. For instance for k = 3, this is zφ(zφ(zφ(uy(z)))). Double differentiation followed by singularity analysis shows that 1 1 VVn [ξk ] ∼ A2 k 2 − A(3 − 4A)k + τ A − 1, 2 2 another result of Meir and Moon [435]. The precise √ analysis of the mean and variance in the interesting regime where k is proportional to n is also given in [435], but it requires either the saddle-point method (Chapter VIII) or the adapted singularity analysis techniques of Theorem IX.16, p. 709. Example VII.8. Mean degree profile. Let ξ(t) ≡ ξk (t) be the number of nodes of degree k in random tree of some variety V. The analysis extends that of the root degree seen earlier. The parameter ξ is an additive functional induced by the basic parameter η(t) ≡ ηk (t) defined by

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VII. APPLICATIONS OF SINGULARITY ANALYSIS

ηk (t) := [[deg(t) = k]]. By the analysis of root degree, we have for the GF of cumulated values associated to η H (z) = φk zy(z)k , φk := [wk ]φ(w), so that, by the fundamental formula (29),

X (z) = φk zy(z)k

zy ′ (z) = z 2 φk y(z)k−1 y ′ (z). y(z)

The singular expansion of zy ′ (z) can be obtained from that of y(z) by differentiation (Theorem VI.8, p. 419), 1 1 zy ′ (z) = γ √ + O(1), 2 1 − z/ρ

the corresponding coefficient satisfying [z n ](zy ′ ) = nyn . This gives immediately the singularity type of X , which is of the form of an inverse square root. Thus, X (z) ∼ ρφk τ k−1 (zy ′ (z)) implying (ρ = τ/φ(τ ))

Xn φk τ k ∼ . nyn φ(τ )

Consequently, one has: Proposition VII.2. In a smooth simple variety of trees, the mean number of nodes of degree k is asymptotic to λk n, where λk := φk τ k /φ(τ ). Equivalently, the probability distribution of the degree 1⋆ of a random node in a random tree of size n satisfies lim Pn (1⋆ ) = λk ≡

n→∞

φk τ k , φ(τ )

with PGF :

X k

λk u k =

φ(uτ ) . φ(τ )

For the usual tree varieties this gives: Tree

φ(w)

τ, ρ

probability distribution

(type)

binary

(1 + w)2

1, 41 1, 31 1, 1 2 4 1, e−1

PGF: 41 + 12 u + 14 u 2 PGF: 31 + 13 u + 13 u 2

(Bernoulli)

unary–binary general Cayley

1 + w + w2 (1 − w)−1 ew

PGF: 1/(2 − u) PGF: eu−1

(Bernoulli) (Geometric) (Poisson)

For instance, asymptotically, a general Catalan tree has on average n/2 leaves, n/4 nodes of degre 1 n/8 of degree 2, and so on; a Cayley tree has ∼ ne−1 /k! nodes of degree k; for binary (Catalan) trees, the four possible types of nodes each appear with asymptotic frequency 1/4. (These data agree with the fact that a random tree under Vn is distributed like a branching process tree determined by the PGF φ(uτ )/φ(τ ); see Subsection III. 6.2, p. 193.) . . . . . . . . .

VII.10. Variances. The variance of the number of k–ary nodes is ∼ νn, so that the distribution of the number of nodes of this type is concentrated, for each fixed k. The starting point is the BGF defined implicitly by Y (z, u) = z φ(Y (z, u)) + φk (u − 1)Y (z, u)k , upon taking a double derivative with respect to u, setting u = 1, and finally performing singularity analysis on the resulting GF.

VII. 3. SIMPLE VARIETIES OF TREES AND INVERSE FUNCTIONS

461

VII.11. The mother of a random node. The discrepancy in distributions between the root degree and the degree of a random node deserves an explanation. Pick up a node distinct from the root at random in a tree and look at the degree of its mother. The PGF of the law is in the limit uφ ′ (uτ )/φ ′ (τ ). Thus the degree of the root is asymptotically the same as that of the mother of any non-root node. More generally, let X have distribution pk := P(X = k). Construct a random variable Y such that the probability qk := P(Y = k) is proportional both to k and pk . Then for the associated PGFs, the relation q(u) = p′ (u)/ p′ (1) holds. The law of Y is said to be the sizebiased version of the law of X . Here, a mother is picked up with an importance proportional to its degree. In this perspective, Eve appears to be just like a random mother. Example VII.9. Path length. Path length of a tree is the sum of the distances of all nodes to the root. It is defined recursively by ξ(t) = |t| − 1 +

deg(t) X

ξ(t j )

j=1

(Example III.15, p. 184 and Subsection VI. 10.3, p. 427). Within the framework of additive functional of trees (28), we have η(t) = |t| − 1 corresponding to the GF of cumulated values H (z) = zy ′ (z) − y(z), and the fundamental relation (29) gives X (z) = (zy ′ (z) − y(z))

z 2 y ′ (z)2 zy ′ (z) = − zy ′ (z). y(z) y(z)

The type of y ′ (z) at its singularity is Z −1/2 , where Z := (1 − z/ρ). The formula for X (z) involves the square of y ′ , so that the singularity of X (z) is of type Z −1 , resembling a simple pole. This means that the cumulated value X n = [z n ]X (z) grows like ρ −n , so that the mean value of ξ over Vn has growth n 3/2 . Working out the constants, we find X (z) + zy ′ (z) ∼

γ2 1 + O(Z −1/2 ). 4τ Z

As a consequence: Proposition VII.3. In a random tree of size n from a smooth simple variety, the expectation of path length satisfies s p φ(τ ) (34) EVn (ξ ) = λ π n 3 + O(n), . λ := 2τ 2 φ ′′ (τ ) For our classical varieties, the main terms of (34) are then: Binary √ ∼ π n3

unary–binary √ ∼ 12 3π n 3

general √ ∼ 21 π n 3

Cayley q ∼ 12 π n 3 .

Observe that the quantity n1 EVn (ξ ) represents the expected depth of a random node in a random √ tree (the model is then [1 . . n]×Vn ), which is thus ∼ λ n. (This result is consistent with height 1/2 of a tree being with high probability of order O(n ).) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

VII.12. Variance of path length. Path length can be analysed starting from the bivariate generating function given by a functional equation of the difference type (see Chapter III, p. 185), which allows for the computation of higher moments. The standard deviation is found to be asymptotic to 32 n 3/2 for some computable constant 32 > 0, so that the distribution is spread. Louchard [416] and Tak´acs [566] have additionally worked out the asymptotic form of all moments, leading to a characterization of the limit law of path length that can be described in terms of the Airy function: see Subsection VII. 10.1, p. 532.

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VII. APPLICATIONS OF SINGULARITY ANALYSIS

# components # cyclic nodes

∼ 12 log n √ ∼ π n/2

Tail length (λ)

∼ ne−k /k!

Component size

Cycle length (µ)

∼ ne−1

# terminal nodes # nodes of in-degree k

Tree size

∼

∼

√

√

π n/8 π n/8

∼ n/3

∼ 2n/3

Figure VII.7. Expectations of the main additive parameters of random mappings of size n.

VII.13. Generalizations P of path length. Define the subtree size index of order α ∈ R≥0 to be ξ(t) ≡ ξα (t) := st |s|α , where the sum is extended to all the subtrees s of t. This corresponds to a recursively defined parameter with η(t) = |t|α . The results of Section VI. 10 relative to Hadamard products and polylogarithms make it possible to analyse the singularities of H (z) and X (z). It is found that there are three different regimes α > 12 EVn (ξ ) ∼ K α n α

α = 21 EVn (ξ ) ∼ K 1/2 n log n

α < 12 EVn (ξ ) ∼ K α n

where each K α is a computable constant. (This extends the results of Subsection VI. 10.3, p. 427 to all simple varieties of trees that are smooth.)

VII. 3.3. Mappings. The basic construction of mappings (Chapter II, p. 129), F = exp(K ) F = S ET(K) 1 K = C YC(T ) H⇒ (35) K = log 1 − T T = Z ⋆ S ET(T ) T = ze T ,

builds maps from Cayley trees, which constitute a smooth simple variety. The construction lends itself to a number of multivariate extensions. For instance, we already know from Example VII.3, p. 449, that the number of components is asymptotic to 21 log n, both on average and in probability. Take next the parameter χ equal to the number of cyclic points, which gives rise to the BGF 1 F(z, u) = exp log = (1 − uT )−1 . 1 − uT The mean number of a cyclic points, for a random mapping of size n, is accordingly T n! ∂ n! F(z, u) (36) µn ≡ EFn [χ ] = n [z n ] . = n [z n ] n ∂u n (1 − T )2 u=1

Singularity analysis is immediate, since T (1 − T )2

∼

z→e−1

1 1 2 1 − ez

−→

[z n ]

T 1 n e . ∼ 2 n→∞ 2 (1 − T )

Thus: √ The mean number of cyclic points in a random mapping of size n is asymptotic to π n/2. Many parameters can be similarly analysed in a systematic manner, thanks to generating function, as shown in the survey [247]: see Figure VII.7 for a summary

VII. 3. SIMPLE VARIETIES OF TREES AND INVERSE FUNCTIONS

463

Figure VII.8. Two views of a random mapping of size n = 100. The random mapping has three connected components, with cycles of respective size 2, 4, 4; it is made of fairly skinny trees, has a giant component of size 75, and its diameter equals 14.

of results whose proofs we leave as exercises to the reader. The left-most table describes global parameters of mappings; the right-most table is relative to properties of random point in random n-mapping: λ is the distance to its cycle of a random point, µ the length of the cycle to which the point leads, tree size and component size are, respectively, the size of the largest tree containing the point and the size of its (weakly) connected component. In particular, a random mapping of size n has relatively few components, some of which are expected to be of a large size. The estimates of Figure VII.7 are in fair agreement with what is observed on the single sample of size n = 100 of Figure VII.8: this particular mapping has 3 components (the average is about 2.97), 10 cyclic points (the average, as calculated in (36), is about 12.20), but a fairly large diameter—the maximum value of λ + µ, taken over all nodes—equal to 14, and a giant component of size 75. The proportion of nodes of degree 0, 1, 2, 3, 4 turns out to be, respectively, 39%, 33%, 21%, 7%, 1%, to be compared against the asymptotic values given by a Poisson law of rate 1 (analogous to the degree profile of Cayley trees found in Example VII.8); namely 36.7%, 36.7%, 18.3%, 6.1%, 1.5%.

VII.14. Extremal statistics on mappings. Let λmax , µmax , and ρ max be the maximum values of λ, µ, and ρ, taken over all the possible starting points, where ρ = λ + µ. Then, the expectations satisfy [247] √ √ √ EFn (λmax ) ∼ κ1 n, EFn (µmax ) ∼ κ2 n, EFn (ρ max ) ∼ κ3 n, √ . . . where κ1 = 2π log 2 = 1.73746, κ2 = 0.78248 and κ3 = 2.4149. (For the estimate relative max to ρ , see also [12].) The largest tree and the largest components have expectations asymptotic, respectively, to . . δ1 n and δ2 n, where δ1 = 0.48 and δ2 = 0.7582.

464

VII. APPLICATIONS OF SINGULARITY ANALYSIS

The properties outlined above for the class of all mappings also prove to be universal for a wide variety of mappings defined by degree restrictions of various sorts: we outline the basis of the corresponding theory in Example VII.10, then show some surprising applications in Example VII.11. Example VII.10. Simple varieties of mappings. Let be a subset of the integers containing 0 and at least another integer greater than 1. Consider mappings φ ∈ F such that the number of preimages of any point is constrained to lie in . Such special mappings may serve to model the behaviour of special classes of functions under iteration, and are accordingly of interest in various areas of computational number theory and cryptography. For instance, the quadratic functions φ(x) = x 2 + a over F p have the property that each element y has either zero, one, or two preimages (depending on whether y − a is a quadratic non-residue, 0, or a quadratic residue). The basic construction of mappings needs to be amended. Start with the family of trees T that are the simple variety corresponding to : X uω (37) T = zφ(T ), φ(w) := . ω! ω∈

At any vertex on a cycle, one must graft r trees with the constraint that r + 1 ∈ (since one edge is coming from the cycle itself). Such legal tuples with a root appended are represented by U = zφ ′ (T ),

(38)

since φ is an exponential generating function and shift (r 7→ (r + 1)) corresponds to differentiation. Then connected components and components are formed in the usual way by 1 1 , F = exp(K ) = . 1−U 1−U The three relations (37), (38), (39) fully determine the EGF of –restricted mappings. The function φ is a subseries of the exponential function; hence, it is entire and it satisfies automatically the smoothness conditions of Theorem VII.2, p. 453. With τ the characteristic value, the function T (z) then has a square-root singularity at ρ = τ/φ(τ ). The same holds for U , which admits the singular expansion (with γ1 a constant simply related to γ of equation (22)) r z (40) U (z) ∼ 1 − γ1 1 − , ρ (39)

K = log

since U = zφ ′ (T ). Thus, eventually:

F(z) ∼ q

κ , 1 − ρz

κ :=

1 . γ1

There results the universality of an n −1/2 counting law in such constrained mappings: Proposition VII.4. Consider mappings with node degrees in a set ⊆ Z≥0 , such that the corresponding tree family belongs to the smooth implicit function schema and is aperiodic. The number of mappings of size n satisfies s 1 φ ′ (τ )2 κ κ= Fn ∼ √ ρ −n , . n! 2φ(τ )φ ′′ (τ ) πn This statement nicely extends what is known to hold for unrestricted mappings. The analysis of additive functionals can then proceed on lines very similar to the case of standard mappings, to the effect that the estimates of the same form as in Figure VII.7 hold, albeit with

VII. 3. SIMPLE VARIETIES OF TREES AND INVERSE FUNCTIONS

465

different multiplicative factors. The programme just sketched has been carried out in a thorough manner by Arney and Bender [18], whose paper provides a detailed treatment. . . . . . . Example VII.11. Applications of random mapping statistics. There are interesting consequences of the foregoing asymptotic theory of random mappings in several areas of computational mathematics, as we now briefly explain. Random number generators. Many (pseudo) random number generators operate by iterating a given function ϕ over a finite domaine E; usually, E is a large integer interval [0 . . N − 1]. Such a scheme produces a pseudo-random sequence u 0 , u 1 , u 2 , . . ., where u 0 is the “seed” and u n+1 = ϕ(u n ). Particular strategies are known for the choice of ϕ, which ensure that the “period” (the maximum of ρ = λ + µ, where λ is the distance to cycle and µ is the cycle’s length) is of the order of N : this is for instance granted by linear congruential generators and feedback register algorithms; see Knuth’s authoritative discussion in [379, Ch. 3]. By contrast, a randomly chosen √ function ϕ has expected O( N ) cycle time (Figure VII.7, p. 462), so that it is highly likely to give rise to a poor generator. As the popular adage says: “A random random number generator is bad!”. Accordingly, one can make use of the results of Figure VII.7 and Example VII.10 in order to compare statistical properties of a proposed random number generator to properties of a random function, and discard the former if there is a manifest closeness. For instance, take ϕ to be ϕ(x) := x 2 + 1 mod (106 + 3),

6 + 3) is expected to cycle where the modulus is a prime number. A random mapping of size (10√ on average after about 1250 steps (the expectation of ρ = λ + µ is ∼ π N /2 by Figure VII.7). From five starting values u 0 , we observe the following periods

31 314 3141 31415 314159 687 985 813 557 932 √ whose magnitude looks suspiciously like N . Such a random number generator is thus to be discarded. For similar reasons, von Neumann’s well-known “middle-square” procedure (start from an ℓ-digit number, then repeatedly square and extract the middle digits) makes for a rather poor random number generator [379, p. 5]. (Related applications to cryptography are presented by Quisquater and Delescaille in [501].)

(41)

u0 : ρ ≡λ+µ :

3 1569

Floyd’s cycle detection. There is a spectacular algorithm due to Floyd [379, Ex. 3.1.6], for cycle detection, which is well worth knowing when one needs to experiment with large mappings. Given an initial seed x0 and a mapping ϕ, Floyd’s algorithm determines, up to a small factor, the value of ρ(x0 ) = λ(x0 ) + µ(x0 ), using only two registers. The principle is as follows. Start a tortoise and a hare on u 0 at time 0; then, let the tortoise move at speed 1 along the rho-shaped path and let the hare move at twice the speed. After λ(x0 ) steps, the tortoise joins the cycle, from which time on, the hare, which is already on the cycle, will catch the tortoise after at most µ(x0 ) steps, since their speed differential on the cycle is one. Pictorially:

λ

µ

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VII. APPLICATIONS OF SINGULARITY ANALYSIS

In more dignified terms, setting X 0 = u0,

X n+1 = ϕ(X n ),

and

Y0 = u 0 ,

Yn+1 = ϕ(ϕ(X n )),

we have the property that the first value ν such that X ν = Yν ≡ X 2ν must satisfy the inequalities (42)

λ ≤ ν ≤ λ + µ ≤ 2ν.

The corresponding algorithm is then extremely short: Algorithm: Floyd’s Cycle Detector: tortoise := x0 ; hare := x0 ; ν := 0; repeat tortoise :=ϕ(tortoise); hare := ϕ(ϕ(hare)); ν := ν + 1; until tortoise = hare {ν is an estimate of λ + µ in the sense of (42)}. Pollard’s rho method for integer factoring. Pollard [487] had the insight to exploit Floyd’s algorithm in order to develop an efficient integer factoring method. Assume heuristically that a quadratic function x 7→ x 2 + a mod p, with p a prime number, has statistical properties similar to those of a random function (we have verified a particular case by (41) above). It must then √ tend to cycle after about p steps. Let N be a (large) number to be factored, and assume for simplicity that N = pq, with p and q both prime (but unknown!). Choose a random a and a random initial value x0 , fix ϕ(x) = x 2 + a

(mod N ),

and run the hare-and-tortoise algorithm. By the Chinese Remainder Theorem, the value of a number x mod N is determined by the pair (x mod p, x mod q); the tortoise T and the hare H can then be seen as running two simultaneous races, one modulo p, the other modulo q. Say √ that p < q. After about p steps, one is likely to have H≡T

(mod p),

while, most probably, hare and tortoise will be non-congruent mod q. In other words, the greatest common divisor of the difference (H − T ) and N will provide p; hence it factors N . The resulting algorithm is also extremely short: Algorithm: Pollard’s Integer Factoring: choose a, x0 randomly in [0 . . N − 1]; T := x0 ; H := x0 ; repeat T := (T 2 + a) mod N ; H := (H 2 + a)2 + a mod N ; D := gcd(H − T, N ); until D 6= 1 {if D 6= 0, a non-trivial divisor has been found}. The agreement with what the theory of random mappings predicts is excellent: one indeed obtains an algorithm that factors large numbers N in O(N 1/4 ) operations with high probability (see for instance the data in [538, p. 470]). Although Pollard’s algorithm is, for very large N , subsumed by other factoring methods, it is still the best for moderate values of N or for numbers with small divisors, where it proves far superior to trial divisions. Equally importantly, similar ideas serve in many areas of computational number theory; for instance the determination of discrete logarithms. (Proving rigorously what one observes in simulations is another story: it often requires advanced methods of number theory [23, 442].) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

VII. 4. TREE-LIKE STRUCTURES AND IMPLICIT FUNCTIONS

467

VII.15. Probabilities of first-order sentences. A beautiful theorem of Lynch [426], much in line with the global aims of analytic combinatorics, gives a class of properties of random mappings for which asymptotic probabilities are systematically computable. In mathematical logic, a first-order sentence is built out of variables, equality, boolean connectives (∨, ∧, ¬, etc), and quantifiers (∀, ∃). In addition, there is a function symbol ϕ, representing a generic mapping. Theorem. Given a property P expressed by a first-order sentence, let µn (P) be the probability that P is satisfied by a random mapping ϕ of size n. Then the quantity µ∞ (P) = limn→∞ µn (P) exists and its value is given by an expression consisting of integer constants and the operators +, −, ×, ÷, and e x . For instance: P : µ∞ (P) :

ϕ is perm.

ϕ without fixed pt.

∀x∃yϕ(y) = x

∀x¬ϕ(x) = x

0

e−1

ϕ has #leaves ≥ 2

∃x, y [x 6= y ∧ ∀z[ϕ(z) 6= x ∧ ϕ(z) 6= y]] 1

One can express in this language a property like P12 : “all cycles of length 1 are attached to −1+e−1

. The proof of the theotrees of height at most 2”, for which the limit probability is e−1+e rem is based on Ehrenfeucht games supplemented by ingenious inclusion–exclusion arguments. (Many cases, like P12 , can be directly treated by singularity analysis.) Compton [125, 126, 127] has produced lucid surveys of this area, known as finite model theory.

VII. 4. Tree-like structures and implicit functions The aim of this section is to demonstrate the universality of the square-root singularity type for classes of recursively defined structures, which considerably extend the case of (smooth) simple varieties of trees. The starting point is the investigation of recursive classes Y, with associated GF y(z), that correspond to a specification: (43)

Y = G[Z, Y]

H⇒

y(z) = G(z, y(z)).

In the labelled case, y(z) is an EGF and G may be an arbitrary composition of basic constructors, which is reflected by a bivariate function G(z, w); in the unlabelled case, y(z) is an OGF and G may be an arbitrary composition of unions, products, and sequences. (P´olya operators corresponding to unlabelled sets and cycles are discussed in Section VII. 5, p. 475.) This situation covers structures that we have already seen, like Schr¨oder’s bracketing systems (Chapter I, p. 69) and hierarchies (Chapter II, p. 128), as well as new ones to be examined here; namely, paths with diagonal steps and trees with variable node sizes or edge lengths. VII. 4.1. The smooth implicit-function schema. The investigation of (43) necessitates certain analytic conditions to be satisfied by the bivariate function G, which we first encapsulate into the definition of a schema. P Definition VII.4. Let y(z) be a function analytic at 0, y(z) = n≥0 yn z n , with y0 = 0 and yn ≥ 0. The function is said to belong to the smooth implicit-function schema if there exists a bivariate G(z, w) such that y(z) = G(z, y(z)), where G(z, w) satisfies the following conditions.

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P (I1 ): G(z, w) = m,n≥0 gm,n z m w n is analytic in a domain |z| < R and |w| < S, for some R, S > 0. (I2 ): The coefficients of G satisfy (44)

gm,n ≥ 0, g0,0 = 0, g0,1 6= 1, gm,n > 0 for some m and for some n ≥ 2.

(I3 ): There exist two numbers r, s, such that 0 < r < R and 0 < s < S, satisfying the system of equations, (45)

G(r, s) = s,

G w (r, s) = 1,

with r < R,

s < S,

which is called the characteristic system. A class Y with a generating y(z) satisfying y(z) = G(z, y(z)) is also said to belong to the smooth implicit-function schema. Postulating that G(z, w) is analytic and with non-negative coefficients is a minimal assumption in the context of analytic combinatorics. The problem is assumed to be normalized, so that y(0) = 0 and G(0, 0) = 0, the condition g0,1 6= 1 being imposed to avoid that the implicit equation be of the reducible form y = y + · · · (first line of (44)). The second condition of (44) means that in G(z, y), the dependency on y is nonlinear (otherwise, the analysis reduces to rational and meromorphic asymptotic methods of Chapter V). The major analytic condition is (I3 ), which postulates the existence of positive solutions r, s to the characteristic system within the domain of analyticity of G. The main result7 due to Meir and Moon [439] expresses universality of the squareroot singularity together with its usual consequences regarding asymptotic counting. Theorem VII.3 (Smooth implicit-function schema). Let y(z) belong to the smooth implicit-function schema defined by G(z, w), with (r, s) the positive solution of the characteristic system. Then, y(z) converges at z = r , where it has a square-root singularity, s p 2r G z (r, s) y(z) = s − γ 1 − z/r + O(1 − z/r ), , γ := z→r G ww (r, s) the expansion being valid in a 1–domain. If, in addition, y(z) is aperiodic8, then r is the unique dominant singularity of y and the coefficients satisfy γ [z n ]y(z) = √ r −n 1 + O(n −1 ) . n→∞ 2 π n 3 7 This theorem has an interesting history. An overly general version of it was first stated by Bender in 1974 (Theorem 5 of [36]). Canfield [102] pointed out ten years later that Bender’s conditions were not quite sufficient to grant square-root singularity. A corrected statement was given by Meir and Moon in [439] with a further (minor) erratum in [438]. We follow here the form given in Theorem 10.13 of Odlyzko’s survey [461] with the correction of another minor misprint (regarding g0,1 which should read g0,1 6= 1). A statement concerning a restricted class of functions (either polynomial or entire) already appears in Hille’s book [334, vol. I, p. 274]. 8In the usual sense of Definition IV.5, p. 266. Equivalently, there exist three indices i < j < k such that yi y j yk 6= 0 and gcd( j − i, k − i) = 1.

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469

Observe that the statement implies the existence of exactly one root of the characteristic system within the part of the positive quadrant where G is analytic, since, obviously, yn cannot admit two asymptotic expressions with different parameters. A complete expansion exists in powers of (1 − z/r )1/2 (for y(z)) and in powers of 1/n (for yn ), while periodic cases can be treated by a simple extension of the technical apparatus to be developed. The proof of this theorem first necessitates two lemmas of independent interest: (i) Lemma VII.2 is logically equivalent to an analytic version of the classical Implicit Function Theorem found in Appendix B.5: Implicit Function Theorem, p. 753. (ii) Lemma VII.3 supplements this by describing what happens at a point where the implicit function theorem “fails”. These two statements extend the analytic and singular inversion lemmas of Subsection IV. 7.1, p. 275. Lemma VII.2 (Analytic Implicit Functions). Let F(z, w) be z bivariate function analytic at (z, w) = (z 0 , w0 ). Assume that F(z 0 , w0 ) = 0 and Fw (z 0 , w0 ) 6= 0. Then, there exists a unique function y(z) analytic in a neighbourhood of z 0 such that y(z 0 ) = w0 and F(z, y(z)) = 0.

Proof. This is a restatement of the Analytic Implicit Function Theorem of Appendix B.5: Implicit Function Theorem, p. 753, upon effecting a translation z 7→ z + z 0 , w 7→ w + w0 . Lemma VII.3 (Singular Implicit Functions). Let F(z, w) be a bivariate function analytic at (z, w) = (z 0 , w0 ). Assume the conditions: F(z 0 , w0 ) = 0, Fz (z 0 , w0 ) 6= 0, Fw (z 0 , w0 ) = 0, and Fww (z 0 , w0 ) 6= 0. Choose an arbitrary ray of angle θ emanating from z 0 . Then there exists a neighbourhood of z 0 such that at every point z of with z 6= z 0 and z not on the ray, the equation F(z, y) = 0 admits two analytic solutions y1 (z) and y2 (z) that satisfy, as z → z 0 : s p 2z 0 Fz (z 0 , w0 ) y1 (z) = y0 − γ 1 − z/z 0 + O (1 − z/z 0 )) , γ := , Fww (z 0 , w0 ) √ √ to − . and similarly for y2 whose expansion is obtained by changing Proof. Locally, near (r, s), the function F(z, w) behaves like

1 F + (w − s)Fw + (z − r )Fz + (w − s)2 Fww , 2 (plus smaller order terms), where F and its derivatives are evaluated at the point (r, s). Since F = Fw = 0, cancelling (46) suggests for the solutions of F(z, w) = 0 near z = r the form √ w − s = ±γ r − z + O(z − r ), (46)

which is consistent with the statement. This informal argument can be justified by the following steps (details omitted): (a) establish the existence of a formal solution in powers of ±(1 − z/r )1/2 ; (b) prove, by the method of majorant series, that the formal solutions also converge locally and provide a solution to the equation. Alternatively, by the Weierstrass Preparation Theorem (Appendix B.5: Implicit Function Theorem, p. 753) the two solutions y1 (z), y2 (z) that assume the value s

470

VII. APPLICATIONS OF SINGULARITY ANALYSIS

(w)

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0 0

0.2

0.4

0.6

0.8

1.0

(z)

Figure VII.9. The connection problem for the equation w = 41 z + w2 (with explicit √ forms w = (1 ± 1 − z)/2): the combinatorial solution y(z) near z = 0 and the two analytic solutions y1 (z), y2 (z) near z = 1.

at z = r are solutions of a quadratic equation

(Y − s)2 + b(z)(Y − s) + c(z) = 0,

where b and c are analytic at z = r , with b(r ) = c(r ) = 0. The solutions are then obtained by the usual formula for solving a quadratic equation, p 1 −b(z) ± b(z)2 − 4c(z) , Y −s = 2 which provides for y1 (z) an expression as the square-root of an analytic function and yields the statement. It is now possible to return to the proof of our main statement. Proof. [Theorem VII.3] Given the two lemmas, the general idea of the proof of Theorem VII.3 can be easily grasped. Set F(z, w) = w − G(z, w). There exists a unique analytic function y(z) satisfying y = G(z, y) near z = 0, by the analytic lemma. On the other hand, by the singular lemma, near the point (z, w) = (r, s), there exist two solutions y1 , y2 , both of which have a square root singularity. Given the positive character of the coefficients of G, it is not hard to see that, of y1 , y2 , the function y1 (z) is increasing as z approaches z 0 from the left (assuming the principal determination of the square root in the definition of γ ). A simple picture of the situation regarding the solutions to the equation y = G(z, y) is exemplified by Figure VII.9. The problem is then to show that a smooth analytic curve (the thin-line curve in Figure VII.9) does connect the positive-coefficient solution at 0 to the increasingbranch solution at r . Precisely, one needs to check that y1 (z) (defined near r ) is the analytic continuation of y(z) (defined near 0) as z increases along the positive real axis. This is indeed a delicate connection problem whose technical proof is discussed

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471

in Note VII.16. Once this fact is granted and it has been verified that r is the unique dominant singularity of y(z) (Note VII.17), the statement of Theorem VII.3 follows directly by singularity analysis.

VII.16. The connection problem for implicit functions. A proof that y(z) and y1 (z) are well connected is given by Meir and Moon in the study [439], from which our description is adapted. Let ρ be the radius of convergence of y(z) at 0 and τ = y(ρ). The point ρ is a singularity of y(z) by Pringsheim’s Theorem. The goal is to establish that ρ = r and τ = s. Regarding the curve C = (z, y(z)) 0 ≤ z ≤ ρ , this means that three cases are to be excluded: (a) C stays entirely in the interior of the rectangle R := (z, y) 0 ≤ z ≤ r, 0 ≤ y ≤ s .

(b) C intersects the upper side of the rectangle R at some point of abscissa r0 < r where y(r0 ) = s. (c) C intersects the right-most side of the rectangle R at the point (r, y(r )) with y(r ) < s. Graphically, the three cases are depicted in Figure VII.10.

(b) (a)

(c)

Figure VII.10. The three cases (a), (b), and (c), to be excluded (solid lines).

In the discussion, we make use of the fact that G(z, w), which has non-negative coefficients is an increasing function in each of its argument. Also, the form (47)

y′ =

G z (z, y) , 1 − G w (z, y)

shows differentiability (hence analyticity) of the solution y as soon as G w (z, y) 6= 1. Case (a) is excluded. Assume that 0 < ρ < r and 0 < τ < s. Then, we have G w (r, s) = 1, and by monotonicity properties of G w , the inequality G w (ρ, τ ) < 1 holds. But then y(z) must be analytic at z = ρ, which contradicts the fact that ρ is a singularity. Case (b) is excluded. Assume that 0 < r0 < r and y(r0 ) = s. Then there are two distinct points on the implicit curve y = G(z, y) at the same altitude, namely (r0 , s) and (r, s), implying the equalities y(r0 ) = G(r0 , y(r0 )) = s = G(r, s), which contradicts the monotonicity properties of G. Case (c) is excluded. Assume that y(r ) < s. Let a < r be a point chosen close enough to r . Then above a, there are three branches of the curve y = G(z, y), namely y(a), y1 (a), y2 (a), where the existence of y1 , y2 results from Lemma VII.3. This means that the function y 7→ G(a, y) has a graph that intersects the main diagonal at three points, a contradiction with the fact that G(a, y) is a convex function of y.

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VII. APPLICATIONS OF SINGULARITY ANALYSIS

VII.17. Unicity of the dominant singularity. From the previous note, we know that y(r ) = s, with r the radius of convergence of y. The aperiodicity of y implies that |y(ζ )| < y(r ) for all |ζ | such that |ζ | = r and |ζ | 6= r (see the Daffodil Lemma IV.1, p. 266). One then has for any such ζ the property: |G w (ζ, y(ζ ))| < G(r, s) = 1, by monotonicity of G w . But then by (47) above, this implies that y(ζ ) is analytic at ζ . The solutions to the characteristic system (45) can be regarded as the intersection points of two curves, namely, G(r, s) − s = 0,

G w (r, s) = 1.

Here are plots in the case of two functions G: the first one has non-negative coefficients whereas the second one (corresponding to a counterexample of Canfield [102]) involves negative coefficients. Positivity of coefficients implies convexity properties that avoid pathological situations. G(z, y) =

1 − 1 − y − y3 1−z−y (positive)

z 24 − 9y + y 2 (not positive)

G(z, y) =

0.4

4

(s) 0.2

(s) 2

0

0.1

0

0.2 (r)

10 (r)

20

VII. 4.2. Combinatorial applications. Many combinatorial classes, which admit a recursive specification of the form Y = G(Z, Y), as in (43), p. 467, can be subjected to Theorem VII.3. The resulting structures are, to varying degrees, avatars of tree structures. In what follows, we describe a few instances in which the squareroot universality holds. (i) Hierarchies are trees enumerated by the number of their leaves (Examples VII.12 and VII.13). (ii) Trees with variable node sizes generalize simple families of trees; they occur in particular as mathematical models of secondary structures in biology (Example VII.14). (iii) Lattice paths with variable edge lengths are attached to some of the most classical objects of combinatorial theory (Note VII.19). Example VII.12. Labelled hierarchies. The class L of labelled hierarchies, as defined in Note II.19, p. 128, satisfies L = Z + S ET≥2 (L)

H⇒

L = z + eL − 1 − L .

VII. 4. TREE-LIKE STRUCTURES AND IMPLICIT FUNCTIONS

473

Indo-European

Celtic

Irish

German

Germanic

WG

English

Italic

NG

Greek

French

Danish

Armenian

BaSl

Italian

Slavic

Baltic

Polish

Russian

InIr

Persian

Urdu

Hindi

Lithuanian

Figure VII.11. A hierarchy placed on some of the modern Indo-European languages.

These occur in statistical classification theory: given a collection of n distinguished items, L n is the number of ways of superimposing a non-trivial classification (cf Figure VII.11). Such abstract classifications usually have no planar structure, hence our modelling by a labelled set construction. In the notations of Definition VII.4, p. 467, the basic function is G(z, w) = z +ew −1−w, which is analytic in |z| < ∞, |w| < ∞. The characteristic system is r + es − 1 − s = s,

es − 1 = 1,

which has a unique positive solution, s = log 2, r = 2 log 2 − 1, obtained by solving the second equation for s, then propagating the solution to get r . Thus, hierarchies belong to the smooth implicit-function schema, and, by Theorem VII.3, the EGF L(z) has a square-root singularity. One then finds mechanically 1 1 Ln ∼ √ (2 log 2 − 1)−n+1/2 . n! 2 π n3 (The unlabelled counterpart is the object of Note VII.23, p. 479.) . . . . . . . . . . . . . . . . . . . . . . . .

VII.18. The degree profile of hierarchies. Combining BGF techniques and singularity analysis, it is found that a random hierarchy of some large size n has on average about 0.57n nodes of degree 2, 0.18n nodes of degree 3, 0.04n nodes of degree 4, and less than 0.01n nodes of degree 5 or higher. Example VII.13. Trees enumerated by leaves. For a (non-empty) set ⊂ Z≥0 that does not contain 0,1, it makes sense to consider the class of labelled trees, C = Z + S EQ (C)

or

C = Z + S ET (C).

(A similar discussion can be conducted for unlabelled plane trees, with OGFs replacing EGFs.) These are rooted trees (plane or non-plane, respectively), with size determined by the number of leaves and with degrees constrained to lie in . The EGF is then of the form C(z) = z + η(C(z)).

This variety of trees includes the labelled hierarchies, which correspond to η(w) = ew − 1 − w. Assume for simplicity η to be entire (possibly a polynomial). The basic function is G(z, w) = z + η(w), and the characteristic system is s = r + η(s), η′ (s) = 1. Since η′ (0) = 0 and η′ (+∞) = +∞, this system always has a solution: s = η[−1] (1),

r = s − η(s).

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VII. APPLICATIONS OF SINGULARITY ANALYSIS

A fragment of RNA is, in first approximation, a treelike structure with edges corresponding to base pairs and “loops” corresponding to leaves. There are constraints on the sizes of leaves (taken here between 4 and 7) and length of edges (here between 1 and 4 base pairs). We model such an RNA fragment as a planted tree P attached to a binary tree (Y) with equations: P = AY, Y = AY 2 + B, A = z2 + z4 + z6 + z8, B = z4 + z5 + z6 + z7. Figure VII.12. A simplified combinatorial model of RNA structures analogous to those considered by Waterman et al.

Thus Theorem VII.3 applies, giving (48)

[z n ]C(z) ∼

γ r −n , √ 2 π n3

γ =

r

1 ′′ r η (s), 2

and a complete expansion can be obtained. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Example VII.14. Trees with variable edge lengths and node sizes. Consider unlabelled plane b of ordered pairs (ω, σ ), trees in which nodes can be of different sizes: what is given is a set where a value (ω, σ ) means that a node of degree ω and size σ is allowed. Simple varieties in their basic form correspond to σ ≡ 1; trees enumerated by leaves (including hierarchies) correspond to σ ∈ {0, 1} with σ = 1 iff ω = 0. Figure VII.12 suggests the way such trees can model the self-bonding of single-stranded nucleic acids like RNA, according to Waterman et al. [336, 453, 534, 558]. Clearly an extremely large number of variations are possible. b is The fundamental equation in the case of a finite X Y (z) = P(z, Y (z)), P(z, w) := z σ wω , b (ω,σ )∈

with P a polynomial. In the aperiodic case, there is invariably a formula of the form Yn ∼ κ · An n 3/2 , corresponding to the universal square-root singularity. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

VII.19. Schr¨oder numbers. Consider the class Y of unary–binary trees where unary nodes have size 2, while leaves and binary nodes have the usual size 1. The GF satisfies Y = z + z 2 Y + zY 2 , so that p 1 − z − 1 − 6z + z 2 2 Y (z) = z D(z ), D(z) = . 2z We have D(z) = 1 + 2 z + 6 z 2 + 22 z 3 + 90 z 4 + 394 z 5 + · · · , which is EIS A006318 (“Large Schr¨oder numbers”). By the bijective correspondence between trees and lattice paths, Y2n+1 is in correspondence with excursions of length n made of steps (1, 1), (2, 0), (1, −1). Upon tilting by 45◦ , this is equivalent to paths connecting the lower left corner to the upper right corner of an (n × n) square that are made of horizontal, vertical, and diagonal steps, and never go under

´ VII. 5. UNLABELLED NON-PLANE TREES AND POLYA OPERATORS

475

the main diagonal. The series S = 2z (1 + D) enumerates Schr¨oder’s generalized parenthesis systems (Chapter I, p. 69): S := z + S 2 /(1 − S), and the asymptotic formula √ −n+1/2 1 1 3−2 2 Y2n−1 = Sn = Dn−1 ∼ √ 2 4 π n3 follows straightforwardly.

VII. 5. Unlabelled non-plane trees and P´olya operators Essentially all the results obtained earlier for simple varieties of trees can be extended to the case of non-plane unlabelled trees. P´olya operators are central, and their treatment is typical of the asymptotic theory of unlabelled objects obeying symmetries (i.e., involving the unlabelled MS ET, PS ET, C YC constructions), as we have seen repeatedly in this book. Binary and general trees. We start the discussion by considering the enumeration of two classes of non-plane trees following P´olya [488, 491] and Otter [466], whose articles are important historic sources for the asymptotic theory of non-plane tree enumeration—a brief account also appears in [319]. (These authors used the more traditional method of Darboux instead of singularity analysis, but this distinction is immaterial here, as calculations develop under completely parallel lines under both theories.) The two classes under consideration are those of general and binary non-plane unlabelled trees. In both cases, there is a fairly direct reduction to the enumeration of Cayley trees and of binary trees, which renders explicit several steps of the calculation. The trick is, as usual, to treat values of f (z 2 ), f (z 3 ), . . . , arising from P´olya operators, as “known” analytic quantities. Proposition VII.5 (Special unlabelled non-plane trees). Consider the two classes of unlabelled non-plane trees H = Z × MS ET(H),

W = Z × MS ET{0,2} (W),

respectively, of the general and binary type. Then, with constants γ H , A H and γW , A W given by Notes VII.21 and VII.22, one has γW γH AnH , W2n−1 ∼ √ AnW . (49) Hn ∼ √ 3 2 πn 2 π n3 Proof. (i) General case. The OGF of non-plane unlabelled trees is the analytic solution to the functional equation ! H (z 2 ) H (z) + + ··· . (50) H (z) = z exp 1 2 Let T be the solution to (51)

T (z) = ze T (z) ,

that is to say, the Cayley function. The function H (z) has a radius of convergence ρ strictly less than 1 as its coefficients dominate those of T (z), the radius of convergence . of the latter being exactly e−1 = 0.367. The radius ρ cannot be 0 since the number of trees is bounded from above by the number of plane trees whose OGF has radius 1/4. Thus, one has 1/4 ≤ ρ ≤ e−1 .

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Rewriting the defining equation of H (z) as H (z) = ζ e H (z)

with

ζ := z exp

H (z 3 ) H (z 2 ) + + ··· 2 3

!

,

we observe that ζ = ζ (z) is analytic for |z| < ρ 1/2 ; that is, ζ is analytic in a disc that properly contains the disc of convergence of H (z). We may thus rewrite H (z) as H (z) = T (ζ (z)).

Since ζ (z) is analytic at z = ρ, a singular expansion of H (z) near z = ρ results from composing the singular expansion of T at e−1 with the analytic expansion of ζ at ρ. In this way, we get: p z z 1/2 , γ = 2eρζ ′ (ρ). +O 1− (52) H (z) = 1 − γ 1 − ρ ρ Thus, γ ρ −n . [z n ]H (z) ∼ √ 2 π n3 (ii) Binary case. Consider the functional equation 1 1 (53) f (z) = z + f (z)2 + f (z 2 ). 2 2 This enumerates non-plane binary trees with size defined as the number of external nodes, so that W (z) = 1z f (z 2 ). Thus, it suffices to analyse [z n ] f (z), which dispenses us from dealing with periodicity phenomena arising from the parity of n. The OGF f (z) has a radius of convergence ρ that is at least 1/4 (since there are fewer non-plane trees than plane ones). It is also at most 1/2, which is seen from a comparison of f with the solution to the equation g = z + 21 g 2 . We may then proceed as before: treat the term 12 f (z 2 ) as a function analytic in |z| < ρ 1/2 , as though it were known, then solve. To this effect, set 1 ζ (z) := z + f (z 2 ), 2 which exists in |z| < ρ 1/2 . Then, the equation (53) becomes a plain quadratic equation, f = ζ + 21 f 2 , with solution p f (z) = 1 − 1 − 2ζ (z).

The singularity ρ is the smallest positive solution of ζ (ρ) = 1/2. The singular √ expansion of f is obtained by combining the analytic expansion of ζ at ρ with 1 − 2ζ . The usual square-root singularity results: p p γ := 2ρζ ′ (ρ). f (z) ∼ 1 − γ 1 − z/ρ,

This induces the ρ −n n −3/2 form for the coefficients [z n ] f (z) ≡ [z 2n−1 ]W (z). The argument used in the proof of the proposition may seem partly non-constructive. However, numerically, the values of ρ and γ can be determined to great accuracy. See the notes below as well as Finch’s section on “Otter’s tree enumeration constants” [211, Sec. 5.6].

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477

VII.20. Complete asymptotic expansions for Hn , W2n−1√ . These can be determined since the OGFs admit complete asymptotic expansions in powers of

1 − z/ρ.

VII.21. Numerical evaluation of constants I. Here is an unoptimized procedure controlled by a parameter m ≥ 0 for evaluating the constants γ H , ρ H of (49) relative to general unlabelled non-plane trees. Procedure Get value of ρ(m : integer); 1. Set up a procedure to compute and memorize the Hn on demand; (this can be based on recurrence relations implied by H ′ (z); see [456]) Pm [m] 2. Define f (z) := j=1 Hn z n ; P m 1 [m] k 3. Define ζ [m] (z) := z exp (z ) ; k=2 k f

4. Solve numerically ζ [m] (x) = e−1 for x ∈ (0, 1) to max(m, 10) digits of accuracy; 5. Return x as an approximation to ρ. For instance, a conservative estimate of the accuracy attained for m = 0, 10, . . . , 50 (in a few billion machine instructions) is: m=0 3 · 10−2

m = 10 10−6

m = 20 10−11

m = 30 10−16

m = 40 10−21

m = 50 10−26

Accuracy appears to be a little better than 10−m/2 . This yields to 25D: . . ρ = 0.3383218568992076951961126, A H ≡ ρ −1 = 2.955765285651994974714818, . γ H = 1.559490020374640885542206.

The formula of Proposition VII.5 estimates H100 with a relative error of 10−3 .

VII.22. Numerical evaluation of constants II. The procedure of the previous note adapts easily to binary trees, giving: . . ρ = 0.4026975036714412909690453, A W ≡ ρ −1 = 2.483253536172636858562289, . γW = 1.130033716398972007144137.

The formula of Proposition VII.5 estimates [z 100 ] f (z) with a relative error of 7 · 10−3 .

The results relative to general and binary trees are thus obtained by a modification of the method used for simple varieties of trees, upon treating the P´olya operator part as an analytic variant of the corresponding equations of simple varieties of trees. Alkanes, alcohols, and degree restrictions. The previous two examples suggest that a general theory is possible for varieties of unlabelled non-plane trees, T = Z MS ET (T ), determined by some ⊂ Z≥0 . First, we examine the case of special regular trees defined by = {0, 3}, which, when viewed as alkanes and alcohols, are of relevance to combinatorial chemistry (Example VII.15). Indeed, the problem of enumerating isomers of such chemical compounds has been at the origin of P´olya’s foundational works [488, 491]. Then, we extend the method to the general situation of trees with degrees constrained to an arbitrary finite set (Proposition VII.5). Example VII.15. Non-plane trees and alkanes. In chemistry, carbon atoms (C) are known to have valency 4 while hydrogen (H ) has valency 1. Alkanes, also known as paraffins (Figure VII.13), are acyclic molecules formed of carbon and hydrogen atoms according to this rule and without multiple bonds; they are thus of the type Cn H2n+2 . In combinatorial terms, we are talking of unrooted trees with (total) node degrees in {1, 4}. The rooted version of these trees are determined by the fact that a root is chosen and (out)degrees of nodes lie in the set = {0, 3}; such rooted ternary trees then correspond to alcohols (with the OH group marking one of the carbon atoms).

478

VII. APPLICATIONS OF SINGULARITY ANALYSIS

H | | H--C--H | | H Methane

H H | | | | H--C--C--H | | | | H H

H H H | | | | | | H--C--C--C--H | | | | | | H H H

H OH H | | | | | | H--C--C--C--H | | | | | | H H H

Ethane

Propane

Propanol

Figure VII.13. A few examples of alkanes (C H4 , C2 H6 , C3 H8 ) and an alcohol. Alcohols (A) are the simplest to enumerate, since they correspond to rooted trees. The OGF starts as (EIS A000598) A(z) = 1 + z + z 2 + z 3 + 2 z 4 + 4 z 5 + 8 z 6 + 17 z 7 + 39 z 8 + 89 z 9 + · · · , with size being taken here as the number of internal nodes. The specification is A = {ǫ} + Z MS ET3 (A). + (Equivalently A := A \ {ǫ} satisfies A+ = Z MS ET0,1,2,3 (A+ ).) This implies that A(z) satisfies the functional equation:

1 1 1 A(z 3 ) + A(z)A(z 2 ) + A(z)3 . 3 2 6 In order to apply Theorem VII.3, introduce the function 1 1 1 A(z 3 ) + A(z 2 )w + w3 , (54) G(z, w) = 1 + z 3 2 6 A(z) = 1 + z

which exists in |z| < |ρ|1/2 and |w| < ∞, with ρ the (yet unknown) radius of convergence of A. Like before, the P´olya terms A(z 2 ), A(z 3 ) are treated as known functions. By methods similar to those earlier in the analysis of binary and general trees, we find that the characteristic system admits a solution, . . r = 0.3551817423143773928, s = 2.1174207009536310225,

so that ρ = r and y(ρ) = s. Thus the growth of the number of alcohols is of the form . κρ −n n −3/2 , with ρ −1 = 2.81546. Let B(z) be the OGF of alkanes (EIS A000602), which are unrooted trees: B(z) = 1 + z + z 2 + z 3 + 2 z 4 + 3 z 5 + 5 z 6 + 9 z 7 + 18 z 8 35 z 9 + 75 z 10 + · · · .

For instance, B6 = 5 because there are five isomers of hexane, C6 H14 , for which chemists had to develop a nomenclature system, interestingly enough based on a diameter of the tree: Hexane

3-Methylpentane

2,3-Dimethylbutane

2,2-Dimethylbutane

2-Methylpentane

´ VII. 5. UNLABELLED NON-PLANE TREES AND POLYA OPERATORS

479

The number of structurally different alkanes can then be found by an adaptation of the dissimilarity formula (Equation (57) below and Note VII.26). This problem has served as a powerful motivation for the enumeration of graphical trees and its fascinating history goes back to Cayley. (See Rains and Sloane’s article [502] and [491]). The asymptotic formula of (unrooted) alkanes is of the global form ρ −n n −5/2 , which represents roughly a proportion 1/n of the number of (rooted) alcohols: see below. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

The pattern of analysis should by now be clear, and we state: Theorem VII.4 (Non-plane unlabelled trees). Let ∋ 0 be a finite subset of Z≥0 and consider the variety V of (rooted) unlabelled non-plane trees with outdegrees of nodes in . Assume aperiodicity (gcd() = 1) and the condition that contains at least one element larger than 1. Then the number of trees of size n in V satisfies an asymptotic formula: Vn ∼ C · An n −3/2 .

Proof. The argument given for alcohols is transposed verbatim. Only the existence of a root of the characteristic system needs to be established. The radius of convergence of V (z) is a priori ≤ 1. The fact that ρ is strictly less than 1 is established by means of an exponential lower bound; namely, Vn > B n , for some B > 1 and infinitely many values of n. To obtain this “exponential diversity” of the set of trees, first choose an n 0 such that Vn 0 > 1, then build a perfect d–ary tree (for some d ∈ , d 6= 0, 1) of height h, and finally graft freely subtrees of size n 0 at n/(4n 0 ) of the leaves of the perfect tree. Choosing d such that d h > n/(4n 0 ) yields the lower bound. That the radius of convergence is non-zero results from the upper bound provided by corresponding plane trees whose growth is at most exponential. Thus, one has 0 < ρ < 1. By the translation of multisets of bounded cardinality, the function G is polynomial in finitely many of the quantities {V (z), V (z 2 ), . . .}. Thus the function G(z, w) constructed as in the case of alcohols, in Equation (54), converges in |z| < ρ 1/2 , |w| < ∞. As z → ρ −1 , we must have τ := V (ρ) finite, since otherwise, there would be a contradiction in orders of growth in the nonlinear equation V (z) = · · ·+· · · V (z)d · · · as z → ρ. Thus (ρ, τ ) satisfies τ = G(ρ, τ ). For the derivative, one must have G w (ρ, τ ) = 1 since: (i) a smaller value would mean that V is analytic at ρ (by the Implicit Function Theorem); (ii) a larger value would mean that a singularity has been encountered earlier (by the usual argument on failure of the Implicit Function Theorem). Thus, Theorem VII.3 on positive implicit functions is applicable. A large number of variations are clearly possible as evidenced by the suggestive title of an article [320] published by Harary, Robinson, and Schwenk in 1975: “Twenty-step algorithm for determining the asymptotic number of trees of various species”.

VII.23. Unlabelled hierarchies. The class H of unlabelled hierarchies is specified by H =

Z + MS ET≥2 (H); see Note I.45, p. 72. One has . en ∼ √γ ρ −n , ρ = 0.29224. H 3 2 πn (Compare with the labelled case of Example VII.12, p. 472.) What is the asymptotic proportion of internal nodes of degree r , for a fixed r > 0?

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VII. APPLICATIONS OF SINGULARITY ANALYSIS

VII.24. Trees with prime degrees and the BBY theory. Bell, Burris, and Yeats [33] develop a general theory meant to account for the fact that, in their words, “almost any family of trees defined by a recursive equation that is nonlinear [. . . ] lead[s] to an asymptotic law of the P´olya form t (n) ∼ Cρ −n n −3/2 ”. Their most general result [33, Th. 75] implies for instance that the number of unlabelled non-plane trees whose node degrees are restricted to be prime numbers admits such a P´olya form (see also Note VII.6, p. 455).

Unlabelled functional graphs (mapping patterns). Unlabelled functional graphs (named “functions” in [319, pp. 69–70]) are denoted here by F; they correspond to unlabelled digraphs with loops allowed, in which each vertex has outdegree equal to 1. They can be specified as multisets of components (L) that are cycles of non-plane unlabelled trees (H), F = MS ET(L);

L = C YC(H);

H = Z × MS ET(H),

a specification that entirely parallels that of mappings in Equation (35), p. 462. Indeed, an unlabelled functional graph can be used to represent the “shape” of a mapping, as obtained when labels are discarded. That is, functional graphs result when mappings are identified up to a possible permutation of their underlying domain. This explains the alternative term of “mapping pattern” [436] sometimes employed for such graphs. The counting sequence starts as 1, 1, 3, 7, 19, 47, 130, 343, 951 (EIS A001372). The OGF H (z) has a square-root singularity by virtue of (52) above, with additionally H (ρ) = 1. The translation of the unlabelled cycle construction, X ϕ( j) 1 log , L(z) = j 1 − H (z j ) j≥1

√ implies that L(z) is logarithmic, and F(z) has a singularity of type 1/ Z where Z := 1 − z/ρ. Thus, unlabelled functional graphs constitute an exp–log structure in the sense of Section VII. 2, p. 445, with κ = 1/2. The number of unlabelled functional graphs thus grows like Cρ −n n −1/2 and the mean number of components in a random functional graph is ∼ 21 log n, as for labelled mappings; see [436] for more on this topic.

VII.25. An alternative form of F(z). Arithmetical simplifications associated with the Euler totient function (A PPENDIX A, p. 721) yield: F(z) =

∞ Y

k=1

1 − H (z k )

−1

.

A similar form applies generally to multisets of unlabelled cycles (Note I.57, p. 85).

Unrooted trees. All the trees considered so far have been rooted and this version is the one most useful in applications. An unrooted tree9 is by definition a connected acyclic (undirected) graph. In that case, the tree is clearly non-plane and no special root node is distinguished. The counting of the class U of unrooted labelled trees is easy: there are plainly Un = n n−2 of these, since each node is distinguished by its label, which entails that 9Unrooted trees are also called sometimes free trees.

´ VII. 5. UNLABELLED NON-PLANE TREES AND POLYA OPERATORS

481

nUn = Tn , with Tn = n n−1 by Cayley’s formula. Also, the EGF U (z) satisfies Z z dy 1 (55) U (z) = T (y) = T (z) − T (z)2 , y 2 0 as already seen when we discussed labelled graphs in Subsection II. 5.3, p. 132. For unrooted unlabelled trees, symmetries are present and a tree can be rooted in a number of ways that depends on its shape. For instance, a star graph leads to a number of different rooted trees that equals 2 (choose either the centre or one of the peripheral nodes), while a line graph gives rise to ⌈n/2⌉ structurally different rooted trees. With H the class of rooted unlabelled trees and I the class of unrooted trees, we have at this stage only a general inequality of the form In ≤ Hn ≤ n In .

A table of values of the ratio Hn /In suggests that the answer is close to the upper bound: (56)

n Hn /In

10 6.78

20 15.58

30 23.89

40 32.15

50 40.39

60 48.62

The solution is provided by a famous exact formula due to Otter (Note VII.26): 1 (57) I (z) = H (z) − H (z)2 − H (z 2 ) , 2 which gives in particular (EIS A000055) I (z) = z + z 2 + z 3 + 2 z 4 + 3 z 5 + 6 z 6 + 11 z 7 + 23 z 8 + · · · . Given (57), it is child’s play to determine the singular expansion of I knowing that of H . The radius of convergence of I is the same as that of H , since the term H (z 2 ) only introduces exponentially small coefficients. Thus, it suffices to analyse H − 21 H 2 : 1 z 1 H (z) − H (z)2 ∼ − δ2 Z + δ3 Z 3/2 + O Z 2 , . Z = 1− 2 2 ρ What is noticeable is the cancellation in coefficients for the term Z 1/2 (since 1 − x − 1 1 2 2 3/2 is the actual singularity type of I . Clearly, 2 (1 − x) = 2 + O(x )), so that Z the constant δ3 is computable from the first four terms in the singular expansion of H at ρ. Then singularity analysis yields: The number of unrooted trees of size n satisfies the formula 3δ3 ρ −n , In ∼ (0.5349496061 . . .) (2.9955765856 . . .)n n −5/2 . (58) In ∼ √ 5 4 πn The numerical values are from [211] and the result is Otter’s original [466]: an unrooted tree of size n gives rise to about different 0.8n rooted trees on average. (The formula (58) corresponds to an error slightly under 10−2 for n = 100.)

VII.26. Dissimilarity theorem for trees. Here is how combinatorics justifies (57), following [50, §4.1]. Let I • (and I •–• ) be the class of unrooted trees with one vertex (respectively, one edge) distinguished. We have I • ∼ = S ET2 (H). The combinatorial = H (rooted trees) and I •–• ∼ isomorphism claimed is (59) I • + I •–• ∼ = I + (I × I) . Proof. A diameter of an unrooted tree is a simple path of maximal length. If the length of any diameter is even, call “centre” its mid-point; otherwise, call “bicentre” its mid-edge. (For

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VII. APPLICATIONS OF SINGULARITY ANALYSIS

each tree, there is either one centre or one bicentre.) The left-hand side of (59) corresponds to trees that are pointed either at a vertex (I • ) or an edge (I •–• ). The term I on the right-hand side corresponds to cases where the pointing happens to coincide with the canonical centre or bicentre. If there is not coincidence, then, an ordered pair of trees results from a suitable surgery of the pointed tree. [Hint: cut in some canonical way near the pointed vertex or edge.]

VII. 6. Irreducible context-free structures In this section, we discuss an important variety of context-free classes, one that gives rise to the universal law of square-root singularities, itself attached to counting sequences that are of the general asymptotic form An n −3/2 . First, we enunciate an abstract structural result (Theorem VII.5, p. 483) that connects “irreducibility” of context-free systems to the square-root singularity phenomenon. Before engaging into a proof, we first illustrate its scope by describing applications to non-crossing configurations in the plane (these are richer than triangulations introduced in Chapter I) and to random boolean expressions. Finally, we prove an important complex analytic result, the Drmota–Lalley–Woods Theorem (Theorem VII.6, p. 489), which provides the underlying analytic engine needed to establish Theorem VII.5 and justify the asymptotic properties of irreducible context-free specifications. General algebraic functions are to be treated next, in Section VII. 7, p. 493. VII. 6.1. Context-free specifications and the irreducibility schema. We start from the notion of a context-free class already introduced in Subsection I. 5.4, p. 79, which we recall: a class is context-free if it is determined as the first component of a system of combinatorial equations Y1 = F1 (Z, Y1 , . . . , Yr ) .. .. .. (60) . . . Yr = Fr (Z, Y1 , . . . , Yr ), where each F j is a construction that only involves the combinatorial constructions of disjoint union and cartesian product. (This repeats Equation (83) of Chapter I, p. 79.) As seen in Subsection I. 5.4, binary and general trees, triangulations, as well as Dyck and Łukasiewicz languages are typical instances of context-free classes. As a consequence of the symbolic rules of Chapter I, the OGF of a context-free class C is the first component (C(z) ≡ y1 (z)) of the solution of a polynomial system of equations of the form y1 (z) = 81 (z, y1 (z), . . . , yr (z)) .. .. .. (61) . . . yr (z) = 8r (z, y1 (z), . . . , yr (z)),

where the 8 j are polynomials. By elimination (Cf Appendix B.1: Algebraic elimination, p. 739), it is always possible to find a bivariate polynomial P(z, y) such that (62)

P(z, C(z)) = 0,

and C(z) is an algebraic function. (Algebraic functions are discussed in all generality in the next section.)

VII. 6. IRREDUCIBLE CONTEXT-FREE STRUCTURES

483

The case of linear systems has been dealt with in Chapter V, when examining the transfer matrix method. Accordingly, we only need to consider here nonlinear systems (of equations or specifications) defined by the condition that at least one 8 j in (61) is a polynomial of degree 2 or more in the y j , corresponding to the fact that at least one of the constructions F j in (60) involves at least a product Yk Yℓ . Definition VII.5. A context-free specification (60) is said to belong to the irreducible context-free schema if it is nonlinear and its dependency graph (p. 33) is strongly connected. It is said to be aperiodic if all the y j (z) are aperiodic10. Theorem VII.5 (Irreducible context-free schema). A class C that belongs to the irreducible context-free schema has a generating function that has a square-root singularity at its radius of convergence ρ: r z z C(z) = τ − γ 1 − + O 1 − , ρ ρ for computable algebraic numbers ρ, τ, γ . If, in addition, C(z) is aperiodic, then the dominant singularity is unique and the counting sequence satisfies γ ρ −n . (63) Cn ∼ √ 2 π n3 This theorem is none other than a transcription, at the combinatorial level, of a remarkable analytic statement, Theorem VII.6, due to Drmota, Lalley, and Woods, which is proved below (p. 489), is slightly stronger, and is of independent interest. Computability issues. There are two complementary approaches to the calculation of the quantities that appear in (63), one based on the original system (61), the other based on the single equation (62) that results from elimination. We offer at this stage a brief pragmatic discussion of computational aspects, referring the reader to Subsection VII. 6.3, p. 488, and Section VII. 7, p. 493, for context and justifications. (a) System: Considering the proof of Theorem VII.6 below, one should solve, in positive real numbers, a polynomial system of m + 1 equations in the m + 1 unknowns ρ, τ1 , . . . , τm ; namely, τ1 = 81 (ρ, τ1 , . . . , τm ) .. .. .. . . . (64) τm = 8m (ρ, τ1 , . . . , τm ) 0 = J (ρ, τ1 , . . . , τm ),

which one can call the characteristic system. There J is the Jacobian determinant: ∂ (65) J (z, y1 , . . . , ym ) := det δi, j − 8i (z, y1 , . . . , ym ) , ∂yj

10An aperiodic function is such that the span of the coefficient sequence is equal to 1 (Definition IV.5, p. 266). For an irreducible system, it can be checked that all the y j are aperiodic if and only if at least one of the y j is aperiodic.

484

VII. APPLICATIONS OF SINGULARITY ANALYSIS

with δi, j ≡ [[i = j]] being the usual Kronecker symbol. The quantity ρ represents the common radius of convergence of all the y j (z) and τ j = y j (ρ). (In case several possibilities present themselves for ρ, as in Note VII.28, then one can use either a priori combinatorial bounds to filter out the spurious ones11 or make use of the reduction to a single equation as in point (b) below.) The constant γ ≡ γ1 in Theorem VII.5 is then a component of the solution to a linear system of equations (with coefficients in the field generated by ρ, τ j ) and is obtained by the method of undetermined coefficients, since each y j is of the form p (66) y j (z) ∼ τ j − γ j 1 − z/ρ, z → ρ.

(b) Equation: The general techniques are going to be described in Section, §VII. 7, p. 493. They give rise to the following algorithm: (i) determine the exceptional set, identify the proper branch of the algebraic curve and the dominant positive singularity; (ii) determine the coefficients in the singular (Puiseux) expansion, knowing a priori that the singularity is of the square-root type. In all events, symbolic algebra systems prove invaluable in performing the required algebraic eliminations and isolating the combinatorially relevant roots (see, in particular, Pivoteau et al. [485] for a general symbolic–numeric approach). Example VII.16 serves to illustrate some of these computations.

VII.27. Catalan and the Jacobian determinant. For the Catalan GF, defined by y = 1 + zy 2 ,

the characteristic system (64) instantiates to

τ − 1 − ρτ 2 = 0,

1 − 2ρτ = 0,

giving back as expected: ρ = 14 , τ = 2.

VII.28. Burris’ Caveat. As noted by Stanley Burris (private communication), even some very simple context-free specifications may be such that there exist several positive solutions to the characteristic system (64). Consider y1 = z(1 + y2 + y 2 ) 1 (B) : y2 = z(1 + y1 + y 2 ), 2 which is clearly associated to a redundant way of counting unary–binary trees (via a deterministic 2-colouring). The characteristic system is n o τ1 = ρ(1 + τ2 + τ12 ), τ2 = ρ(1 + τ1 + τ22 ), (1 − 2ρτ1 )(1 − 2ρτ2 ) − ρ 2 = 0 . The positive solutions are 1 ρ = , τ1 = τ2 = 1 3

∪

ρ=

1 √ (2 2 − 1), 7

τ1 = τ2 =

√ 2+1 .

Only the first solution is combinatorially significant. (A somewhat similar situation, though it relates to a non-irreducible context-free specification, arises with supertrees of Example VII.20, p. 503: see Figure VII.19, p. 504.)

11This is once more a connection problem, in the sense of p. 470.

VII. 6. IRREDUCIBLE CONTEXT-FREE STRUCTURES

485

VII. 6.2. Combinatorial applications. Lattice animals (Example I.18, p. 80), random walks on free groups [395], directed walks in the plane (see references [27, 392, 395] and p. 506 below), coloured trees [616], and boolean expression trees (reference [115] and Examples VII.17) are only some of the many combinatorial structures belonging to the irreducible context-free schema. Stanley presents in his book [554, Ch. 6] several examples of algebraic GFs, and an inspiring survey is provided by Bousquet-M´elou in [84]. We limit ourselves here to a brief discussion of non-crossing configurations and random boolean expressions. Example VII.16. Non-crossing configurations. Context-free descriptions can model naturally very diverse sorts of objects including particular topological-geometric configurations—we examine here non-crossing planar configurations. The problems considered have their origin in combinatorial musings of the Rev. T.P. Kirkman in 1857 and were revisited in 1974 by Domb and Barett [169] for the purpose of investigating certain perturbative expansions of statistical physics. Our presentation follows closely the synthesis offered by Flajolet and Noy in [245]. Consider, for each value of n, graphs built on vertices that are all the nth complex roots of unity, numbered 0, . . . , n − 1. A non-crossing graph is a graph such that no two of its edges cross. One can also define connected non-crossing graphs, non-crossing forests (acyclic graphs), and non-crossing trees (acyclic connected graphs); see Figure VII.14. Note that the various graphs considered can always be considered as rooted in some canonical way (e.g., at the vertex of smallest index) . Trees. A non-crossing tree is rooted at 0. To the root vertex is attached an ordered collection of vertices, each of which has an end-node ν that is the common root of two non-crossing trees, one on the left of the edge (0, ν) the other on the right of (0, ν). Let T denote the class of trees and U denote the class of trees whose root has been severed. With • ≡ Z denoting a generic node, we have T = • × U,

U = S EQ(U × • × U),

which corresponds graphically to the “butterfly decomposition”:

U=

T= U

U

U

U

U

The reduction to a pure context-free form is obtained by noticing that U = S EQ(V) is equivalent to U = 1 + UV: a specification and the associated polynomial system are then (67) {T = ZU, U = 1 + UV, V = ZUU }

H⇒

{T = zU, U = 1 + U V, V = zU 2 }.

This system relating U and V is irreducible (then, T is immediately obtained from U ), and aperiodicity is obvious from the first few values of the coefficients. The Jacobian (65) of the {U, V }-system (obtained by z → ρ, U → υ, V → β), is 1−β υ = 1 − β − 2ρυ 2 . 2ρυ 1 Thus, the characteristic system (64) giving the singularity of U, V is

{υ = 1 + υβ, β = ρυ 2 , 1 − β − 2ρυ 2 = 0},

486

VII. APPLICATIONS OF SINGULARITY ANALYSIS

(tree)

(forest)

(graph)

(connected graph)

Configuration / OGF

coefficients (exact / asymptotic)

Trees (EIS A001764)

z + z 2 + 3z 3 + 12z 4 + 55z 5 + · · · 3n − 3 1 2n − √ 1 n−1 3 27 ∼ √ ( )n 27 π n 3 4

T 3 − zT + z 2 = 0

Forests (EIS A054727) F 3 + (z 2 − z − 3)F 2 + (z + 3)F − 1 = 0

Connected graphs (EIS A007297) C 3 + C 2 − 3zC + 2z 2 = 0

Graphs (EIS A054726) G 2 + (2z 2 − 3z − 2)G + 3z + 1 = 0

1 + z + 2z 2 + 7z 3 + 33z 4 + 181z 5 · · · n X n 3n − 2 j − 1 1 j=1

2n − j

j −1

n− j

0.07465 ∼ √ (8.22469)n π n3 z + z 2 + 4z 3 + 23z 4 + 156z 5 + · · · 2n−3 X 3n − 3 j − 1 1 n−1 n+ j j −n+1 √ √ j=n−1 2 6 − 3 2 √ n ∼ 6 3 √ 18 π n 3 1 + z + 2z 2 + 8z 3 + 48z 4 + 352z 5 + · · · n−1 n 2n − 2 − j n−1− j 1X 2 (−1) j n−1− j j n j=0 p √ √ n 140 − 99 2 q ∼ 6+4 2 4 π n3

Figure VII.14. (Top) Non-crossing graphs: a tree, a forest, a connected graph, and a general graph. (Bottom) The enumeration of non-crossing configurations by algebraic functions.

VII. 6. IRREDUCIBLE CONTEXT-FREE STRUCTURES

487

4 , υ = 3 , β = 1 . The complete asymptotic formula is whose positive solution is ρ = 27 2 3 displayed in Figure VII.14. (In a simple case like this, we have more: T satisfies T 3 −zT +z 2 = 3n−3 1 0, which, by Lagrange inversion, gives Tn = 2n−1 n−1 .)

Forests. A (non-crossing) forest is a non-crossing graph that is acyclic. In the present context, it is not possible to express forests simply as sequences of trees, because of the geometry of the problem. Starting conventionally from the root vertex 0 and following all connected edges defines a “backbone” tree. To the left of every vertex of the tree, a forest may be placed. There results the decomposition (expressed directly in terms of OGFs) F = 1 + T [z 7→ z F],

(68)

where T is the OGF of trees and F is the OGF of forests. In (68), the term T [z 7→ z F] denotes a functional composition. A context-free specification in standard form results mechanically from (67) upon replacing z by z F: (69)

{ F = 1 + T,

T = z FU,

U = 1 + U V,

V = z FU 2 }.

This system is irreducible and aperiodic, so that the asymptotic shape of Fn is a priori of the form γ ωn n −3/2 according to Theorem VII.5. The characteristic system is found to have three . solutions, of which only one has all its components positive, corresponding to ρ = 0.12158, a 3 2 root of the cubic equation 5ρ − 8ρ − 32ρ + 4 = 0. (The values of constants are otherwise worked out in Example VII.19, p. 502, by means of the equational approach.) Graphs. Similar constructions (see [245]) give the OGFs of connected and general graphs, with the results tabulated in Figure VII.14. In summary: Proposition VII.6. The number of non-crossing trees, forests, connected graphs, and graphs each satisfy an asymptotic formula of the form C An . π n3 The common shape of the asymptotic estimates is worthy of note, as is the fact that binomial expressions are available in each particular case (Note VII.34, p. 495, introduces a general framework that “explains” the existence of such binomial expressions). . . . . . . . . . . . . . . . . . . √

Example VII.17. Random boolean expressions. We reconsider boolean expressions in the form of and–or trees introduced in Example I.15, p. 69, in connection with Hipparchus of Rhodes and Schr¨oder, and in Example I.17, p. 77. Such an expression is described by a binary tree whose internal nodes can be tagged with “∨” (or-function) or “∧” (and-function); external nodes are formal variables and their negations (“literals”). We fix the number of variables to some number m. The class E of all such boolean expressions satisfies a symbolic equation of the form m ∧ ∨ X E = ւ ց + ւ ց + x j + ¬x j . E E E E j=1 Size is taken to be the number of internal (binary) nodes; that is, the number of boolean connectives. Each boolean expression given in the form of such an and–or tree represents a certain m boolean function of m variables, among the 22 functions. The corresponding OGF and coefficients are √ 2m 1 − 1 − 16mz 1 2n n n n+1 ∼ √ E(z) = , E n ≡ [z ]E(z) = 2 (2m) (16m)n , 4z n+1 n π n3 the radius of convergence of E(z) being ρ = 1/(16m).

488

VII. APPLICATIONS OF SINGULARITY ANALYSIS

Our purpose is to establish the following result due to Lefmann and Savick´y [405], our line of proof following [115]. Proposition VII.7. Let f be a boolean function of m variables (m fixed). Then the probability that a random and–or formula of size n computes f converges, as n tends to infinity, to a constant value ̟ ( f ) 6= 0.

Proof. Consider, for each f , the subclass Y f ⊂ E of expressions that compute f . We thus m have 22 such classes. It is then immediate to write combinatorial equations describing the Y f , by considering all the ways in which a function f can arise. Indeed, if f is not a literal, then ∨ ∨ X X ւ ց ւ ց Yf = + Yg Yh Yg Yh , (g∨h)= f (g∧h)= f while, if f = x j (say), then Yf = xj +

∨ ∨ X ւ ց ւ ց + Yg Yh . Yg Yh (g∧h)= f (g∨h)= f X

m

Thus, at generating function level, we have a system of 22 polynomial equations. This system is irreducible: given two functions f and g represented by 8 and Ŵ (say), we can always construct an expression for f involving the expression Ŵ by building a tree of the form (8 ∧ (True ∨ Ŵ)) = ((8 ∧ ((x1 ∨ ¬x1 ) ∨ Ŵ)). Thus any Y f depends on any other Yg . Similar arguments, based on the fact that True = (True ∧ True) = (True ∧ True ∧ True) = · · · ,

with “True” itself representable as (x1 ∨ ¬x1 ) = ((x1 ∧ x1 ) ∨ ¬x1 ) = · · · , guarantee aperiodicity. Thus Theorem VII.5 applies: the Y f all have the same radius of P convergence, and that radius must be equal to that of E(z) (namely ρ = 1/(16m)), since E = f Y f . Thereby the proposition is established.

It is an interesting and largely open problem to characterize the relation between the limit probability ̟ ( f ) of a function f and its structural complexity. At least, the cases m = 1, 2, 3 can be solved exactly and numerically: it appears that functions of low complexity tend to occur much more frequently, as shown by the data of [115]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

VII. 6.3. The analysis of irreducible polynomial systems. The analytic engine behind Theorem VII.5 is a fundamental result, the “Drmota–Lalley–Woods” (DLW) Theorem, due to independent research by several authors: Drmota [172] developed a version of the theorem in the course of studies relative to limit laws in various families of trees defined by context-free grammars; Woods [616], motivated by questions of boolean complexity and finite model theory, gave a form expressed in terms of colouring rules for trees; finally, Lalley [395] came across a similarly general result when quantifying return probabilities for random walks on groups. Drmota and Lalley show how to pull out limit Gaussian laws for simple parameters (by a perturbative analysis; see Chapter IX); Woods shows how to deduce estimates of coefficients even in some periodic or non-irreducible cases. In the treatment that follows we start from a polynomial system of equations, y j = 8 j (z, y1 , . . . , ym ) , j = 1, . . . , m,

VII. 6. IRREDUCIBLE CONTEXT-FREE STRUCTURES

489

in accordance with the notations adopted at the beginning of the section. We only consider nonlinear systems defined by the fact that at least one polynomial 8 j is nonlinear in some of the indeterminates y1 , . . . , ym . (Linear systems have been discussed extensively in Chapter V.) For applications to combinatorics, we define four possible attributes of a polynomial system. The first one is a natural positivity condition. (i) Algebraic positivity (or a-positivity). A polynomial system is said to be apositive if all the component polynomials 8 j have non-negative coefficients. Next, we want to restrict consideration to systems that determine a unique solution vector (y1 , . . . , ym ) ∈ (C[[z]])m . Define the z-valuation val(Ey ) of a vector yE ∈ C[[z]]m as the minimum over all j’s of the individual valuations12 val(y j ). The distance between two vectors is defined as usual by d(E u , vE) = 2− val(Eu −Ev ) . Then: (ii) Algebraic properness (or a-properness). A polynomial system is said to be a-proper if it satisfies a Lipschitz condition d(8(Ey ), 8(Ey ′ )) < K d(Ey , yE ′ )

for some K < 1.

In that case, the transformation 8 is a contraction on the complete metric space of formal power series and, by the general fixed point theorem, the equation yE = 8(Ey ) admits a unique solution. This solution may be obtained by the iterative scheme, yE(0) = (0, . . . , 0)t ,

yE(h+1) = 8(y (h) ),

yE = lim yE(h) . h→∞

in accordance with our discussion of the semantics of recursion, on p. 31. The key notion is irreducibility. To a polynomial system, yE = 8(Ey ), associate its dependency graph defined in the usual way as a graph whose vertices are the numbers 1, . . . , m and the edges ending at a vertex j are k → j, if y j figures in a monomial of 8k . (iii) Algebraic irreducibility (or a-irreducibility). A polynomial system is said to be a-irreducible if its dependency graph is strongly connected. (This notion matches that of Definition VII.5, p. 483.) Finally, one needs the usual technical notion of aperiodicity: (iv) Algebraic aperiodicity (or a-aperiodicity). A proper polynomial system is said to be aperiodic if each of its component solutions y j is aperiodic in the sense of Definition IV.5, p. 266. We can now state: Theorem VII.6 (Irreducible positive polynomial systems, DLW Theorem). Consider a nonlinear polynomial system yE = 8(Ey ) that is a-positive, a-proper, and a-irreducible. Then, all component solutions y j have the same radius of convergence ρ < ∞, and there exist functions h j analytic at the origin such that, in a neighbourhood of ρ: p 1 − z/ρ . (70) yj = h j 12Let f = P∞ f z n with f 6= 0 and f = · · · = f β 0 β−1 = 0; the valuation of f is by definition n=β n

val( f ) = β; see Appendix A.5: Formal power series, p. 730.

490

VII. APPLICATIONS OF SINGULARITY ANALYSIS

In addition, all other dominant singularities are of the form ρω with ω a root of unity. If furthermore the system is a-aperiodic, all y j have ρ as unique dominant singularity. In that case, the coefficients admit a complete asymptotic expansion, X (71) [z n ]y j (z) ∼ ρ −n dk n −3/2−k , k≥0

for computable dk .

Proof. The proof consists in gathering by stages consequences of the assumptions. It is essentially based on a close examination of “failure” of the multivariate implicit function theorem and the way this situation leads to square-root singularities. (a) As a preliminary observation, we note that each component solution y j is an algebraic function that has a non-zero radius of convergence. This can be checked directly by the method of majorant series (Note IV.20, p. 250), or as a consequence of the multivariate version of the implicit function theorem (Appendix B.5: Implicit Function Theorem, p. 753). (b) Properness together with the positivity of the system implies that each y j (z) has non-negative coefficients in its expansion at 0, since it is a formal limit of approximants that have non-negative coefficients. In particular, by positivity, ρ j is a singularity of y j (by virtue of Pringsheim’s theorem). From the known nature of singularities of algebraic functions (e.g., the Newton–Puiseux Theorem, p. 498 below), there must exist some order R ≥ 0 such that each Rth derivative ∂zR y j (z) becomes infinite as z → ρ − j . We establish now that ρ1 = · · · = ρm . In effect, differentiation of the equations composing the system implies that a derivative of arbitrary order r , ∂zr y j (z), is a linear form in other derivatives ∂zr y j (z) of the same order (and a polynomial form in lower order derivatives); also the linear combination and the polynomial form have nonnegative coefficients. Assume a contrario that the radii were not all equal, say ρ1 = · · · = ρs , with the other radii ρs+1 , . . . being strictly greater. Consider the system differentiated a sufficiently large number of times, R. Then, as z → ρ1 , we must have ∂zR y j tending to infinity for j ≤ s. On the other hand, the quantities ys+1 , etc., being analytic, their Rth derivatives that are analytic as well must tend to finite limits. In other words, because of the irreducibility assumption (and again positivity), infinity has to propagate and we have reached a contradiction. Thus: all the y j have the same radius of convergence. We let ρ denote this common value. (c1 ) The key step consists in establishing the existence of a square-root singularity at the common singularity ρ. Consider first the scalar case, that is (72)

y − φ(z, y) = 0,

where φ is assumed to be a nonlinear polynomial in y and have non-negative coefficients. This case belongs to the smooth implicit function schema, whose argument we briefly revisit under our present perspective. Let y(z) be the unique branch of the algebraic function that is analytic at 0. Comparison of the asymptotic orders in y inside the equality y = φ(z, y) shows that (by

VII. 6. IRREDUCIBLE CONTEXT-FREE STRUCTURES

491

nonlinearity) we cannot have y → ∞ when z tends to a finite limit. Let now ρ be the radius of convergence of y(z). Since y(z) is necessarily finite at its singularity ρ, we set τ = y(ρ) and note that, by continuity, τ − φ(ρ, τ ) = 0. By the implicit function theorem, a solution (z 0 , y0 ) of (72) can be continued analytically as (z, y0 (z)) in the vicinity of z 0 as long as the derivative with respect to y (the simplest form of a Jacobian), J (z 0 , y0 ) := 1 − φ y′ (z 0 , y0 ), remains non-zero. The quantity ρ being a singularity, we must thus have J (ρ, τ ) = 0. ′′ is non-zero at (ρ, τ ) (by nonlinearity On the other hand, the second derivative −φ yy and positivity). Then, the local expansion of the defining equation (72) at (ρ, τ ) binds (z, y) locally by 1 ′′ (ρ, τ ) + · · · = 0, −(z − ρ)φz′ (ρ, τ ) − (y − τ )2 φ yy 2 implying the singular expansion y − τ = −γ (1 − z/ρ)1/2 + · · · . This establishes the first part of the assertion in the scalar case. (c2 ) In the multivariate case, we graft Lalley’s ingenious argument [395] that is based on a linearized version of the system to which Perron–Frobenius theory is applicable. First, irreducibility implies that any component solution y j depends positively and nonlinearly on itself (by possibly iterating 8), so that a contradiction in asymptotic regimes would result, if we suppose that any y j tends to infinity. Each y j (z) remains finite at the positive dominant singularity ρ. Now, the multivariate version of the implicit function theorem (Theorem B.6, p. 755) grants us locally the analytic continuation of any solution y1 , y2 , . . . , ym at z 0 provided there is no vanishing of the Jacobian determinant ∂ J (z 0 , y1 , . . . , ym ) := det δi, j − 8i (z 0 , y1 , . . . , ym ) . ∂yj i, j=1 . . m Thus, we must have (73)

J (ρ, τ1 , . . . , τm ) = 0

where

τ j := y j (ρ).

The next argument uses Perron–Frobenius theory (Subsection V. 5.2 and Note V.34, p. 345) and linear algebra. Consider the Jacobian matrix ∂ K (z, y1 , . . . , ym ) := 8i (z, y1 , . . . , ym ) , ∂yj i, j=1 . . m which represents the “linear part” of 8. For z, y1 , . . . , ym all non-negative, the matrix K has positive entries (by positivity of 8) so that it is amenable to Perron–Frobenius theory. In particular it has a positive eigenvalue λ(z, y1 , . . . , ym ) that dominates all the other in modulus. The quantity λ(z) := λ(z, y1 (z), . . . , ym (z))

492

VII. APPLICATIONS OF SINGULARITY ANALYSIS

is increasing, as it is an increasing function of the matrix entries that themselves increase with z for z ≥ 0. We propose to prove that λ(ρ) = 1, In effect, λ(ρ) < 1 is excluded since otherwise (I − K ) would be invertible at z = ρ and this would imply J 6= 0, thereby contradicting the singular character of the y j (z) at ρ. Assume a contrario λ(ρ) > 1 in order to exclude the other case. Then, by the monotonicity and continuity of λ(z), there would exist ρ < ρ such that λ(ρ) = 1. Let v be a left eigenvector of K (ρ, y1 (ρ), . . . , ym (ρ)) corresponding to the eigenvalue λ(ρ). Perron–Frobenius theory guarantees that such a vector v has all its coefficients that are positive. Then, upon multiplying on the left by v the column vectors corresponding to y and 8(y) (which are equal), one gets an identity; this derived identity, upon expanding near ρ, gives X Bi, j (yi (z) − yi (ρ))(y j (z) − y j (ρ)) + · · · , (74) A(z − ρ) = − i, j

where · · · hides lower order terms and the coefficients A, Bi, j are non-negative with A > 0. There is a contradiction in the orders of growth if each yi is assumed to be analytic at ρ, since the left-hand side of (74) is of exact order (z − ρ) while the righthand side is at least as small as (z − ρ)2 . Thus, we must have λ(ρ) = 1 and λ(x) < 1 for x ∈ (0, ρ). A calculation similar to (74) but with ρ replaced by ρ shows finally that, if yi (z) − yi (ρ) ∼ γi (ρ − z)α , then consistency of asymptotic expansions implies 2α = 1, that is α = 12 . We have thus proved: All the component solutions y j (z) have a square-root singularity at ρ. (The existence of a complete expansion in powers of (ρ − z)1/2 results from a refinement of this argument.) The proof of the general case (70) is thus complete. (d) In the aperiodic case, we first observe that each y j (z) cannot assume an infinite value on its circle of convergence |z| = ρ, since this would contradict the boundedness of |y j (z)| in the open disc |z| < ρ (where y j (ρ) serves as an upper bound). Consequently, by singularity analysis, the Taylor coefficients of any y j (z) are O(n −1−η ) for some η > 1 and the series representing y j at the origin converges on |z| = ρ.

For the rest of the argument, we observe that, if yE = 8(z, yE), then yE = 8hmi (z, yE) where the superscript denotes iteration of the transformation 8 in the variables yE = (y1 , . . . , ym ). By irreducibility, 8hmi is such that each of its component polynomials involves all the variables. Assume a contrario the existence of a singularity ρ ∗ of some y j (z) on |z| = ρ. The triangle inequality yields |y j (ρ ∗ )| ≤ y j (ρ), and the stronger form |y j (ρ ∗ )| < y j (ρ) results from the Daffodil Lemma (p. 267). Then, the modified Jacobian matrix K hmi of 8hmi taken at the y j (ρ ∗ ) has entries dominated strictly by the entries of K hmi taken at the y j (ρ). Therefore, the dominant eigenvalue of K hmi (z, yE j (ρ ∗ )) must be strictly less than 1. This would imply that I − K hmi (z, yE j (ρ ∗ )) is invertible so that

VII. 7. THE GENERAL ANALYSIS OF ALGEBRAIC FUNCTIONS

493

the y j (z) would be analytic at ρ ∗ . A contradiction has been reached: ρ is the sole dominant singularity of each y j and this concludes the argument. Many extensions of the DLW Theorem are possible, as indicated by the notes and references below—the underlying arguments are powerful, versatile, and highly general. Consequences regarding limit distributions, as obtained by Drmota and Lalley, are further explored in Chapter IX (p. 681).

VII.29. Analytic systems. Drmota [172] has shown that the conclusions of the DLW Theorem regarding universality of the square-root singularity hold more generally for 8 j that are analytic functions of Cm+1 to C, provided there exists a positive solution of the characteristic system within the domain of analyticity of the 8 j (see the original article [172] and the note [99] for a discussion of precise conditions). This extension then unifies the DLW theorem and Theorem VII.3 relative to the smooth implicit function schema. VII.30. P´olya systems. Woods [616] has shown that several systems built from P´olya operators of the form MS ETk can also be treated by an extension of the DLW Theorem, which then unifies this theorem and Theorem VII.4. VII.31. Infinite systems. Lalley [398] has extended the conclusions of the DLW Theorem to certain infinite systems of generating function equations. This makes it possible to quantify the return probabilities of certain random walks on infinite free products of finite groups.

The square-root singularity property ceases to be universal when the assumptions of Theorems VII.5 and VII.6, in essence, positivity or irreducibility, fail to be satisfied. For instance, supertrees that are specified by a positive but reducible system have a singularity of the fourth-root type (Example VII.10, p. 412 to be revisited in Example VII.20, p. 503). We discuss next, in Section VII. 7, general methods that apply to any algebraic function and are based on the minimal polynomial equation (rather than a system) satisfied by the function. Note that the results there do not always subsume the present ones, since structure is not preserved when a system is reduced, by elimination, to a single equation. It would at least be desirable to determine directly, from a positive (but reducible) system, the type of singular behaviour of the solution, but the systematic research involved in such a programme is yet to be carried out. VII. 7. The general analysis of algebraic functions Algebraic series and algebraic functions are simply defined as solutions of a polynomial equation or system. Their singularities are strongly constrained to be branch points, with the local expansion at a singularity being a fractional power series known as a Newton–Puiseux expansion (Subsection VII. 7.1). Singularity analysis then turns out to be systematically applicable to algebraic functions, to the effect that their coefficients are asymptotically composed of elements of the form p ∈ Q \ {−1, −2, . . .}, (75) C · ωn n p/q , q see Subsection VII. 7.2. This last form includes as a special case the exponent p/q = −3/2, that was encountered repeatedly, when dealing with inverse functions, implicit functions, and irreducible systems. In this section, we develop the basic structural results that lead to the asymptotic forms (75). However, designing effective methods (i.e., decision procedures) to compute the characteristic constants in (75) is not obvious in the algebraic case. Several algorithms will be described in order to locate and

494

VII. APPLICATIONS OF SINGULARITY ANALYSIS

analyse singularities (e.g., Newton’s polygon method). In particular, the multivalued character of algebraic functions creates a need to solve what are known as connection problems. Basics. We adopt as the starting point of the present discussion the following definition of an algebraic function or series (see also Note VII.32 for a variant). Definition VII.6. A function f (z) analytic in a neighbourhood V of a point z 0 is said to be algebraic if there exists a (non-zero) polynomial P(z, y) ∈ C[z, y], such that P(z, f (z)) = 0,

(76)

z ∈ V.

A power series f ∈ C[[z]] is said to be an algebraic power series if it coincides with the expansion of an algebraic function at 0. The degree of an algebraic series or function f is by definition the minimal value of deg y P(z, y) over all polynomials that are cancelled by f (so that rational series are algebraic of degree 1). One can always assume P to be irreducible over C (that is P = Q R implies that one of Q or R is a scalar) and of minimal degree. An algebraic function may also be defined by starting with a polynomial system of the form P1 (z, y1 , . . . , ym ) = 0 .. .. .. (77) . . . Pm (z, y1 , . . . , ym ) = 0,

where each P j is a polynomial. A solution of the system (77) is by definition an m– tuple ( f 1 , . . . , f m ) that cancels each P j ; that is, P j (z, f 1 , . . . , f m ) = 0. Any of the f j is called a component solution. A basic but non-trivial result of elimination theory is that any component solution of a non-degenerate polynomial system is an algebraic series (Appendix B.1: Algebraic elimination, p. 739). In other words, one can eliminate the auxiliary variables y2 , . . . , ym and construct a single bivariate polynomial Q such that Q(z, y1 ) = 0. We stress the point that, in the definitions by an equation (76) or a system (77), no positivity of any sort nor irreducibility is assumed. The analysis which is now presented applies to any algebraic function, whether or not it comes from combinatorics.

VII.32. Algebraic definition of algebraic series. It is also customary to define f to be an algebraic series if it satisfies P(z, f ) = 0 in the sense of formal power series, without a priori consideration of convergence issues. Then the technique of majorant series may be used to prove that the coefficients of f grow at most exponentially. Thus, the alternative definition is indeed equivalent to Definition VII.6.

VII.33. “Alg is in Diag of Rat”. Every algebraic function F(z) over C(z) is the diagonal of a rational function G(x, y) = A(x, y)/B(x, y) ∈ C(x, y). Precisely: X X F(z) = G n,n z n , where G(x, y) = G m,n x m y n . n≥0

m,n≥0

This is implied by a theorem of Denef and Lipshitz [154], which is related to the holonomic framework (Appendix B.4: Holonomic functions, p. 748).

VII. 7. THE GENERAL ANALYSIS OF ALGEBRAIC FUNCTIONS

−1

0

495

+1

Figure VII.15. The real section of the lemniscate of Bernoulli defined by P(z, y) = (z 2 + y 2 )2 − (z 2 − y 2 ) = 0: the origin is a double point where two analytic branches meet; there are also two real branch points at z = ±1.

VII.34. Multinomial sums and algebraic coefficients. Let F(z) be an algebraic function. Then Fn = [z n ]F(z) is a (finite) linear combination of “multinomial forms” defined as X n0 + h n Sn (C; h; c1 , . . . , cr ) := c 1 · · · crnr , n 1 , . . . , nr 1 C

where the summation is over all values of n 0 , n 1 , . . . , nr satisfying a collection of linear inequalities C involving n. [Hint: a consequence of Denef–Lipshitz.] Consequently: coefficients of any algebraic function over Q(z) invariably admit combinatorial (i.e., binomial) expressions”. (Eisenstein’s lemma, p. 505, can be used to establish algebraicity over Q(z).) An alternative proof can be based on Note IV.39, p. 270, and Equation (31), p. 753.

VII. 7.1. Singularities of general algebraic functions. Let P(z, y) be an irreducible polynomial of C[z, y], P(z, y) = p0 (z)y d + p1 (z)y d−1 + · · · + pd (z).

The solutions of the polynomial equation P(z, y) = 0 define a locus of points (z, y) in C × C that is known as a complex algebraic curve. Let d be the y-degree of P. Then, for each z there are at most d possible values of y. In fact, there exist d values of y “almost always”, that is except for a finite number of cases. — If z 0 is such that p0 (z 0 ) = 0, then there is a reduction in the degree in y and hence a reduction in the number of finite y-solutions for the particular value of z = z 0 . One can conveniently regard the points that disappear as “points at infinity” (formally, one then operates in the projective plane). — If z 0 is such that P(z 0 , y) has a multiple root, then some of the values of y will coalesce. Define the exceptional set of P as the set (R is the resultant of Appendix B.1: Algebraic elimination, p. 739): (78) 4[P] := {z R(z) = 0}, R(z) := R(P(z, y), ∂ y P(z, y), y).

The quantity R(z) is also known as the discriminant of P(z, y), with y as the main variable and z a parameter. If z 6∈ 4[P], then we have a guarantee that there exist d distinct solutions to P(z, y) = 0, since p0 (z) 6= 0 and ∂ y P(z, y) 6= 0. Then, by the Implicit Function Theorem, each of the solutions y j lifts into a locally analytic function y j (z). A branch of the algebraic curve P(z, y) = 0 is the choice of such a y j (z) together with a simply connected region of the complex plane throughout which this particular y j (z) is analytic.

496

VII. APPLICATIONS OF SINGULARITY ANALYSIS

Singularities of an algebraic function can thus only occur if z lies in the exceptional set 4[P]. At a point z 0 such that p0 (z 0 ) = 0, some of the branches escape to infinity, thereby ceasing to be analytic. At a point z 0 where the resultant polynomial R(z) vanishes but p0 (z) 6= 0, then two or more branches collide. This can be either a multiple point (two or more branches happen to assume the same value, but each one exists as an analytic function around z 0 ) or a branch point (some of the branches actually cease to be analytic). An example of an exceptional point that is not a branch point is provided by the classical lemniscate of Bernoulli: at the origin, two branches meet while each one is analytic there (see Figure VII.15). A partial knowledge of the topology of a complex algebraic curve may be obtained by first looking at its restriction to the reals. Consider for instance the polynomial equation P(z, y) = 0, where P(z, y) = y − 1 − zy 2 ,

which defines the OGF of the Catalan numbers. A rendering of the real part of the curve is given in Figure VII.16. The complex aspect of the curve, as given by ℑ(y) as a function of z, is also displayed there. In accordance with earlier observations, there are normally two sheets (branches) above each point. The exceptional set is given by the roots of the discriminant, R = z(1 − 4z), 1 that is, z = 0, 4 . For z = 0, one of the branches escapes at infinity, while for z = 1/4, the two branches meet and there is a branch point: see Figure VII.16. In summary the exceptional set provides a set of possible candidates for the singularities of an algebraic function. Lemma VII.4 (Location of algebraic singularities). Let y(z), analytic at the origin, satisfy a polynomial equation P(z, y) = 0. Then, y(z) can be analytically continued along any simple path emanating from the origin that does not cross any point of the exceptional set defined in (78). Proof. At any z 0 that is not exceptional and for a y0 satisfying P(z 0 , y0 ) = 0, the fact that the discriminant is non-zero implies that P(z 0 , y) has only a simple root at y0 , and we have Py (z 0 , y0 ) 6= 0. By the Implicit Function Theorem, the algebraic function y(z) is analytic in a neighbourhood of z 0 . Nature of singularities. We start the discussion with an exceptional point that is placed at the origin (by a translation z 7→ z + z 0 ) and assume that the equation P(0, y) = 0 has k equal roots y1 , . . . , yk where y = 0 is this common value (by a translation y 7→ y + y0 or an inversion y 7→ 1/y, if points at infinity are considered). Consider a punctured disc |z| < r that does not include any other exceptional point relative to P. In the argument that follows, we let y1 , (z), . . . , yk (z) be analytic determinations of the root that tend to 0 as z → 0. Start at some arbitrary value interior to the real interval (0, r ), where the quantity y1 (z) is locally an analytic function of z. By the implicit function theorem, y1 (z) can be continued analytically along a circuit that starts from z and returns to z while simply encircling the origin (and staying within the punctured disc). Then, by permanence of (1) analytic relations, y1 (z) will be taken into another root, say, y1 (z). By repeating the

VII. 7. THE GENERAL ANALYSIS OF ALGEBRAIC FUNCTIONS

497

10 8 6 y 4 2

–1

–0.8

–0.6

z

–0.4

0

–0.2

0.2

–2 –4 –6 –8

4

–10 2 3 y0

2 1 y0

–2

–1 –2 –3 –0.1

–1

0.15 –0.05

Re(z) 0

0.2 Im(z) 0

1

–1

–0.2 0 –0.8 –0.6 –0.4 Im(z)

0.2

0.4

0.6

0.8

0.25 0.05

1

Re(z)

0.3 0.1

0.35

Figure VII.16. The real section of the Catalan curve (top). The complex Catalan curve with a plot of ℑ(y) as a function of z = (ℜ(z), ℑ(z)) (bottom left); a blow-up of ℑ(y) near the branch point at z = 1/4 (bottom right).

process, we see that, after a certain number of times κ with 1 ≤ κ ≤ k, we will have (0) (κ) obtained a collection of roots y1 (z) = y1 (z), . . . , y1 (z) = y1 (z) that form a set of κ distinct values. Such roots are said to form a cycle. In this case, y1 (t κ ) is an analytic function of t except possibly at 0 where it is continuous and has value 0. Thus, by general principles (regarding removable singularities, see Morera’s Theorem, p. 743), it is in fact analytic at 0. This in turn implies the existence of a convergent expansion near 0: (79)

κ

y1 (t ) =

∞ X

cn t n .

n=1

(The parameter t is known as the local uniformizing parameter, as it reduces a multivalued function to a single-valued one.) This translates back into the world of z: each determination of z 1/κ yields one of the branches of the multivalued analytic function as (80)

y1 (z) =

∞ X n=1

cn z n/κ .

498

VII. APPLICATIONS OF SINGULARITY ANALYSIS

Alternatively, with ω = e2iπ/κ a root of unity, the κ determinations are obtained as ( j) y1 (z)

=

∞ X

cn ωn z n/κ ,

n=1

each being valid in a sector of opening < 2π . (The case κ = 1 corresponds to an analytic branch.) If κ = k, then the cycle accounts for all the roots which tend to 0. Otherwise, we repeat the process with another root and, in this fashion, eventually exhaust all roots. Thus, all the k roots that have value 0 at z = 0 are grouped into cycles of size κ1 , . . . , κℓ . Finally, values of y at infinity are brought to zero by means of the change of variables y = 1/u, then leading to negative exponents in the expansion of y. Theorem VII.7 (Newton–Puiseux expansions at a singularity). Let f (z) be a branch of an algebraic function P(z, f (z)) = 0. In a circular neighbourhood of a singularity ζ slit along a ray emanating from ζ , f (z) admits a fractional series expansion (Puiseux expansion) that is locally convergent and of the form X f (z) = ck (z − ζ )k/κ , k≥k0

for a fixed determination of (z − ζ )1/κ , where k0 ∈ Z and κ is an integer ≥ 1, called the “branching type”13. Newton (1643–1727) discovered the algebraic form of Theorem VII.7 and published it in his famous treatise De Methodis Serierum et Fluxionum (completed in 1671). This method was subsequently developed by Victor Puiseux (1820–1883) so that the name of Puiseux series is customarily attached to fractional series expansions. The argument given above is taken from the neat presentation offered by Hille in [334, Ch. 12, vol. II]. It is known as a “monodromy argument”, meaning that it consists in following the course of values of an analytic function along paths in the complex plane till it returns to its original value. Newton polygon. Newton also described a constructive approach to the determination of branching types near a point (z 0 , y0 ), that, by means of the previous discussion, can always be taken to be (0, 0). In order to introduce the discussion, let us examine the Catalan generating function near z 0 = 1/4. Elementary algebra gives the explicit form of the two branches √ √ 1 1 1 − 1 − 4z , y2 (z) = 1 + 1 − 4z , y1 (z) = 2z 2z

whose forms are consistent with what Theorem VII.7 predicts. If however one starts directly with the equation, P(z, y) ≡ y − 1 − zy 2 = 0

13 From the general discussion, if k < 0, then κ = 1 is possible (case f (ζ ) = ∞, with a polar 0 singularity); if k0 ≥ 0, then a singularity only exists if κ ≥ 2 (case of a branch point with | f (ζ )| < ∞).

VII. 7. THE GENERAL ANALYSIS OF ALGEBRAIC FUNCTIONS

499

then, the translation z = 1/4 − Z (the minus sign is a mere notational convenience), y = 2 + Y yields

1 Q(Z , Y ) ≡ − Y 2 + 4Z + 4Z Y + Z Y 2 . 4 Look for solutions of the form Y = cZ α (1 + o(1)) with c 6= 0, whose existence is a priori granted by Theorem VII.7 (Newton–Puiseux). Each of the monomials in (81) gives rise to a term of a well-determined asymptotic order, respectively, Z 2α , Z 1 , Z α+1 , Z 2α+1 . If the equation is to be identically satisfied, then the main asymptotic order of Q(Z , Y ) should be 0. Since c 6= 0, this can only happen if two or more of the exponents in the sequence (2α, 1, α + 1, 2α + 1) coincide and the coefficients of the corresponding monomial in P(Z , Y ) is zero, a condition that is an algebraic constraint on the constant c. Furthermore, exponents of all the remaining monomials have to be larger since by assumption they represent terms of lower asymptotic order. Examination of all the possible combinations of exponents leads one to discover that the only possible combination arises from the cancellation of the first two terms of Q, namely − 41 Y 2 + 4Z , which corresponds to the set of constraints (81)

1 − c2 + 4 = 0, 4 with the supplementary conditions α + 1 > 1 and 2α + 1 > 1 being satisfied by this choice α = 1/2. We have thus discovered that Q(Z , Y ) = 0 is consistent asymptotically with Y ∼ 4Z 1/2 , Y ∼ −4Z 1/2 . 2α = 1,

The process can be iterated upon subtracting dominant terms. It invariably gives rise to complete formal asymptotic expansions that satisfy Q(Z , Y ) = 0 (in the Catalan example, these are series in ±Z 1/2 ). Furthermore, elementary majorizations establish that such formal asymptotic solutions represent indeed convergent series. Thus, local expansions of branches have indeed been determined. An algorithmic refinement (also due to Newton) is known as the method of Newton polygons. Consider a general polynomial X Q(Z , Y ) = Zaj Y bj , j∈J

and associate to it the finite set of points (a j , b j ) in N × N, which is called the Newton diagram. It is easily verified that the only asymptotic solutions of the form Y ∝ Z τ correspond to values of τ that are inverse slopes (i.e., 1x/1y) of lines connecting two or more points of the Newton diagram (this expresses the cancellation condition between two monomials of Q) and such that all other points of the diagram are on this line or to the right of it (as the other monomials must be of smaller order). In other words: Newton’s polygon method. Any possible exponent τ such that Y ∼ cZ τ is a solution to a polynomial equation corresponds to one of the inverse slopes of the left-most convex envelope of the Newton diagram. For each viable τ , a polynomial equation constrains the possible values of the corresponding

500

VII. APPLICATIONS OF SINGULARITY ANALYSIS

0.4 y 0.2

–0.4

–0.2

0

–0.2

1 0 0111 1 0 1 0 1 0 0001 0 1 0 1 0 1 0 1 0 0 0001 0 0 1 4 1 0111 0 1 1 0 1 1 0 1 0 1 0 1 000 111 1 0 0111 1 0 1 1 0 1 0 000 0 1 0 1 0 1 0 1 0 1 0 1 000 111 0 1 0 1 3 1 0111 0 1 0 1 0 1 000 0 1 0 1 1 0 11 00 000000000 111111111 0 1 0 1 0 1 0 1 0 1 0 1 000000000 111111111 0 1 0 1 0 1 0 1 00 11 00 11 0 1 0 1 000000000 111111111 2 1 0 1 0 1 0 1 0 1 00 11 00 11 0 0 1 000000000 111111111 0 1 1 0 11 1 0 1 0 1 0 0 1 000000000 111111111 00 0 1 0 1 0 1 0 1 0 0 1 1 1 0000000 1111111 000000000 111111111 001 11 0 1 0 1 0 0 0 1 0 1 0000000 1111111 0 1 0 1111111 0 1 0 1 1 1 1 1 0 0 1 0000000 0 1 1 0 1 1 0 0 0 1 0 0 1 0000000 1111111 0 0 1 0 1 0 1 05 1 1 0 1 0 1 0 1 2 3 4 6

5

0.2 z

0.4

–0.4

Figure VII.17. The real algebraic curve defined by the equation P = (y − z 2 )(y 2 − z)(y 2 − z 3 ) − z 3 y 3 near (0, 0) (left) and the corresponding Newton diagram (right).

coefficient c. Complete expansions are obtained by repeating the process, which means deflating Y from its main term by way of the substitution Y 7→ Y − cZ τ .

Figure VII.17 illustrates what goes on in the case of the curve P = 0 where P(z, y)

= (y − z 2 )(y 2 − z)(y 2 − z 3 ) − z 3 y 3 = y5 − y3 z − y4 z2 + y2 z3 − 2 z3 y3 + z4 y + z5 y2 − z6,

considered near the origin. As the factored part suggests, the curve is expected to resemble (locally) the union of two orthogonal parabolas and of a curve y = ±z 3/2 having a cusp, i.e., the union of √ y = z 2 , y = ± z, y = ±z 3/2 , respectively. It is visible on the Newton diagram that the possible exponents y ∝ z τ at the origin are the inverse slopes of the segments composing the envelope, that is, 3 1 τ = 2, τ = , τ = . 2 2 For computational purposes, once determined the branching type κ, the value of k0 that dictates where the expansion starts, and the first coefficient, the full expansion can be recovered by deflating the function from its first term and repeating the Newton diagram construction. In fact, after a few initial stages of iteration, the method of indeterminate coefficients can always be eventually applied [Bruno Salvy, private communication, August 2000]. Computer algebra systems usually have this routine included as one of the standard packages; see [531]. VII. 7.2. Asymptotic form of coefficients. The Newton–Puiseux theorem describes precisely the local singular structure of an algebraic function. The expansions are valid around a singularity and, in particular, they hold in indented discs of the type required in order to apply the formal translation mechanisms of singularity analysis.

VII. 7. THE GENERAL ANALYSIS OF ALGEBRAIC FUNCTIONS

501

P Theorem VII.8 (Algebraic asymptotics). Let f (z) = n f n z n be the branch of an algebraic function that is analytic at 0. Assume that f (z) has a unique dominant singularity at z = α1 on its circle of convergence. Then, in the non-polar case, the coefficient f n satisfies the asymptotic expansion, X (82) f n ∼ α1−n dk n −1−k/κ , k≥k0

where k0 ∈ Z and κ is an integer ≥ 2. In the polar case, κ = 1 and k0 < 0, the estimate (82) is to be interpreted as a terminating (exponential–polynomial) form. If f (z) has several dominant singularities |α1 | = |α2 | = · · · = |αr |, then there exists an asymptotic decomposition (where ǫ is some small fixed number, ǫ > 0) fn =

(83) where each

φ ( j) (n)

r X j=1

φ ( j) (n) + O((|α1 | + ǫ))−n ,

admits a complete asymptotic expansion, X ( j) φ ( j) (n) ∼ α −n dk n −1−k/κ j , j ( j)

k≥k0

( j)

with either k0 in Z and κ j an integer ≥ 2 or κ j = 1 and k0 < 0. Proof. An early version of this theorem appeared as [220, Th. D, p. 293]. The expansions granted by Theorem VII.7 are of the exact type required by singularity analysis (Theorem VI.4, p. 393). For multiple singularities, Theorem VI.5 (p. 398) based on composite contours is to be used: in that case each φ ( j) (n) is the contribution obtained by transfer of the corresponding local singular element. In the case of multiple singularities, partial cancellations may occur in some of the dominant terms of (83): consider for instance the case of 1 q = 1 + 0.60z + 0.04z 2 − 0.36z 3 − 0.408z 4 − · · · , 6 2 1 − 5z + z where the function has two complex conjugate singularities with an argument not commensurate to π , and refer to the corresponding discussion of rational coefficients asymptotics (Subsection IV. 6.1, p. 263). Fortunately, such delicate arithmetic situations tend not to arise in combinatorial situations. Example VII.18. Branches of unary–binary trees. The generating function of unary–binary trees (Motzkin numbers, pp. 68 and 396) is f (z) defined by P(z, f (z)) = 0 where P(z, y) = y − z − zy − zy 2 ,

so that

p √ 1 − z − (1 + z)(1 − 3z) 1 − 2z − 3z 2 = . 2z 2z There exist only two branches: f and its conjugate f that form a 2–cycle at z = 1/3. The singularities of all branches are at 0, −1, 1/3 as is apparent from the explicit form of f or from f (z) =

1−z−

502

VII. APPLICATIONS OF SINGULARITY ANALYSIS

1.2 1.1 1 0.9 y 0.8 0.7 0.6

–0.4

–0.2

0.5 0

0.2 z

Figure VII.18. The real algebraic curve corresponding to non-crossing forests.

the defining equation. The branch representing f (z) at the origin is analytic there (by a general argument or by the combinatorial origin of the problem). Thus, the dominant singularity of f (z) is at 1/3 and it is unique in its modulus class. The “easy” case of Theorem VII.8 then applies once f (z) has been expanded near 1/3. As a rule, the organization of computations is simpler if one makes use of the local uniformizing parameter with a choice of sign in accordance to the direction along which the singularity is approached. In this case, we set z = 1/3 − δ 2 and find 1/2 9 63 27 2997 5 1 f (z) = 1 − 3 δ + δ 2 − δ 3 + δ 4 − δ + ··· , δ = −z . 2 8 2 128 3 This translates immediately into 8085 15 505 3n+1/2 − + · · · , 1− + f n ≡ [z n ] f (z) ∼ √ 16n 512n 2 8192n 3 2 π n3 which agrees with the direct derivation of Example VI.3, p. 396. . . . . . . . . . . . . . . . . . . . . . . . .

VII.35. Meta-asymptotics. Estimate the growth of the coefficients in the asymptotic expansions of Catalan and Motzkin (unary–binary trees) numbers. Example VII.19. Branches of non-crossing forests. Consider the polynomial equation P(z, y) = 0, where P(z, y) = y 3 + (z 2 − z − 3)y 2 + (z + 3)y − 1, (see Figure VII.18 for the real branches) and the combinatorial GF satisfying P(z, F) = 0 determined by the initial conditions, F(z) = 1 + 2z + 7z 2 + 33z 3 + 181z 4 + 1083z 5 + · · · . (EIS A054727). F(z) is the OGF of non-crossing forests defined in Example VII.16, p. 485. The exceptional set is mechanically computed: its elements are roots of the discriminant R = −z 3 (5z 3 − 8z 2 − 32z + 4). Newton’s algorithm shows that two of the branches at 0, say y0 and y2 , form a cycle of length 2 √ √ with y0 = 1− z+O(z), y2 = 1+ z+O(z) while it is the “middle branch” y1 = 1+z+O(z 2 ) that corresponds to the combinatorial GF F(z).

VII. 7. THE GENERAL ANALYSIS OF ALGEBRAIC FUNCTIONS

503

The non-zero exceptional points are the roots of the cubic factor of R; namely . = {−1.93028, 0.12158, 3.40869}. . Let ξ = 0.1258 be the root in (0, 1). By Pringsheim’s theorem and the fact that the OGF of an infinite combinatorial class must have a positive dominant singularity in [0, 1], the only possibility for the dominant singularity of y1 (z) is ξ . For z near ξ , the three branches of the cubic give rise to one branch that is analytic with value approximately 0.67816 and a cycle of two conjugate branches with value near 1.21429 at z = ξ . The expansion of the two conjugate branches is of the singular type, p α ± β 1 − z/ξ ,

where

q 43 18 35 1 . . 228 − 981ξ − 5290ξ 2 = 0.14931. + ξ − ξ 2 = 1.21429, β = 37 37 74 37 The determination with a minus sign must be adopted for representing the combinatorial GF when z → ξ − since otherwise one would get negative asymptotic estimates for the non-negative coefficients. Alternatively, one may examine the way the three real branches along (0, ξ ) match with one another at 0 and at ξ − , then conclude accordingly. Collecting partial results, we finally get by singularity analysis the estimate β 1 1 . Fn = √ ωn 1 + O( ) , ω = = 8.22469 3 n ξ 2 πn with the cubic algebraic number ξ and the sextic β as above. . . . . . . . . . . . . . . . . . . . . . . . . . . . . α=

The example above illustrates several important points in the analysis of coefficients of algebraic functions when there are no simple explicit radical forms. First, a given combinatorial problem determines a unique branch of an algebraic curve at the origin. Next, the dominant singularity has to be identified by “connecting” the combinatorial branch with the branches at every possible singularity of the curve. Finally, computations tend to take place over algebraic numbers and not simply rational numbers. So far, examples have illustrated the common situation where the function’s exponent at its dominant singularity is 1/2. Our last example shows a case where the exponent assumes a different value, namely 1/4. Example VII.20. Branches of supertrees. Consider the quartic equation y 4 − 2 y 3 + (1 + 2 z) y 2 − 2 yz + 4 z 3 = 0 and let K be the branch analytic at 0 determined by the initial conditions: K (z) = 2 z 2 + 2 z 3 + 8 z 4 + 18 z 5 + +64 z 6 + 188 z 7 + · · · . The OGF K corresponds to bicoloured supertrees of Example VI.10, p. 412; a partial graph is represented in Figure VII.19. The discriminant is found to be R = 16 z 4 16 z 2 + 4 z − 1 (−1 + 4 z)3 , √ with roots at 1/4 and (−1 ± 5)/8. The dominant singularity of the branch of combinatorial interest turns out to be at z = 41 where K (1/4) = 1/2. The translation z = 1/4+Z , y = 1/2+Y

504

VII. APPLICATIONS OF SINGULARITY ANALYSIS 2

1.5

1

k

0.5

–0.6

–0.4

–0.2

0.2

z –0.5

–1

Figure VII.19. The real algebraic curve associated with the generating function of supertrees of type K .

then transforms the basic equation into 4 Y 4 + 8 Z Y 2 + 16 Z 3 + 12 Z 2 + Z = 0. According to Newton’s polygon method, the main cancellation arises from 4Y 4 + Z = 0: this corresponds to a segment of inverse slope 1/4 in the Newton diagram and accordingly to a cycle formed with four conjugate branches, i.e., a fourth-root singularity. Thus, one has 1/4 3/4 1 1 1 4n 1 K (z) ∼ 1/2 − √ , −z −z −√ + · · · , [z n ]K (z) ∼ n→∞ 8Ŵ( 3 )n 5/4 2 4 2 4 z→ 41 4 which is consistent with values found earlier (p. 412). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Computable coefficient asymptotics. The previous discussion contains the germ of a complete algorithm for deriving an asymptotic expansion of coefficients of any algebraic function. We sketch in Note VII.36 the main principles, while leaving some of the details to the reader. Observe that the problem is a connection problem: the “shapes” of the various sheets around each point (including the exceptional points) are known, but it remains to connect them together and see which ones are encountered first when starting with a given branch at the origin.

VII.36. Algebraic Coefficient Asymptotics (ACA). Here is an outline of the algorithm. Algorithm ACA: Input: A polynomial P(z, y) with d = deg y P(z, y); a series Y (z) such that P(z, Y ) = 0 and assumed to be specified by sufficiently many initial terms so as to be distinguished from all other branches. Output: The asymptotic expansion of [z n ]Y (z) whose existence is granted by Theorem VII.8. The algorithm consists of three main steps: Preparation (I), Dominant singularities (II), and Translation (III). I. Preparation: Define the discriminant R(z) = R(P, Py′ , y).

VII. 7. THE GENERAL ANALYSIS OF ALGEBRAIC FUNCTIONS

505

(P1 ) Compute the exceptional set 4 = {z R(z) = 0} and the points of infinity 40 = {z p0 (z) = 0}, where p0 (z) is the leading coefficient of P(z, y) considered as a function of y. (P2 ) Determine the Puiseux expansions of all the d branches at each of the points of 4 ∪ {0} (by Newton diagrams and/or indeterminate coefficients). This includes the expansion of analytic branches as well. Let {yα, j (z)}dj=1 be the collection of all such expansions at some α ∈ 4 ∪ {0}. (P3 ) Identify the branch at 0 that corresponds to Y (z). II. Dominant singularities: (Controlled approximate matching of branches). Let 41 , 42 , . . . be a partition of the elements of 4 ∪ {0} sorted according to the increasing values of their modulus: it is assumed that the numbering is such that if α ∈ 4i and β ∈ 4 j , then |α| < |β| is equivalent to i < j. Geometrically, the elements of 4 have been grouped in concentric circles. First, a preparation step is needed. (D1 ) Determine a non-zero lower bound δ on the radius of convergence of any local Puiseux expansion of any branch at any point of 4. Such a bound can be constructed from the minimal distance between elements of 4 and from the degree d of the equation. The sets 4 j are to be examined in sequence until it is detected that one of them contains a singularity. At step j, let σ1 , σ2 , . . . , σs be an arbitrary listing of the elements of 4 j . The problem is to determine whether any σk is a singularity and, in that event, to find the right branch to which it is associated. This part of the algorithm proceeds by controlled numerical approximations of branches and constructive bounds on the minimum separation distance between distinct branches. (D2 ) For each candidate singularity σk , with k ≥ 2, set ζk = σk (1 − δ/2). By assumption, each ζk is in the domain of convergence of Y (z) and of any yσk , j . (D3 ) Compute a non-zero lower bound ηk on the minimum distance between two roots of P(ζk , y) = 0. This separation bound can be obtained from resultant computations. (D4 ) Estimate Y (ζk ) and each yσk , j (ζk ) to an accuracy better than ηk /4. If two elements, Y (z) and yσk , j (z) are (numerically) found to be at a distance less than ηk for z = ζk , then they are matched: σk is a singularity and the corresponding yσk , j is the corresponding singular element. Otherwise, σk is declared to be a regular point for Y (z) and discarded as candidate singularity. The main loop on j is repeated until a singularity has been detected, when j = j0 , say. The radius of convergence ρ is then equal to the common modulus of elements of 4 j0 ; the corresponding singular elements are retained. III. Coefficient expansion: Collect the singular elements at all the points σ determined to be a dominant singularity at Phase II. Translate termwise using the singularity analysis rule, Ŵ(− p/κ + n) (σ − z) p/κ 7→ σ p/κ−n , Ŵ(− p/κ)Ŵ(n + 1) and reorganize into descending powers of n, if needed.

This algorithm vindicates the following assertion (see also Chabaud’s thesis [110]). Proposition VII.8 (Decidability of algebraic connections.). The dominant singularities of a branch of an algebraic function can be determined in a finite number of operations by the algorithm ACA of Note VII.36.

VII.37. Eisenstein’s lemma. Let y(z) be an algebraic function with rational coefficients (for instance a combinatorial generating function) satisfying 8(z, y(z)) = 0, where the coefficient of the polynomial 8 are in C; then there exists a polynomial 9 with integer coefficients such that 9(z, y(z)) = 0. (Hint [65]. Consider the case where the coefficients of 8 are Q–linear combinations of 1 and an irrational α, and write 8(z, y) = 81 (z, y) + α8α (z, y), where 81 , 8α ∈ Q[z, y]; extracting [z n ]8(z, y(z)) would produce a Q–linear relation between 1

506

VII. APPLICATIONS OF SINGULARITY ANALYSIS

and α, unless one of 81 , 8α is trivial, which must then be the case.) Thus, one can get 9(z, y) in Q[z, y], and by clearing denominators, in Z[z, y]. As a consequence, for algebraic y(z) with rational coefficients, there exists an integer B such that for all n, one has B n [z n ]y(z) ∈ Z. Since P P there are infinitely many primes, the functions e z , log(1 + z), z n /n 2 , z n /(n!)3 , and so on, are transcendental (i.e., not algebraic).

VII.38. Powers of binomial coefficients. Define Sr (z) :=

2n r n n≥0 n z , with r ∈ Z>0 . For

P

even r = 2ν the function S2ν (z) is transcendental (not algebraic) since its singular expansion involves a logarithmic term. For odd r = 2ν + 1 and r ≥ 3, the function S2ν+1 (z) is also transcendental as a consequence of the arithmetic transcendence of the number π ; see [220]. These functions intervene in P´olya’s drunkard problem (p. 425). In contrast with the “hard” theory of arithmetic transcendence, it is usually “easy” to establish transcendence of functions, by exhibiting a local expansion that contradicts the Newton–Puiseux Theorem (p. 498).

VII. 8. Combinatorial applications of algebraic functions In this section, we introduce objects whose construction leads to algebraic functions, in a way that extends the basic symbolic method. This includes: walks with a finite number of allowed jumps (Subsection VII. 8.1) and planar maps (Subsection VII. 8.2). In such cases, bivariate functional equations reflect the combinatorial decompositions of objects. The common form of these functional equations is (84)

8(z, u, F(z, u), h 1 (z), . . . , h r (z)) = 0,

where 8 is a known polynomial and the unknown functions are F and h 1 , . . . , h r . Specific methods are needed in order to attain solutions to such functional equations that would seem at first glance to be grossly underdetermined. Walks and excursions lead to a linear version of (84) that is treated by the so-called kernel method. Maps lead to nonlinear versions that are solved by means of Tutte’s quadratic method. In both cases, the strategy consists in binding z and u by forcing them to lie on an algebraic curve (suitably chosen in order to eliminate the dependency on F(z, u)), and then pulling out consequences of such a specialization. Asymptotic estimates can then be developed from such algebraic solutions, thanks to the general methods expounded in the previous section. VII. 8.1. Walks and the kernel method. Start with a set that is a finite subset of Z and is called the set of jumps. A walk (relative to ) is a sequence w = (w0 , w1 , . . . , wn ) such that w0 = 0 and wi+1 − wi ∈ , for all i, 0 ≤ i < n. A non-negative walk (also known as a “meander”) satisfies wi ≥ 0 and an excursion is a non-negative walk such that, additionally, wn = 0. A bridge is a walk such that wn = 0. The quantity n is called the length of the walk or the excursion. For instance, Dyck paths and Motzkin paths analysed in Section V. 4, p. 318, are excursions that correspond to = {−1, +1} and = {−1, 0, +1}, respectively. (Walks and excursions are also somewhat related to paths in graphs in the sense of Section V. 5, p. 336.) We let −c denote the smallest (negative) value of a jump, and d denote the largest (positive) jump. A fundamental rˆole is played in this discussion by the characteristic

VII. 8. APPLICATIONS OF ALGEBRAIC FUNCTIONS

507

polynomial14 of the walk, S(y) :=

X

ω∈

yω =

d X

Sj y j,

j=−c

which is a Laurent polynomial; that is, it involves negative powers of the variable y. . Walks. Observe first the rational character of the BGF of walks, with z marking length and u marking final altitude: (85)

W (z, u) =

1 . 1 − zS(u)

Since walks may terminate at a negative altitude, this is a Laurent series in u. Bridges. The GF of bridges is formally [u 0 ]W (z, u), since bridges correspond to walks that end at altitude 0. Thus one has Z 1 1 du (86) B(z) = , 2iπ γ 1 − zS(u) u upon integrating along a circle γ that separates the small and large branches, as discussed below. The integral can then be evaluated by residues: details are found in [27]; the net result is Equation (97), p. 511. Excursions and meanders. We propose next to determine the number Fn of excursions of length n and type , via the corresponding OGF F(z) =

∞ X

Fn z n .

n=0

In fact, we shall determine the more general BGF X F(z, u) := Fn,k u k z n , n,k

where Fn,k is the number of non-negative walks (meanders) of length n and final altitude k (i.e., the value of wn in the definition of a walk is constrained to equal k). In particular, one has F(z) = F(z, 0). The main result of this subsection can be stated informally as follows (see Propositions VII.9, p. 510 and VII.10, p. 513 for precise versions): For each finite set ∈ Z, the generating function of excursions is an algebraic function that is explicitly computable from . The number of excursions of length n satisfies asymptotically a universal law of the form C An n −3/2 . 14 If is a set, then the coefficients of S lie in {0, 1}. The treatment presented here applies in all generality to cases where the coefficients are arbitrary positive real numbers. This accounts for probabilistic situations as well as multisets of jump values.

508

VII. APPLICATIONS OF SINGULARITY ANALYSIS

There are many ways to view this result. The problem is usually treated within probability theory by means of Wiener–Hopf factorizations [515], and Lalley [396] offers an insightful analytic treatment from this angle. On another level, Labelle and Yeh [392] show that an unambiguous context-free specification of excursions can be systematically constructed, a fact that is sufficient to ensure the algebraicity of the GF F(z). (Their approach is implicitly based on the construction of a pushdown automaton itself equivalent, by general principles, to a context-free grammar.) The Labelle–Yeh construction reduces the problem to a large, but somewhat “blind”, combinatorial preprocessing. Accordingly, for analysts, it has the disadvantage of not extracting a simpler analytic (but non-combinatorial) structure inherent in the problem: the shape of the end result can indeed be predicted by the Drmota–Lalley–Woods Theorem, but the nature of the constants involved is not clearly accessible in this way. The kernel method. The method described below is often known as the kernel method. It takes some of its inspiration from exercises in the 1968 edition of Knuth’s book [377] (Ex. 2.2.1.4 and 2.2.1.11), where a new approach was proposed to the enumeration of Catalan and Schr¨oder objects. The technique has since been extended and systematized by several authors; see for instance [26, 27, 86, 202, 203] for relevant combinatorial works. Our presentation below follows that of Lalley [396] and of Banderier and Flajolet [27]. The polynomial f n (u) = [z n ]F(z, u) is the generating function of non-negative walks of length n, with u recording final altitude. A simple recurrence relates f n+1 (u) to f n (u), namely, f n+1 (u) = S(u) · f n (u) − rn (u),

(87)

where rn (u) is a Laurent polynomial consisting of the sum of all the monomials of S(u) f n (u) that involve negative powers15 of u: (88)

rn (u) :=

−1 X

j=−c

u j ([u j ] S(u) f n (u)) = {u 1. Second, it may be the case that a parameter is accessible via a collection of univariate GFs rather than a BGF (see typically our discussion of extremal parameters in Section III. 8, p. 214). We briefly indicate in this section ways to deal with such situations. VII. 10.1. Moment pumping. Our reader should have no difficulty in recognizing as familiar at least the first two steps of the following procedure, nicknamed “moment pumping” in [249], which serve to extract moments from bivariate generating functions. Procedure: Moment Pumping Input: A bivariate generating function F(z, u) determined by a functional equation. Output: The limit law corresponding to the array of coefficients [z n u k ]F(z, u); that is, the asymptotic probability distribution of a parameter χ on a class Fn . Step 1. Elucidate the singular structure of F(z, 1) corresponding to the counting problem [z n ]F(z, 1). (Tools of Chapters IV–VII are well-suited for this task, the functional equation satisfied by F(z, 1) being usually simpler than that of F(z, u).) Step 2. Work out the singular structure (main terms) of each of the partial derivatives ∂r F(z, u) µr (z) := r ∂u u=1

VII. 10. SINGULARITY ANALYSIS AND PROBABILITY DISTRIBUTIONS

533

for r = 1, 2, . . ., and use meromorphic methods or singularity analysis to conclude as to [z n ]µr (z). If, as it is most often the case, the combinatorial parameter marked by u is of polynomial growth in the size n, then the radius of convergence of each µr is a priori the same as that of F(z, 1). Furthermore, in many cases, the singular structure of the µr (z) is of the same broad type as that of µ0 (z) ≡ F(z, 1). Step 3. From the moments, as given by Step 2, attempt to reconstruct the limit distribution using the Moment Convergence Theorem (Theorem C.2, p. 778).

In order for the procedure to succeed22, we typically need the standard deviation of χ to be of the same order as the mean, which necessitates that the distribution is spread in the sense of Chapter III, p. 161. (Otherwise, there are larger and larger cancellations in moments of the centred and scaled variant of χ , so that the analysis requires an unbounded number of terms in the singular expansions of the GFs µr (z); see also Pittel’s study [484] for an insightful discussion of related problems.) Example VII.26. The area under Dyck excursions. We now examine the coefficients in the BGF, which is a solution of the functional equation 1 (143) F(z, q) = , i.e., F(z, q) = 1 + z F(z, q)F(qz, q). 1 − z F(qz, q)

It is such that [z n q k ]F(z, q) represents the number of Dyck excursions of length 2n and area k− n (p. 330). Thus we are aiming at characterizing the distribution of area in Dyck paths. We set , which is, up to normalization, the GF of the r th factorial moments. µr (z) := ∂qr F(z, q) q=1 1 1 − √1 − 4z , as anticipated. Clearly, µ0 satisfies the relation µ0 = 1 + zµ20 , and µ0 = 2z Application of the moment pumping procedure leads to a collection of equations, µ1 µ2

= =

2zµ0 µ1 + z 2 µ0 µ′0 2zµ0 µ2 + 2zµ21 + 2z 2 µ1 µ′0 + 2z 2 µ0 µ′1 + z 3 µ0 µ′′0 ,

and so on. Precisely, the shape of the equation giving µr , for r ≥ 1, is j r X X j k k r z ∂z µ j−k , µr − j (144) µr = z k j j=0

k=0

as results, upon setting q = 1, from Leibniz’s product rule and a computation of the derivatives j ∂q F(qz, q). In particular, each µr can be expressed from the previous µ and their derivatives, since the equation relative √ to µr is of the linear form µr = 2zµ0 µr + · · · , so that µr (z) is a rational form in z and δ := 1 − 4z. An examination of the initial values of the µ then suggests that, in terms of dominant singular asymptotics, as z → 14 , there holds Kr (145) µr (z) = + O (1 − 4z)−(3r −2)/2 , r ≥ 1, (3r −1)/2 (1 − 4z) a property that is readily verified by induction. (In such situations, the closure of functions of singularity analysis class under differentiation, p. 419, proves handy.) In particular, by singularity analysis, the mean and standard deviation of χ on Fn are each of order n 3/2 . Now, equipped with (145), we can trace back the main singular contributions in (144), noting that the “weight”, as measured by the exponent of (1 − 4z)−1 , of the term in (144) 22The important Gaussian case, which is mostly excluded by moment pumping, tends to yield agreeably to the perturbation methods of Chapter IX, so that the univariate methods discussed here and those of Chapter IX are indeed complementary.

534

VII. APPLICATIONS OF SINGULARITY ANALYSIS

corresponding to generic indices j, k is (3r − k − 2)/2. Then, by identifying the corresponding coefficients, we come up with the recurrence valid for r ≥ 2 (146)

3r =

r −1 1X r r (3r − 1) 3r −1 3r − j 3 j + 4 4 j j=1

(the linear term arises from j = r, k = 1) and from (145) and (146), the shape of factorial moments, hence that of the usual power moments, results by plain singularity analysis: √ π3r (147) En χ r ∼ Mr n 3r/2 , Mr := . Ŵ((3r − 1)/2)

It can then be verified [568] that the moment Mr uniquely characterize a probability distribution (Appendix C.5: Convergence in law, p. 776). Proposition VII.15. The distribution of area χ in Dyck excursions, scaled by n −3/2 , converges to a limit, known as the Airy23 distribution of the area type, which is determined by its moments Mr , as specified by (146) and (147). In other terms, there exists a distribution function H (x) supported by R>0 such that limn→∞ Pn (χ < xn 3/2 ) = H (x).

Due to the exact correspondence between Dyck excursions and trees, the same limit distribution occurs for path length in general Catalan trees. Proposition VII.15 is originally due to Louchard [415, 416], who developed connections with Brownian motion—the limit distribution is indeed up to normalization that of Brownian excursion area. (The approach presented here also has the merit of providing finite n corrections.) Our moment pumping approach largely follows the lines of Tak´acs’ treatment [568]. The recurrence relation (144) can furthermore be solved by generating functions, to the effect that the 3r entertain intimate relations with the Airy function: for surveys, see [244, 352]. Curiously, the Wright constants arising in the enumeration of labelled graphs of fixed excess (the Pk (1) of p. 134) appear to be closely related to the moments Mr : this fact can be explained combinatorially by means of breadth-first search of graphs, as noted by Spencer [548]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

VII.53. Path length in simple varieties of trees. Under the usual conditions on φ, the limit distribution is an Airy distribution of the area type, as shown by Tak´acs [566]. VII.54. A parking problem II. This continues Example II.19, p. 146. Consider m cars and condition by the fact that everybody eventually finds a parking space and the last space remains empty. Define total displacement as the sum of the distances (over all cars) between the initially intended parking location and the first available space. The analysis reduces to the differencedifferential equation [249, 380], which generalizes (65), p. 146, F(z, q) − q F(qz, q) ∂ F(z, q) = F(z, q) · . ∂z 1−q

Moment pumping is applicable [249]: the limit distribution is once more an Airy (of area type). This problem arises in the analysis of the linear probing hashing algorithm [380, §6.4] and is of relevance as a discrete version of important coalescence models. It is also shown in [249] based on [285] that the number of inversions in a Cayley tree is asymptotically Airy. 23 The Airy function Ai(z) is of hypergeometric type and is closely related to Bessel functions of order ±1/3. It is defined as the solution of y ′′ − zy = 0 satisfying Ai(0) = 3−2/3 / Ŵ(2/3) and Ai′ (0) = −3−1/3 / Ŵ(1/3); see [3, 604] for basic properties. The 3r intervene in the expansion of log Ai(z) at infinity [244, 352]. After Louchard and Tak´acs, the distribution function H (x) can be expressed in terms of confluent hypergeometric functions and zeros of the Airy function.

VII. 10. SINGULARITY ANALYSIS AND PROBABILITY DISTRIBUTIONS

535

VII.55. The Wiener index and other functionals of trees. The Wiener index, a structural index of interest to chemists, is defined as the sum of the distances between all pairs of nodes in a tree. For simple families, as shown by Janson [348], it admits a limit distribution. (Similar properties hold for many additive functionals of combinatorial tree families [210]. As regards moment pumping, the methods are also related to those of Subsection VI. 10.3, p. 427, dedicated to tree recurrences.)

VII.56. Difference equations, polyominoes, and limit laws. Many of the q–difference equations that are defined by a polynomial relation between F(z, q), F(qz, q), . . . (and even systems) may be analysed, as shown by Richard [509, 510]. This covers several models of polyominoes, including the staircase, the horizontally-vertically convex, and the column convex ones. Area (for fixed perimeter) is asymptotically Airy distributed. It is from these and similar results, supplemented by extensive computations based on transfer-matrix methods, that Guttmann and the Melbourne school have been led to conjecturing that the limit area of self-avoiding polygons (closed walks) in the plane is Airy (see our comments on p. 365). VII.57. Path length in increasing trees. For binary increasing trees, the analysis of path length reduces to that of the functional equation, Z z F(z, q) = 1 + F(qt, q)2 dt. 0

There exists a limit law, as first shown by Hennequin [328] using moment pumping, with alternative approaches due to R´egnier [505] and R¨osler [517]. This law is important in computer science, since it describes the number of comparisons used by the Quicksort algorithm and involved in the construction of a binary search tree. The mean is 2n log n + O(n), the variance is ∼ (7 − 4ζ (2))n 2 , and the moment of order r of the limit law is a polynomial form in zeta values ζ (2), . . . , ζ (r ). See [209] for recent news and references.

VII. 10.2. Families of generating functions. There is no logical obstacle to applying singularity analysis to a whole family of functions. In a way, this is similar to what was done in Chapter V when analysing longest runs in words (p. 308) and the height of general Catalan trees (p. 326), in the simpler case of meromorphic coefficient asymptotics. One then needs to develop suitable singular expansions together with companion error terms, a task that may be technically demanding when GFs are given by nonlinear functional relations or recurrences. We illustrate below the situation by an aperc¸u of the analysis of height in simple varieties of trees. Example VII.27. Height in simple varieties of trees. The recurrence (148)

y0 (z) = 0,

yh+1 (z) = 1 + zyh (z)2

is such that yh (z) is the OGF of binary trees of height less than h, with size measured by the number of binary nodes (Example III.28, p. 216). Each yh (z) is a polynomial, with deg(yh ) = 2h−1 − 1. Some technical difficulties are to be expected since the yh have no singularity at a finite distance, whereas their formal limit y(z) is the OGF of Catalan number, √ 1 y(z) = 1 − 1 − 4z , 2z which has a square-root singularity at z = 1/4. As a matter of fact, the sequence wh = zyh satisfies the recurrence wh+1 = z + wh2 , which was made famous by Mandelbrot’s studies and gives rise to amazing graphics [473]; see Figure VII.23 for a poor man’s version. When |z| ≤ r < 1/4, simple majorant series considerations show that the convergence yh (z) → y(z) is uniformly geometric. When z ≥ s > 1/4, it can be checked that the yh (z) grow doubly exponentially. What happens in-between, in a 1–domain, needs to be quantified. We do so following Flajolet, Gao, Odlyzko, and Richmond [230, 246].

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VII. APPLICATIONS OF SINGULARITY ANALYSIS

The grey level relative to a point z = x +i y in the diagram indicates the number of iterations necessary for the GFs yh (z) either to diverge to infinity (the outer, darker region) or to the finite limit y(z) (the inner region, corresponding to the Mandelbrot set, with the darker area around 0 corresponding to faster convergence). The cardioid-shaped region defined by |1 − ε(z)| ≤ 1 is a guaranteed region of convergence, beyond the circle |z| = 1/4. The determination of height reduces to finding what goes on near the cusp z = 1/4 of the cardioid. Figure VII.23. The GFs of binary trees of bounded height: speed of convergence.

Starting from the basic recurrence (148), we have y − yh+1 = z(y 2 − yh2 ) = z(y − yh )(2y − (y − yh )), which rewrites as (149)

eh+1 = (2zy)eh (1 − eh ),

where

eh (z) =

1 y(z) − yh (z) 2zy(z)

is proportional to the OGF of trees having height at least h. (The function x 7→ λx(1 − x), which is at the basis of the recurrence (149), is also known as the logistic map; its iterates, for real parameter values λ, give rise to a rich diversity of patterns.) First, let us examine what happens right at the singularity 1/4 and consider eh ≡ eh ( 14 ). The induced recurrence is e0 = 12 , whose solution decreases monotonically to 0 (argument: otherwise, there would need to be a fixed point in (0, 1)). This form resembles the familiar recurrence associated with the solution by iteration of a fixed-point equation ℓ = f (ℓ), but here it corresponds to an “indifferent” fixed-point, f ′ (ℓ) = 1, which precludes the usual geometric convergence. A classical trick of iteration theory, found in de Bruijn’s book [143, §8.4], neatly solves the problem. Consider instead the quantities f h := 1/eh , which satisfy the induced recurrence eh+1 = eh (1 − eh ),

(150)

with

1 1 + 2 ··· , with fh fh This suggests that f h ∼ h. Indeed, by a terminating form of (151),

(151)

f h+1 =

fh

1 − f h−1

(152) f h+1 = f h + 1 +

≡ fh + 1 +

f h−2 1 + , f h 1 − f −1 h

i.e.,

f h+1 = h + 2 +

h X

j=0

f 0 = 2.

f j−1 +

h X

f j−2

−1 j=0 1 − f j

,

one can derive properties of the sequence ( f h ) by “bootstrapping”: the fact that f h > h implies that the first sum in (152) is O(log h), while the second one is O(1); then, another round serves

VII. 10. SINGULARITY ANALYSIS AND PROBABILITY DISTRIBUTIONS

to refine the estimates, so that, for some C: f h = h + log h + C + O

log h h

537

,

and the behaviour of eh = 1/ f h is now well quantified. √ The analysis for z 6= 1/4 proceeds along similar lines. We set ε ≡ ε(z) := 1 − 4z and again abbreviate eh (z) as eh . Upon considering eh fh = (1 − ε)h and taking inverses, we obtain f h+1 = f h + (1 − ε)h +

(153)

Proceeding as before leads to the general approximation

f h eh2 1 − eh

.

√ ε(z)(1 − ε(z))h , ε(z) := 1 − 4z, 1 − (1 − ε(z))h proved to be valid for any fixed z ∈ (0, 1/4), as h → ∞. This approximation is compatible both with eh (1/4) ∼ 1/ h (derived earlier) and with the geometric convergence of yh (z) to y(z) valid for 0 < z < 1/4. With some additional work, it can be proved that (154) remains valid as z → 41 in a 1–domain and as h → ∞; see Figure VII.23. Obtaining the detailed conditions on (z, h), together with a uniform error term for (154), is the crux of the analysis in [247]. From this point on, we content ourselves with brief indications on subsequent developments. Given (154), one deduces24 that the GF of cumulated height satisfies X X ε(1 − ε)h 1 = 4 log + O(1), H (z) := 2y(z) eh (z) ∼ 4 h ε 1 − (1 − ε) (154)

eh (z) ∼

h≥0

h≥1

as z → 41 . Thus, by singularity analysis, one has

1 −→ [z n ]H (z) ∼ 2 · 4n /n, 1 − 4z √ which gives the expected height [z n ]H (z)/[z n ]y(z) of a binary tree of size n as ∼ 2 π n. Moments of higher order can be similarly analysed. It is of interest to note that the GFs that surface explicitly in the analysis of height in general Catalan trees (eventually due to the continued fraction structure and the implied linear recurrences) appear here as analytic approximations in suitable regions of the complex plane. A precise form of the approximation (154) can also be subjected to singularity analysis, to the effect that the same Theta law expresses in the asymptotic limit the distribution of height in binary trees. Finally, the technique can be extended to all simple varieties of trees satisfying the smooth inverse-function schema (Theorem VII.2, p. 453). In summary, we have the following proposition [230, 246]. Proposition VII.16. Let Y be a simple variety of trees satisfying the conditions of Theorem VII.2, with φ the basic tree constructor and τ the root of the characteristic equation φ(τ ) − τ φ(τ ) = 0. Let χ denote tree height. Then the r th moment of height satisfies H (z) ∼ 2 log

EYn [χ r ] ∼ r (r − 1)Ŵ(r/2)ζ (r )ξ r nr/2 ,

ξ :=

2φ ′ (τ )2 . φ(τ )φ ′′ (τ )

24 In order to obtain the logarithmic approximation of H (z), one can for instance appeal to Mellin transform techniques in a way parallel to the analysis of general Catalan trees (p. 326): set 1 − ε(z) = e−t .

538

VII. APPLICATIONS OF SINGULARITY ANALYSIS

√ The normalized height χ / ξ n converges to a Theta law, both in distribution and in the sense of a local limit law. (The Theta distribution is defined in (67), p. 328; Chapter IX develops the notions of convergence in law and of local limits much further.) In particular the expected height in general Catalan trees [145], binary trees, unary–binary trees, pruned t–ary trees, and Cayley trees [507], is found to be, respectively, asymptotic to p √ √ √ √ π n, 2 π n, 3π n, 2π t/(t − 1), 2π n, and a pleasant universality phenomenon manifests itself in the height of simple trees. A somewhat related analysis of a polynomial iteration in the vicinity of a singularity yields the asymptotic number of balanced trees (Note IV.49, p. 283). . . . . . . . . . . . . . . . . . . . . . . . . . .

VII. 11. Perspective The theorems in this chapter demonstrate the central rˆole of the singularity analysis theory developed in Chapter VI, this in a way that parallels what Chapter V did for Chapter IV with meromorphic function analysis. Exploiting properties of complex functions to develop coefficient asymptotics for abstract schemas helps us solve whole collections of combinatorial constructions at once. Within the context of analytic combinatorics, the results in this chapter have broad reach, and bring us closer to our ideal of a theory covering full analysis of combinatorial objects of any “reasonable” description. Analytic side conditions defining schemas often play a significant rˆole. Adding in this chapter the mathematical support for handling set constructions (with the exp–log schema) and context-free constructions (with coefficient asymptotics of algebraic functions) to the support developed in Chapter V to handle the sequence construction (with the supercritical sequence schema) and regular constructions (with coefficient asymptotics of rational functions) gives us general methods encompassing a broad swathe of combinatorial analysis, with a great many applications (Figure VII.24). Together, the methods covered in Chapter V, this chapter, and, next, Chapter VIII (relative to the saddle-point method) apply to virtually all of the generating functions derived in Part A of this book by means of the symbolic techniques defined there. The S EQ construction and regular specifications lead to poles; the S ET construction leads to algebraic singularities (in the case of logarithmic generators discussed here) or to essential singularities (in most of the remaining cases discussed in Chapter VIII); recursive (context-free) constructions lead to square-root singularities. The surprising end result is that the asymptotic counting sequences from all of these generating functions have one of just a few functional forms. This universality means that comparisons of methods, finding optimal values of parameters, and many other outgrowths of analysis can be very effective in practical situations. Indeed, because of the nature of the asymptotic forms, the results are often extremely accurate, as we have seen repeatedly in this book. The general theory of coefficient asymptotics based on singularities has many applications outside of analytic combinatorics (see the notes below). The broad reach of the theory provides strong indications that universal laws hold for many combinatorial constructions and schemas yet to be discovered.

VII. 11. PERSPECTIVE

Combinatorial Type

539

coeff. asymptotics (subexp. term)

Rooted maps

n −5/2

§VII. 8.2

Unrooted trees

n −5/2

§VII. 5

Rooted trees

n −3/2

§VII. 3, §VII. 4

Excursions

n −3/2

§VII. 8.1

Bridges

n −1/2

§VII. 8.1

Mappings

n −1/2

§VII. 3.3

Exp-log sets

n κ−1

§VII. 2

Increasing d–ary trees

Analytic form

n −(d−2)/(d−1) singularity type

§VII. 9.2

coeff. asymptotics

Positive irred. (polynomial syst.)

Z 1/2

ζ −n n −3/2

§VII. 6

General algebraic

Z p/q

ζ −n n − p/q−1

§VII. 7

Z θ (log Z )ℓ

ζ −n n −θ−1 (log n)ℓ

Regular singularity (ODE)

§VII. 9.1

Figure VII.24. A collection of universality laws summarized by the subexponential factors involved in the asymptotics of counting sequences (top). A summary of the main singularity types and asymptotic coefficient forms of this chapter (bottom).

Bibliographic notes. The exp–log schema, like its companion, the supercritical-sequence schema, illustrates the level of generality that can be attained by singularity analysis techniques. Refinements of the results we have given can be found in the book by Arratia, Barbour, and Tavar´e [20], which develops a stochastic process approach to these questions; see also [19] by the same authors for an accessible introduction. The rest of the chapter deals in an essential manner with recursively defined structures. As noted repeatedly in the course of this chapter, recursion is conducive to square-root singularity and universal behaviours of the form n −3/2 . Simple varieties of trees have been introduced in an important paper of Meir and Moon [435], that bases itself on methods developed earlier by P´olya [488, 491] and Otter [466]. One of the merits of [435] is to demonstrate that a high level of generality is attainable when discussing properties of trees. A similar treatment can be inflicted more generally to recursively defined structures when their generating functions satisfy an implicit equation. In this way, non-plane unlabelled trees are shown to exhibit properties very similar to their plane counterparts. It is of interest to note that some of the enumerative questions in this area had been initially motivated by problems of theoretical chemistry: see the colourful account of Cayley and Sylvester’s works in [67], the reference books by Harary and Palmer [319] and Finch [211], as well as P´olya’s original studies [488, 491]. Algebraic functions are the modern counterpart of the study of curves by classical Greek mathematicians. They are either approached by algebraic methods (this is the core of algebraic geometry) or by transcendental methods. For our purposes, however, only rudiments of the theory of curves are needed. For this, there exist several excellent introductory books, of which

540

VII. APPLICATIONS OF SINGULARITY ANALYSIS

we recommend the ones by Abhyankar [2], Fulton [273], and Kirwan [365]. On the algebraic side, we have aimed at providing an introduction to algebraic functions that requires minimal apparatus. At the same time the emphasis has been put somewhat on algorithmic aspects, since most algebraic models are nowadays likely to be treated with the help of computer algebra. As regards symbolic computational aspects, we recommend the treatise by von zur Gathen and Gerhard [599] for background, while polynomial systems are excellently reviewed in the book by Cox, Little, and O’Shea [135]. In the combinatorial domain, algebraic functions have been used early: in Euler and Segner’s enumeration of triangulations (1753) as well as in Schr¨oder’s famous “Vier combinatorische Probleme” described by Stanley in [554, p. 177]. A major advance was the realization by Chomsky and Sch¨utzenberger that algebraic functions are the “exact” counterpart of contextfree grammars and languages (see their historic paper [119]). A masterful summary of the early theory appears in the proceedings edited by Berstel [54] while a modern and precise presentation forms the subject of Chapter 6 of Stanley’s book [554]. On the analytic asymptotic side, many researchers have long been aware of the power of Puiseux expansions in conjunction with some version of singularity analysis (often in the form of the Darboux–P´olya method: see [491] based on P´olya’s classic paper [488] of 1937). However, there appeared to be difficulties in coping with the fully general problem of algebraic coefficient asymptotics [102, 440]. We believe that Section VII. 7 sketches the first complete theory (though most ingredients are of folklore knowledge). In the case of positive systems, the “Drmota–Lalley–Woods” theorem is the key to most problems encountered in practice—its importance should be clear from the developments of Section VII. 6. The applicability of algebraic function theory to context-free languages has been known for some time (e.g., [220]). Our presentation of one-dimensional walks of a general type follows articles by Lalley [396] and Banderier and Flajolet [27], which can be regarded as the analytic pendant of algebraic studies by Gessel [286, 287]. The kernel method has its origins in problems of queueing theory and random walks [202, 203] and is further explored in an article by Bousquet-M´elou and Petkovˇsek [86]. The algebraic treatment of random maps by the quadratic method is due to brilliant studies of Tutte in the 1960s: see for instance his census [579] and the account in the book by Jackson and Goulden [303]. A combinatorial–analytic treatment of multiconnectivity issues is given in [28], where the possibility of treating in a unified manner about a dozen families of maps appears clearly. Regarding differential equations, an early (and at the time surprising) occurrence in an asymptotic expansion of terms of the form n α , with α an algebraic number, is found in the study [252], dedicated to multidimensional search trees. The asymptotic analysis of coefficients of solutions to linear differential equations can also, in principle, be approached from the recurrences that these coefficients satisfy. Wimp and Zeilberger [611] propose an interesting approach based on results by George Birkhoff and his school (e.g., [70]), which are relative to difference equations in the complex plane. There are, however, some doubts among specialists regarding the completeness of Birkhoff’s programme (see our discussion in Section VIII. 7, p. 581). By contrast, the (easier) singularity theory of linear ODEs is well established, and, as we showed in this chapter, it is possible—in the regular singular case at least—to base a sound method for asymptotic coefficient extraction on it.

VIII

Saddle-point Asymptotics Like a lazy hiker, the path crosses the ridge at a low point; but unlike the hiker, the best path takes the steepest ascent to the ridge. [· · · ] The integral will then be concentrated in a small interval. — DANIEL G REENE AND D ONALD K NUTH [310, sec. 4.3.3]

VIII. 1. Landscapes of analytic functions and saddle-points VIII. 2. Saddle-point bounds VIII. 3. Overview of the saddle-point method VIII. 4. Three combinatorial examples VIII. 5. Admissibility VIII. 6. Integer partitions VIII. 7. Saddle-points and linear differential equations. VIII. 8. Large powers VIII. 9. Saddle-points and probability distributions VIII. 10. Multiple saddle-points VIII. 11. Perspective

543 546 551 558 564 574 581 585 594 600 606

A saddle-point of a surface is a point reminiscent of the inner part of a saddle or of a geographical pass between two mountains. If the surface represents the modulus of an analytic function, saddle-points are simply determined as the zeros of the derivative of the function. In order to estimate complex integrals of an analytic function, it is often a good strategy to adopt as contour of integration a curve that “crosses” one or several of the saddle-points of the integrand. When applied to integrals depending on a large parameter, this strategy provides in many cases accurate asymptotic information. In this book, we are primarily concerned with Cauchy integrals expressing coefficients of large index of generating functions. The implementation of the method is then fairly simple, since integration can be performed along a circle centred at the origin. Precisely, the principle of the saddle-point method for the estimation of contour integrals is to choose a path crossing a saddle-point, then estimate the integrand locally near this saddle-point (where the modulus of the integrand achieves its maximum on the contour), and deduce, by local approximations and termwise integration, an asymptotic expansion of the integral itself. Some sort of “localization” or “concentration” property is required to ensure that the contribution near the saddle-point captures the essential part of the integral. A simplified form of the method provides what are known as saddle-point bounds—these useful and technically simple upper bounds are obtained by applying trivial bounds to an integral relative to a saddle-point path. In 541

542

VIII. SADDLE-POINT ASYMPTOTICS

many cases, the saddle-point method can furthermore provide complete asymptotic expansions. In the context of analytic combinatorics, the method is applicable to Cauchy coefficient integrals, in the case of rapidly varying functions: typical instances are entire functions as well as functions with singularities at a finite distance that exhibit some form of exponential growth. Saddle-point analysis then complements singularity analysis whose scope is essentially the category of functions having only moderate (i.e., polynomial) growth at their singularities. The saddle-point method is also a method of choice for the analysis of coefficients of large powers of some fixed function and, in this context, it paves the way to the study of multivariate asymptotics and limiting Gaussian distributions developed in the next chapter. Applications are given here to Stirling’s formula, as well as the asymptotics of the central binomial coefficients, the involution numbers and the Bell numbers associated to set partitions. The asymptotic enumeration of integer partitions is one of the jewels of classical analysis and we provide an introduction to this rich topic where saddlepoints lead to effective estimates of an amazingly good quality. Other combinatorial applications include balls-in-bins models and capacity, the number of increasing subsequences in permutations, and blocks in set partitions. The counting of acyclic graphs (equivalently forests of unrooted trees), finally takes us beyond the basic paradigm of simple saddle-points by making use of multiple saddle-points, also known as “monkey saddles”. Plan of this chapter. First, we examine the surface determined by the modulus of an analytic function and give, in Section VIII. 1, a classification of points into three kinds: ordinary points, zeros, and saddle-points. Next we develop general purpose saddle-point bounds in Section VIII. 2, which also serves to discuss the properties of saddle-point crossing paths. The saddle-point method per se is presented in Section VIII. 3, both in its most general form and in the way it specializes to Cauchy coefficient integrals. Section VIII. 4 then discusses three examples, involutions, set partitions, and fragmented permutations, which help us get further familiarized with the method. We next jump to a new level of generality and introduce in Section VIII. 5 the abstract concept of admissibility—this approach has the merit of providing easily testable conditions, while opening the possibility of determining broad classes of functions to which the saddle-point method is applicable. In particular, many combinatorial types whose leading construction is a S ET operation are seen to be “automatically” amenable to saddle-point analysis. The case of integer partitions, which is technically more advanced, is treated in a separate section, Section VIII. 6. The saddle-method is also instrumental in analysing coefficients of many generating functions implicitly defined by differential equations, including holonomic functions: see Section VIII. 7. Next, the framework of “large powers”, developed in Section VIII. 8 constitutes a combinatorial counterpart of the central limit theorem of probability theory, and as such it provides a bridge to the study of limit distributions to be treated systematically in Chapter IX. Other applications to discrete probability distributions are examined in Section VIII. 9. Finally, Section VIII. 10 serves as a brief introduction to the rich subject of multiple saddle-points and coalescence.

VIII. 1. LANDSCAPES OF ANALYTIC FUNCTIONS AND SADDLE-POINTS

543

VIII. 1. Landscapes of analytic functions and saddle-points This section introduces a well-known classification of points on the surface representing the modulus of an analytic function. In particular, as we are going to see, saddle-points, which are determined by roots of the function’s derivative, are associated with a simple geometric property that gives them their name. Consider any function f (z) analytic for z ∈ , where is some domain of C. Its modulus | f (x +i y)| can be regarded as a function of the two real quantities, x = ℜ(z) and y = ℑ(z). As such, it can be represented as a surface in three-dimensional space. This surface is smooth (analytic functions are infinitely differentiable), but far from being arbitrary. Let z 0 be an interior point of . The local shape of the surface | f (z)| for z near z 0 depends on which of the initial elements in the sequence f (z 0 ), f ′ (z 0 ), f ′′ (z 0 ), . . ., vanish. As we are going to see, its points can be of only one of three types: ordinary points (the generic case), zeros, and saddle-points; see Figure VIII.1. The classification of points is conveniently obtained by considering polar coordinates, writing z = z 0 + r eiθ , with r small. An ordinary point is such that f (z 0 ) 6= 0, f ′ (z 0 ) 6= 0. This is the generic situation as analytic functions have only isolated zeros. In that case, one has, for small r > 0, (1) | f (z)| = f (z 0 ) + r eiθ f ′ (z 0 ) + O(r 2 ) = | f (z 0 )| 1 + λr ei(θ+φ) + O(r 2 ) ,

where we have set f ′ (z 0 )/ f (z 0 ) = λeiφ , with λ > 0. The modulus then satisfies | f (z)| = | f (z 0 )| 1 + λr cos(θ + φ) + O(r 2 ) .

Thus, for r kept small enough and fixed, as θ varies, | f (z)| is maximum when θ = −φ (where it is ∼ | f (z 0 )|(1 + λr )), and minimum when θ = −φ + π (where it is ∼ | f (z 0 )(1 − λr )). When θ = −φ ± π2 , one has | f (z)| = | f (z 0 )| + o(r ), which means that | f (z)| is essentially constant. This is easily interpreted: the line θ ≡ −φ (mod π ) is (locally) a steepest descent line; the perpendicular line θ ≡ −φ + π2 (mod π ) is locally a level line. In particular, near an ordinary point, the surface | f (z)| has neither a minimum nor a maximum. In figurative terms, this is like standing on the flank of a mountain. A zero is by definition a point such that f (z 0 ) = 0. In this case, the function | f (z)| attains its minimum value 0 at z 0 . Locally, to first order, one has | f (z)| ∼ | f ′ (z 0 )|r for a simple zero and | f (z)| = O(r m ) or a zero of order m. A zero is thus like a sink or the bottom of a lake, save that, in the landscape of an analytic function, all lakes are at sea level. A saddle-point is a point such that f (z 0 ) 6= 0, f ′ (z 0 ) = 0; it thus corresponds to a zero of the derivative, when the function itself does not vanish. It is said to be a simple saddle-point if furthermore f ′′ (z 0 ) 6= 0. In that case, a calculation similar to (1), (2) 1 | f (z)| = f (z 0 ) + r 2 e2iθ f ′′ (z 0 ) + O(r 3 ) = | f (z 0 )| 1 + λr 2 ei(2θ+φ) + O(r 3 ) , 2

544

VIII. SADDLE-POINT ASYMPTOTICS

Ordinary point f (z 0 ) 6= 0, f ′ (z 0 ) 6= 0

1111111111111111 0000000000000000 0000000000000000 1111111111111111 0000000000000000 1111111111111111 0000000000000000 1111111111111111 0000000000000000 1111111111111111 0000000000000000 1111111111111111 0000000000000000 1111111111111111 0000000000000000 1111111111111111 0000000000000000 1111111111111111 0000000000000000 1111111111111111 0000000000000000 1111111111111111 0000000000000000 1111111111111111 0000000000000000 1111111111111111 0000000000000000 1111111111111111 0000000000000000 1111111111111111 0000000000000000 1111111111111111 0000000000000000 1111111111111111

Zero f (z 0 ) = 0

Saddle-point f (z 0 ) 6= 0, f ′ (z 0 ) = 0 f ′′ (z 0 ) 6= 0 000000000000000 111111111111111 111111111111111 000000000000000 000000000000000 111111111111111 000000000000000 111111111111111 000000000000000 111111111111111 000000000000000 111111111111111 000000000000000 111111111111111 000000000000000 111111111111111 000000000000000 111111111111111 000000000000000 111111111111111 0000000000000000 1111111111111111 0000000000000000 1111111111111111 0000000000000000 1111111111111111 0000000000000000 1111111111111111 0000000000000000 1111111111111111 0000000000000000 1111111111111111 0000000000000000 1111111111111111 0000000000000000 1111111111111111 0000000000000000 1111111111111111 0000000000000000 1111111111111111

Figure VIII.1. The different types of points on a surface | f (z)|: an ordinary point, a zero, a simple saddle-point. Top: a diagram showing the local structure of level curves (in solid lines), steepest descent lines (dashed with arrows pointing towards the direction of increase) and regions (hashed) where the surface lies below the reference value | f (z 0 )|. Bottom: the function f (z) = cosh z and the local shape of | f (z)| near an ordinary point (iπ/4), a zero (iπ/2), and a saddle-point (0), with level lines shown on the surfaces.

f ′′ (z 0 )/ f (z 0 ) = λeiφ , shows that the modulus satisfies | f (z)| = | f (z 0 )| 1 + λr 2 cos(2θ + φ) + O(r 3 ) .

where we have set

1 2

Thus, starting at the direction θ = −φ/2 and turning around z 0 , the following sequence of events regarding the modulus | f (z)| = | f (r eiθ )| is observed: it is maximal (θ = −φ/2), stationary (θ = −φ/2 + π/4), minimal (θ = −φ/2 + π/2), stationary, (θ = −φ/2 + 3π /4), maximal again (θ = −φ/2 + π ), and so on. The pattern, symbolically “+ = − =”, repeats itself twice. This is superficially similar to an ordinary point, save for the important fact that changes are observed at twice the angular speed. Accordingly, the shape of the surface looks quite different; it is like the central part of a saddle. Two level curves cross at a right angle: one steepest descent line (away from the saddle-point) is perpendicular to another steepest descent line (towards the saddlepoint). In a mountain landscape, this is thus much like a pass between two mountains. The two regions on each side corresponding to points with an altitude below a simple saddle-point are often referred to as “valleys”.

VIII. 1. LANDSCAPES OF ANALYTIC FUNCTIONS AND SADDLE-POINTS

545

1.0

0.5

y 0.0

−0.5

−1.0

−1.5

−1.0

−0.5

0.0

x

Figure VIII.2. The “tripod”: two views of |1 + z + z 2 + z 3 | as function of x = ℜ(z), y = ℑ(z): (left) the modulus as a surface in R3 ; (right) the projection of level lines on the z-plane.

Generally, a multiple saddle-point has multiplicity p if f (z 0 ) 6= 0 and all derivatives f ′ (z 0 ), . . . , f ( p) (z 0 ) are equal to zero while f ( p+1) (z 0 ) 6= 0. In that case, the basic pattern “+ = − =” repeats itself p + 1 times. For instance, from a double saddle-point ( p = 2), three roads go down to three different valleys separated by the flanks of three mountains. A double saddle-point is also called a “monkey saddle” since it can be visualized as a saddle having places for the legs and the tail: see Figure VIII.12 (p. 602) and Figure VIII.14 (p. 605). Theorem VIII.1 (Classification of points on modulus surfaces). A surface | f (z)| attached to the modulus of a function analytic over an open set has points of only three possible types: (i) ordinary points, (ii) zeros, (iii) saddle-points. Under projection on the complex plane, a simple saddle-point is locally the common apex of two curvilinear sectors with angle π/2, referred to as “valleys”, where the modulus of the function is smaller than at the saddle-point. As a consequence, the surface defined by the modulus of an analytic function has no maximum: this property is known as the Maximum Modulus Principle. It has no minimum either, apart from zeros. It is therefore a peakless landscape, in de Bruijn’s words [143]. Accordingly, for a meromorphic function, peaks are at ∞ and minima are at 0, the other points being either ordinary points or isolated saddle-points. Example VIII.1. The tripod: a cubic polynomial. An idea of the typical shape of the surface representing the modulus of an analytic function can be obtained by examining Figure VIII.2 relative to the third degree polynomial f (z) = 1 + z + z 2 + z 3 . Since f (z) = (1 − z 4 )/(1 − z), the zeros are at −1, i, −i. There are saddle-points at the zeros of the derivative f ′ (z) = 1 + 2z + 3z 2 , that is, at the points

546

VIII. SADDLE-POINT ASYMPTOTICS

1 1 i√ i√ ζ := − + 2, ζ ′ := − − 2. 3 3 3 3 The diagram below summarizes the position of these “interesting” points: i (zero) √ ζ = − 13 + 3i 2 (saddle-point)

ζ

(3)

−1 (zero)

(0) √ ζ ′ = − 31 − 3i 2 (saddle-point)

ζ′

−i (zero)

The three zeros are especially noticeable on Figure VIII.2 (left), where they appear at the end of the three “legs”. The two saddle-points are visible on Figure VIII.2 (right) as intersection points of level curves. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

VIII.1. The Fundamental Theorem of Algebra. This theorem asserts that a non-constant polynomial has at least one root, hence n roots if its degree is n (Note IV.38, p. 270). Let P(z) = 1 + a1 z + · · · an z n be a polynomial of degree n. Consider f (z) = 1/P(z). By basic analysis, one can take R sufficiently large, so that on |z| = R, one has | f (z)| < 12 . Assume a contrario that P(z) has no zero. Then, f (z) which is analytic in |z| ≤ R should attain its maximum at an interior point (since f (0) = 1), so that a contradiction has been reached.

VIII.2. Saddle-points of polynomials and the convex hull of zeros. Let P be a polynomial and H the convex hull of its zeros. Then any root of P ′ (z) lies in H. (Proof: assume distinct zeros and consider X P ′ (z) 1 φ(z) := = . P(z) z−α α : P(α)=0

If z lies outside H, then z “sees” all zeros α in a half-plane, this by elementary geometry. By projection on the normal to the half-plane boundary, it is found that, for some θ , one has ℜ(eiθ φ(z)) < 0, so that P ′ (z) 6= 0.)

VIII. 2. Saddle-point bounds Saddle-point analysis is a general method suited to the estimation of integrals of analytic functions F(z), Z B (4) I = F(z) dz, A

where F(z) ≡ Fn (z) involves some large parameter n. The method is instrumental when the integrand F is subject to rather violent variations, typically when there occurs in it some exponential or some fixed function raised to a large power n → +∞. In this section, we discuss some of the global properties of saddle-point contours, then particularize the discussion to Cauchy coefficient integrals. General saddle-point bounds, which are easy to derive, result from simple geometric considerations (a preliminary discussion appears in Chapter IV, p. 246.).

VIII. 2. SADDLE-POINT BOUNDS

547

Starting from the general form (4), we let C be a contour joining A and B and taken in a domain of the complex plane where F(z) is analytic. By standard inequalities, we have (5)

|I | ≤ ||C|| · sup |F(z)|, z∈C

with ||C|| representing the length of C. This is the common trivial bound from integration theory applied to a fixed contour C. For an analytic integrand F with A and B inside the domain of analyticity, there is an infinite class P of acceptable paths to choose from, all in the analyticity domain of F. Thus, by optimizing the bound (5), we may write " # (6)

|I | ≤ inf ||C|| · sup |F(z)| , C ∈P

z∈C

where the infimum is taken over all paths C ∈ P. Broadly speaking, a bound of this type is called a saddle-point bound1. The length factor ||C|| usually turns out to be unimportant for asymptotic bounding purposes—this is, for instance, the case when paths remain in finite regions of the complex plane. If there happens to be a path C from A to B such that no point is at an altitude higher than sup(|F(A)|, |F(B)|), then a simple bound results, namely, |I | ≤ ||C||·sup(|F(A)|, |F(B)|): this is in a sense the uninteresting case. The common situation, typical of Cauchy coefficient integrals of combinatorics, is that paths have to go at some higher altitude than the end points. A path C that traverses a saddle-point by connecting two points at a lower altitude on the surface |F(z)| and by following two steepest descent lines across the saddle-point is clearly a local minimum for the path functional 8(C) = sup |F(z)|, z∈C

as neighbouring paths must possess a higher maximum. Such a path is called a saddlepoint path or steepest descent path. Then, the search for a path minimizing " # inf sup |F(z)| C

z∈C

(a simplification of (6) to its essential feature) naturally suggests considering saddlepoints and saddle-point paths. This leads to the variant of (6), (7)

|I | ≤ ||C0 || · sup |F(z)|, z∈C0

C0 minimizes sup |F(z)|, z∈C

also referred to as a saddle-point bound. We can summarize this stage of the discussion by a simple generic statement. Theorem VIII.2 (General saddle-point bounds). Let F(z) be a function analytic in R a domain . Consider the class of integral γ F(z) dz where the contour γ connects 1 Notice additionally that the optimization problem need not be solved exactly, as any approximate

solution to (6) still furnishes a valid upper bound because of the universal character of the trivial bound (5).

548

VIII. SADDLE-POINT ASYMPTOTICS

two points A, B and is constrained to a class P of allowable paths in (e.g., those that encircle 0). Then one has the saddle-point bound2: Z F(z) dz ≤ ||C0 || · sup |F(z)|, γ z∈C0 (8) where C0 is any path that minimizes sup |F(z)|. z∈C

If A and B lie in opposite valleys of a saddle-point z 0 , then the minimization problem is solved by saddle-point paths C0 made of arcs connecting A to B through z 0 . In that case, one has Z B F(z) dz ≤ ||C0 || · |F(z 0 )| , F ′ (z 0 ) = 0. A

Borrowing a metaphor of de Bruijn [143], the situation may be described as follows. Estimating a path integral is like estimating the difference of altitude between two villages in a mountain range. If the two villages are in different valleys, the best strategy (this is what road networks often do) consists in following paths that cross boundaries between valleys at passes, i.e., through saddle-points. The statement of Theorem VIII.2 does no fix all details of the contour, when there are several saddle-points “separating” A and B—the problem is like finding the most economical route across a whole mountain range. But at least it suggests the construction of a composite contour made of connected arcs crossing saddle-points from valley to valley. Furthermore, in cases of combinatorial interest, some strong positivity is present and the selection of the suitable saddle-point contour is normally greatly simplified, as we explain next.

VIII.3. An integral of powers. Consider the polynomial P(z) = 1 + z + z 2 + z 3 of Example VIII.1. Define the line integral Z +i In = P(z)n dz. −1

On the segment connecting the end points, the maximum of |P(z)| is 0.63831, giving√the weak trivial bound In = O(0.63831n ). In contrast, there is a saddle-point at ζ = − 13 + 3i 2 where |P(ζ )| = 13 , resulting in the bound n 1 . |In | ≤ λ , λ := |ζ + 1| + |i − ζ | = 1.44141, 3

as follows from adopting a contour made of two segments connecting −1 to i through ζ . Discuss R ′ further the bounds on αα , when (α, α ′ ) ranges over all pairs of roots of P.

Saddle-point bounds for Cauchy coefficient integrals. Saddle-point bounds can be applied to Cauchy coefficient integrals, I dz 1 G(z) n+1 , (9) gn ≡ [z n ]G(z) = 2iπ z

2The form given by (8) is in principle weaker than the form (6), since it does not take into account the

length of the contour itself, but the difference is immaterial in all our asymptotic problems.

VIII. 2. SADDLE-POINT BOUNDS

549

−n−1 . for which we can avail H ourselves of the previous discussion, with F(z) = G(z)z In (9) the symbol indicates that the allowable paths are constrained to encircle the origin (the domain of definition of the integrand is a subset of C \ {0}; the points A, B can then be seen as coinciding and taken somewhere along the negative real line; equivalently, one may take A = −aeiǫ and B = −ae−iǫ , for a > 0 and ǫ → 0). In the particular case where G(z) is a function with non-negative coefficients, a simple condition guarantees the existence of a saddle-point on the positive real axis. Indeed, assume that G(z), which has radius of convergence R with 0 < R ≤ +∞, satisfies G(z) → +∞ as z → R − along the real axis and G(z) not a polynomial. Then the integrand F(z) = G(z)z −n−1 satisfies F(0+ ) = F(R − ) = +∞. This means that there exists at least one local minimum of F over (0, R), hence, at least one value ζ ∈ (0, R) where the derivative F ′ vanishes. (Actually, there can be only one such point; see Note VIII.4, p. 550.) Since ζ corresponds to a local minimum of F, we have additionally F ′′ (ζ ) > 0, so that the saddle-point is crossed transversally by a circle of radius ζ . Thus, the saddle-point bound, specialized to circles centred at the origin, yields the following corollary. Corollary VIII.1 (Saddle-point bounds for generating functions). Let G(z), not a polynomial, be analytic at 0 with non-negative coefficients and radius of convergence R ≤ +∞. Assume that G(R − ) = +∞. Then one has

(10)

[z n ]G(z) ≤

G(ζ ) , ζn

with ζ ∈ (0, R) the unique root of ζ

G ′ (ζ ) = n + 1. G(ζ )

Proof. The saddle-point is the point where the derivative of the integrand is 0. Therefore, we consider (G(z)z −n−1 )′ = 0, or G ′ (z)z −n−1 − (n + 1)G(z)z −n−2 = 0, or

G ′ (z) = n + 1. G(z) We refer to this as the saddle-point equation and use ζ to denote its positive root. The perimeter of the circle is 2π ζ , so that the inequality [z n ]G(z) ≤ G(ζ )/ζ n follows. z

Corollary VIII.1 is equivalent to Proposition IV.1, p. 246, on which it sheds a new light, while paving the way to the full saddle-point method to be developed in the next section.

We examine below two particular cases related to the central binomial and the inverse factorial. The corresponding landscapes of Figure VIII.3, which bear a surprising resemblance to one another, are, by the previous discussion, instances of a general pattern for functions with non-negative coefficients. It is seen on these two examples that the saddle-point bounds already catch the proper exponential growths, being off only by a factor of O(n −1/2 ). Example VIII.2. Saddle-point bounds for central binomials and inverse factorials. Consider the two contour integrals around the origin I I dz dz 1 1 (1 + z)2n n+1 , e z n+1 , (11) Jn = Kn = 2iπ 2iπ z z whose values are otherwise known, by virtue of Cauchy’s coefficient formula, to be Jn = 2n n and K n = 1/n!. In that case, one can think of the end points A and B as coinciding and taken

550

VIII. SADDLE-POINT ASYMPTOTICS

Figure VIII.3. The modulus of the integrands of Jn (central binomials) and K n (inverse factorials) for n = 5 and the corresponding saddle-point contours. somewhat arbitrarily on the negative real axis, while the contour has to encircle the origin once and counter-clockwise. The saddle-point equations are, respectively, n+1 n+1 2n − = 0, 1− = 0, 1+z z z n+1 the corresponding saddle-points being ζ = and ζ ′ = n + 1. This provides the upper n−1 bounds !n 4n 2 2n 1 en+1 16 n ≤ (12) Jn = 4 , K = ≤ , ≤ n n 9 n! (n + 1)n n2 − 1 which are valid for all values n ≥ 2. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

VIII.4. Upward convexity of G(x)x −n . For G(z) having non-negative coefficients at the origin, the quantity G(x)x −n is upward convex for x > 0, so that the saddle-point equation for ζ can have at most one root. Indeed, the second derivative

d 2 G(x) x 2 G ′′ (x) − 2nx G ′ (x) + n(n + 1)G(x) , = dx2 xn x n+2 is positive for x > 0 since its numerator, X (n + 1 − k)(n − k)gk x k , gk := [z k ]G(z), (13)

k≥0

has only non-negative coefficients. (See Note IV.46, p. 280, for an alternative derivation.)

VIII.5. A minor optimization. The bounds of Equation (6), p. 547, which take the length of the contour into account, lead to estimates that closely resemble (10). Indeed, we have G ′ (z) G(b ζ) b ζ root of z [z n ]G(z) ≤ n , = n, b G(z) ζ

VIII. 3. OVERVIEW OF THE SADDLE-POINT METHOD

when optimization is carried out over circles centred at the origin.

551

VIII. 3. Overview of the saddle-point method Given a complex integral with a contour traversing a single saddle-point, the saddle-point corresponds locally to a maximum of the integrand along the path. It is then natural to expect that a small neighbourhood of the saddle-point may provide the dominant contribution to the integral. The saddle-point method is applicable precisely when this is the case and when this dominant contribution can be estimated by means of local expansions. The method then constitutes the complex analytic counterpart of the method of Laplace (Appendix B.6: Laplace’s method, p. 755) for the evaluation of real integrals depending on a large parameter, and we can regard it as being Saddle-point method = Choice of contour + Laplace’s method. Similar to its real-variable counterpart, the saddle-point method is a general strategy rather than a completely deterministic algorithm, since many choices are left open in the implementation of the method concerning details of the contour and choices of its splitting into pieces. To proceed, it is convenient to set F(z) = e f (z) and consider Z B (14) I = e f (z) dz, A

where f (z) ≡ f n (z), as F(z) ≡ Fn (z) in the previous section, involves some large parameter n. Following possibly some preparation based on Cauchy’s theorem, we may assume that the contour C connects two end points A and B lying in opposite valleys of the saddle-point ζ . The saddle-point equation is F ′ (ζ ) = 0, or equivalently since F = e f : f ′ (ζ ) = 0. The saddle-point method, of which a summary is given in Figure VIII.4, is based on a fundamental splitting of the integration contour. We decompose C = C (0) ∪ C (1) , where C (0) called the “central part” contains ζ (or passes very near to it) and C (1) is formed of the two remaining “tails”. This splitting has to be determined in each case in accordance with the growth of the integrand. The basic principle rests on two major conditions: the contributions of the two tails should be asymptotically negligible (condition SP1 ); in the central region, the quantity f (z) in the integrand should be asymptotically well approximated by a quadratic function (condition SP2 ). Under these conditions, the integral is asymptotically equivalent to an incomplete Gaussian integral. It then suffices to verify—this is condition SP3 , usually a minor a posteriori technical verification—that tails can be completed back, introducing only negligible error terms. By this sequence of steps, the original integral is asymptotically reduced to a complete Gaussian integral, which evaluates in closed form. Specifically, the three steps of the saddle-point method involve checking conditions expressed by Equations (15), (16), and (18) below.

552

VIII. SADDLE-POINT ASYMPTOTICS

Goal: Estimate

Z B A

F(z) dz, setting F = e f ; here, F ≡ Fn and f ≡ f n depend on a large

parameter n. — The end points A, B are assumed to lie in opposite valleys of the saddle-point. — A contour C through (or near) a simple saddle-point ζ , so that f ′ (ζ ) = 0, has been chosen. — The contour is split as C = C (0) ∪ C (1) . The following conditions are to be verified. R SP1 : Tails pruning. On the contour C (1) , the tails integral C (1) is negligible: Z Z F(z) dz = o F(z) dz . C (1)

C

SP2 : Central approximation. Along C (0) , a quadratic expansion,

1 ′′ f (ζ )(z − ζ )2 + O(ηn ), 2 is valid, with ηn → 0 as n → ∞, uniformly with respect to z ∈ C (0) . SP3 : Tails completion. The incomplete Gaussian integral resulting from SP2 , taken over the central range, is asymptotically equivalent to a complete Gaussian integral (with f ′′ (ζ ) = eiφ | f ′′ (ζ )| and ε = ±1 depending on orientation): s Z Z ∞ 1 ′′ 2 ′′ 2 2π e 2 f (ζ )(z−ζ ) dz ∼ εie−iφ/2 e−| f (ζ )|x /2 d x ≡ εie−iφ/2 ′′ (ζ )| . (0) | f C −∞ f (z) = f (ζ ) +

Result: Assuming SP1 , SP2 , and SP3 , one has, with ε = ±1 and arg( f ′′ (ζ )) = φ: Z B e f (ζ ) 1 e f (ζ ) = ±p . e f (z) dz ∼ εe−iφ/2 p 2iπ A 2π | f ′′ (ζ )| 2π f ′′ (ζ ) Figure VIII.4. A summary of the basic saddle-point method.

SP1 : Tails pruning. On the contour C (1) , the tail integral Z

(15)

C (1)

F(z) dz = o

Z

C

F(z) dz .

R

C (1)

is negligible:

This condition is usually established by proving that F(z) remains small enough (e.g., exponentially small in the scale of the problem) away from ζ , for z ∈ C (1) . SP2 : Central approximation. Along C (0) , a quadratic expansion, (16)

f (z) = f (ζ ) +

1 ′′ f (ζ )(z − ζ )2 + O(ηn ), 2

is valid, with ηn → 0 as n → ∞, uniformly for z ∈ C (0) . This guarantees that well-approximated by an incomplete Gaussian integral: Z Z 1 ′′ 2 (17) e f (z) dz ∼ e f (ζ ) e 2 f (ζ )(z−ζ ) dz. C (0)

C (0)

R

e f is

VIII. 3. OVERVIEW OF THE SADDLE-POINT METHOD

553

SP3 : Tails completion. The tails can be completed back, at the expense of asymptotically negligible terms, meaning that the incomplete Gaussian integral is asymptotically equivalent to a complete one (itself given by (12), p. 744), s Z ∞ Z 1 ′′ 2π 2 ′′ (ζ )|x 2 /2 f (ζ )(z−ζ ) −iφ/2 −| f −iφ/2 dz ∼ εie e d x ≡ εie (18) e2 . | f ′′ (ζ )| −∞ C (0)

where ε = ±1 is determined by the orientation of the original contour C, and f ′′ (ζ ) = eiφ | f ′′ (ζ )|. This last step deserves a word of explanation. Along a steepest descent curve across ζ , the quantity f ′′ (ζ )(z − ζ )2 is real and negative, as we saw when discussing saddle-point landscapes (p. 543). Indeed, with f ′′ (ζ ) = eiφ | f ′′ (ζ )|, one has arg(z −ζ ) ≡ −φ/2+ π2 (mod π ). Thus, the change of variables x = ±i(z −ζ )e−iφ/2 reduces the left side of (18) to an integral taken along (or close to) the real line3. The condition (18) then demands that this integral can be completed to a complete Gaussian integral, which itself evaluates in closed form. If these conditions are granted, one has the chain s Z Z Z 1 ′′ 2π 2 f (ζ )(z−ζ ) −iφ/2 f (ζ ) f f f (ζ ) dz ∼ ±ie e e dz ∼ e dz ∼ e e2 , ′′ (ζ )| (0) (0) | f C C C by virtue of Equations (15), (17), (18). In summary:

RB Theorem VIII.3 (Saddle-point Algorithm). Consider an integral A F(z) dz, where the integrand F = e f is an analytic function depending on a large parameter and A, B lie in opposite valleys across a saddle-point ζ , which is a root of the saddlepoint equation f ′ (ζ ) = 0 (or, equivalently, F ′ (ζ ) = 0). Assume that the contour C connecting A to B can be split into C = C (0) ∪ C (1) in such a way that the following conditions are satisfied: (i) tails are negligible, in the sense of Equation (15) of SP1 , (ii) a central approximation hold, in the sense of Equation (16) of SP2 , (iii) tails can be completed back, in the sense of Equation (18) of SP3 .

Then one has, with ε = ±1 reflecting orientation and φ = arg( f ′′ (ζ )): Z B e f (ζ ) e f (ζ ) 1 = ±p . e f (z) dz ∼ εe−iφ/2 p (19) 2iπ A 2π | f ′′ (ζ )| 2π f ′′ (ζ )

It can be verified at once that a blind application of the formula to the two integrals of Example VIII.2 produces the expected asymptotic estimates 4n 2n 1 1 ∼√ (20) Jn ≡ and Kn ≡ . ∼ √ n −n n n! πn n e 2π n The complete justification in the case of K n is given in Example VIII.3 below. The case of Jn is covered by the general theory of “large powers” of Section VIII. 8, p. 585. 3The sign in (18) is naturally well-defined, once the data A, B, and f are fixed: one possibility is to adopt the determination of φ/2 (mod π ) such that A and B are sent close to the negative and the positive real axis, respectively, after the final change of variables x = i(z − ζ )e−iφ/2 .

554

VIII. SADDLE-POINT ASYMPTOTICS

In order for the saddle-point method to work, conflicting requirements regarding the dimensioning of C (0) and C (1) must be satisfied. The tails pruning and tails completion conditions, SP1 and SP3 , force C (0) to be chosen large enough, so as to capture the main contribution to the integral; the central approximation condition SP2 requires C (0) to be small enough, to the effect that f (z) can be suitably reduced to its quadratic expansion. Usually, one has to take ||C (0) ||/||C|| → 0, and the following observation may help make the right choices. The error in the two-term expansion being likely given by the next term, which involves a third derivative, it is a good guess to dimension C (0) to be of length δ ≡ δ(n) chosen in such a way that (21)

f ′′ (ζ )δ 2 → ∞,

f ′′′ (ζ )δ 3 → 0,

so that both tail and central approximation conditions can be satisfied. We call this choice the saddle-point dimensioning heuristic. On another register, it often proves convenient to adopt integration paths that come close enough to the saddle-point but need not pass exactly through it. In the same vein, a steepest descent curve may be followed only approximately. Such choices will still lead to valid conclusions, as long as the conditions of Theorem VIII.3 are verified. (Note carefully that these conditions neither impose that the contour should pass strictly through the saddle-point, nor that a steepest descent curve should be exactly followed.) Saddle-point method for Cauchy coefficient integrals. For the purposes of analytic combinatorics, the general saddle-point method specializes. We are given a generating function G(z), assumed to be analytic at the origin and with non-negative coefficients, and seek an asymptotic form of the coefficients, given in integral form by Z 1 dz n [z ]G(z) = G(z) n+1 . 2iπ C z There, C encircles the origin, lies within the domain where G is analytic, and is positively oriented. This is a particular case of the general integral (14) considered earlier, with the integrand being F(z) = G(z)/z n+1 . The geometry of the problem is now simple, and, for reasons seen in the previous section, it suffices to consider as integration contour a circle centred at the origin and passing through (or very near) a saddle-point present on the positive real line. It is then natural to make use of polar coordinates and set z = r eiθ , where the radius r of the circle will be chosen equal to (or close to) the positive saddlepoint value. We thus need to estimate I Z dz r −n +π 1 n G(z) n+1 = G(r eiθ )e−niθ dθ. (22) [z ]G(z) = 2iπ 2π −π z Under the circumstances, the basic split of the contour C = C (0) ∪ C (1) involves a central part C (0) , which is an arc of the circle of radius r determined by |θ | ≤ θ0 for

VIII. 3. OVERVIEW OF THE SADDLE-POINT METHOD

555

some suitably chosen θ0 . On C (0) , a quadratic approximation should hold, according to SP2 [central approximation]. Set (23)

f (z) := log G(z) − n log z.

A natural possibility is to adopt for r the value that cancels f ′ (r ), (24)

r

G ′ (r ) = n, G(r )

which is a version of the saddle-point equation4 relative to polar coordinates. This grants us locally, a quadratic approximation without linear terms, with β(r ) a computable quantity (in terms of f (r ), f ′ (r ), f ′′ (r )), we have 1 f (r eiθ ) − f (r ) = − β(r )θ 2 + o(θ 3 ), 2 which is valid at least for fixed r (i.e., for fixed n), as θ → 0 The cutoff angle θ0 is to be chosen as a function of n (or, equivalently, r ) in accordance with the saddle-point heuristic (21). It then suffices to carry out a verification of the validity of the three conditions of the saddle-point method, SP1 , SP2 (for which a suitably uniform version of (25) needs to be developed), and SP3 of Theorem VIII.3, p. 553, adjusted to take into account polar coordinate notations. (25)

The example below details the main steps of the saddle-point analysis of the generating function of inverse factorials, based on the foregoing principles. Example VIII.3. Saddle-point analysis of the exponential and the inverse factorial I. The goal 1 = [z n ]e z , the starting point being is to estimate n! Z 1 dz Kn = e z n+1 , 2iπ |z|=r z where integration will be performed along a circle centred at the origin. The landscape of the modulus of the integrand has been already displayed in Figure VIII.3, p. 550—there is a saddlepoint of G(z)z −n−1 at ζ = n + 1 with an axis perpendicular to the real line. We thus expect an asymptotic estimate to derive from adopting a circle passing through the saddle-point, or about. We switch to polar coordinates, fix the choice of the radius r = n in accordance with (24), and set z = neiθ . The original integral becomes, in polar coordinates, Z +π iθ en 1 en e −1−iθ dθ, (26) Kn = n · n 2π −π where, for readability, we have taken out the factor G(r )/r n ≡ en /n n . Set h(θ) = eiθ − 1 − iθ . The function |eh(θ) | = ecos θ−1 is unimodal with its peak at θ = 0 and the same property holds for |enh(θ) |, representing the modulus of the integrand in (26), which gets more and more strongly peaked at θ = 0, as n → +∞; see Figure VIII.5. 4Equation (24) is almost the same as ζ G ′ (ζ )/G(ζ ) = n + 1 of (10), which defines the saddle-point in

z-coordinates. The (minor) difference is accounted for by the fact that saddle-points are sensitive to changes of variables in integrals. In practice, it proves workable to integrate along a circle of radius either r or ζ , or even a suitably close approximation of r, ζ , the choice being often suggested by computational convenience.

556

VIII. SADDLE-POINT ASYMPTOTICS

Figure VIII.5. Plots of |e z z −n−1 | for n = 3 and n = 30 (scaled according to the value of the saddle-point) illustrate the essential concentration condition as higher values of n produce steeper saddle-point paths.

In agreement with the saddle-point strategy, the estimation of K n proceeds by isolating a small portion of the contour, corresponding to z near the real axis. We thus introduce Z +θ0 Z 2π −θ0 (0) (1) Kn = enh(θ) dθ, Kn = enh(θ) dθ, −θ0

θ0

and choose θ0 in accordance with the general heuristic of (21), which corresponds to the two conditions: nθ02 → ∞ (informally: θ0 ≫ n −1/2 ) and nθ03 → 0, (informally: θ0 ≪ n −1/3 ). One way of realizing the compromise is to adopt θ0 = n a , where a is any number between −1/2 and −1/3. To be specific, we fix a = −2/5, so θ0 ≡ θ0 (n) = n −2/5 .

(27)

In particular, the angle of the central region tends to zero. (i) Tails pruning. For z = neiθ one has e z = en cos θ , and, by unimodality properties of

the cosine, the tail integral K (1) satisfies (1) (28) K n = O e−n(cos θ0 −1) = O exp −Cn 1/5 ,

for some C > 0. The tail integral is thus is exponentially small.

(ii) Central approximation. Near θ = 0, one has h(θ) ≡ eiθ − 1 − iθ = − 12 θ 2 + O(θ 3 ), so that, for |θ | ≤ θ0 , 2 3 2 enh(θ) = e−nθ /2+O(nθ ) = e−nθ /2 1 + O(nθ03 ) . Since θ0 = n −2/5 , we have (29)

(0)

Kn

=

Z +n −2/5 −n −2/5

2 e−nθ /2 dθ 1 + O(n −1/5 ) ,

√ which, by the change of variables t = θ n, becomes Z +n 1/10 2 1 (0) (30) Kn = √ e−t /2 dt 1 + O(n −1/5 ) . n −n 1/10

The central integral is thus asymptotic to an incomplete Gaussian integral.

VIII. 3. OVERVIEW OF THE SADDLE-POINT METHOD

557

(iii) Tails completion. Given (30), the task is now easy. We have, elementarily, for c > 0, Z +∞ 2 2 (31) e−t /2 dt = O e−c /2 , c

which expresses the exponential smallness of Gaussian tails. As a consequence, r Z +∞ 2 /2 1 2π (0) −t (32) Kn ∼ √ e dt ≡ . n n −∞

Assembling (28) and (32), we obtain r en 1 en (0) 2π (1) (1) (0) Kn + Kn ∼ √ , i.e., K n = . Kn + Kn ∼ n n n 2π n n 2π n

The proof also provides a relative error term of O(n −1/5 ). Stirling’s formula is thus seen to be (inter alia!) a consequence of the saddle-point method. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Complete asymptotic expansions. Just like Laplace’s method, the saddle-point method can often be made to provide complete asymptotic expansions. The idea is still to localize the main contribution in the central region, but now take into account corrections terms to the quadratic approximation. As an illustration of these general principles, we make explicit here the calculations relative to the inverse factorial. Example VIII.4. Saddle-point analysis of the exponential and the inverse factorial II. For a complete expansion of [z n ]e z , we only need to revisit the estimation of K (0) in the previous example, since K (1) is exponentially small anyhow. One first rewrites Z θ0 1 2 2 (0) = Kn e−nθ /2 en(cos θ−1+ 2 θ ) dθ −θ0

=

Z θ0 √n √ 1 −w2 /2 enξ(w/ n) dw, √ √ e n −θ0 n

1 ξ(θ) := cos θ − 1 + θ 2 . 2

The calculation proceeds exactly in the same way as for the Laplace method (Appendix B.6: Laplace’s method, p. 755). It suffices to expand h(θ) to any fixed order, which is legitimate in the central region. In this way, a representation of the form, ! Z θ0 √n M−1 3M X E k (w) 2 1 + w 1 (0) dw, +O e−w /2 1 + Kn = √ n −θ0 √n n k/2 n M/2 k=1

is obtained, where the E k (w) are computable polynomials of degree 3k. Distributing the integral operator over terms in the asymptotic expansion and completing the tails yields an expansion of the form M−1 X 1 d (0) k Kn ∼ √ + O(n −M/2 ) , n n k/2 k=0 √ R +∞ −w2 /2 where d0 = 2π and dk := −∞ e E k (w) dw. All odd terms disappear by parity. The net result is then the following. Proposition VIII.1 (Stirling’s formula). The factorial numbers satisfy en n −n 1 1 139 571 1 ∼ √ + + − + ··· . 1− n! 12n 288 n 2 51840 n 3 2488320 n 4 2π n

558

VIII. SADDLE-POINT ASYMPTOTICS

Notice the amazing similarity with the form obtained directly for n! in Appendix B.6: Laplace’s method, p. 755. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

VIII.6. A factorial surprise. Why is it that the expansion of n! and 1/n! involve the same set

of coefficients, up to sign?

VIII. 4. Three combinatorial examples The saddle-point method permits us to solve a number of asymptotic problems coming from analytic combinatorics. In this section, we illustrate its use by treating in some detail three combinatorial examples5: Involutions (I), Set partitions (S), Fragmented permutations (F). These are all labelled structures introduced in Chapter EGFs are I = S ET(S ET1,2 (Z)) Involutions : (33) Set Partition : S = S ET(S ET≥1 (Z)) Fragmented perms : F = S ET(S EQ (Z)) ≥1

II. Their specifications and H⇒ H⇒ H⇒

2

I (z) = e z+z /2 z S(z) = ee −1 F(z) = e z/(1−z) .

The first two are entire functions (i.e., they only have a singularity at ∞), while the last one has a singularity at z = 1. Each of these functions exhibits a fairly violent growth—of an exponential type—near its positive singularity, at either a finite or infinite distance. As the reader will have noticed, all three combinatorial types are structurally characterized by a set construction applied to some simpler structure. Each example is treated, starting from the easier saddle-point bounds and proceeding with the saddle-point method. The example of involutions deals with a problem that is only a little more complicated than inverse factorials. The case of set partitions (Bell numbers) illustrates the need in general of a good asymptotic technology for implicitly defined saddle-points. Finally, fragmented permutations, with their singularity at a finite distance, pave the way for the (harder) analysis of integer partitions in Section VIII. 6. We recapitulate the main features of the saddle-point analyses of these three structures, together with the case of inverse factorials (urns), in Figure VIII.6. Example VIII.5. Involutions. An involution is a permutation τ such that τ 2 is the identity 2 permutation (p. 122). The corresponding EGF is I (z) = e z+z /2 . We have in the notation of (23) z2 f (z) = z + − n log z, 2 and the saddle-point equation in polar coordinates is r (1 + r ) = n,

√ 1 1 1 1√ 4n + 1 ∼ n − + √ + O(n −3/2 ). implying r = − + 2 2 2 8 n

5The purpose of these examples is to become further familiarized with the practice of the saddle-point method in analytic combinatorics. The impatient reader can jump directly to the next section, where she will find a general theory that covers these and many more cases.

VIII. 4. THREE COMBINATORIAL EXAMPLES

Class urns

559

EGF

radius (r )

angle (θ0 )

coeff [z n ] in EGF

ez

n

n −2/5

en n −n ∼ √ 2π n

n − 21

n −2/5

S ET(Z) (Ex. VIII.3, p. 555)

involutions S ET(C YC1,2 (Z))

2 e z+z /2

∼

√

∼

en/2−1/4 n −n/2 √n e √ 2 πn

(Ex. VIII.5, p. 558)

set partitions S ET(S ET≥1 (Z))

r

z ee −1

∼ log n − log log n e−2r/5 /r

(Ex. VIII.6, p. 560)

ee −1 ∼ n√ r 2πr (r + 1)er

fragmented perms S ET(S EQ≥1 (Z))

e z/(1−z)

n −7/10

∼ 1 − √1 n

√

e−1/2+2 n ∼ √ 3/4 2 πn

(Ex. VIII.7, p. 562)

Figure VIII.6. A summary of some major saddle-point analyses in combinatorics.

The use of the saddle-point bound then gives mechanically √

(34)

en/2+ n In (1 + o(1)), ≤ e−1/4 n! n n/2

√ √ In ≤ e−1/4 2π ne−n/2+ n n n/2 (1 + o(1)).

√ (Notice that if we use instead the approximate saddle-point value, n, we only lose a factor . e−1/4 = 0.77880.) The cutoff point between the central and non-central regions is determined, in agreement with (21), by the fact that the length δ of the contour (in z coordinates) should satisfy f ′′ (r )δ 2 → ∞ and f ′′′ (r )δ 3 → 0. In terms of angles, this means that we should choose a cutoff angle θ0 that satisfies r 2 f ′′ (r )θ02 → ∞,

r 3 f ′′′ (r )θ03 → 0.

Here, we have f ′′ (r ) = O(1) and f ′′′ (r ) = O(n −1/2 ). Thus, θ0 must be of an order somewhere in between n −1/2 and n −1/3 , and we fix θ0 = n −2/5 . (i) Tails pruning. First, some general considerations are to be made, regarding the behaviour of |I (z)| along large circles, z = r eiθ . One has r2 cos 2θ. 2 As a function of θ , this function decreases on (0, π2 ), since it is the sum of two decreasing log |I (r eiθ )| = r cos θ + 2

2

functions. Thus, |I (z)| attains its maximum (er +r /2 ) at r and its minimum (e−r /2 ) at z = ri. r In the left half-plane, first for θ ∈ ( π2 , 3π 4 ), the modulus |I (z)| is at most e √since cos 2θ < 0. 3π Finally, for θ ∈ ( 4 , π ) smallness is granted by the fact that cos θ < −1/ 2 resulting in the √

2 bound |I (z)| ≤ er /2−r/ 2 . The same argument applies to the lower half plane ℑ(z) < 0.

560

VIII. SADDLE-POINT ASYMPTOTICS

√ As a consequence of these bounds, I (z)/I ( n) is strongly peaked at z = r ; in particular, it is exponentially small away from the positive real axis, in the sense that ! I (r eiθ0 ) I (r eiθ ) =O = O exp(−n α ) , θ 6∈ [−θ0 , θ0 ], (35) I (r ) I (r ) for some α > 0.

(ii) Central approximation. We then proceed and consider the central integral Z e f (r ) +θ0 (0) exp f (r eiθ ) − f (r ) dθ. Jn = 2π −θ0 √ What is required is a Taylor expansion with remainder near the point r ∼ n. In the central region, the relations f ′ (r ) = 0 f ′′ (r ) = 2 + O(1/n), and f ′′′ (z) = O(n −1/2 ) yield r 2 ′′ f (r eiθ ) − f (r ) = f (r )(eiθ − 1)2 + O n −1/2 r 3 θ03 = −r 2 θ 2 + O(n −1/5 ). 2 This is enough to guarantee that Z e f (r ) +θ0 −r 2 θ 2 (0) (36) Jn = dθ 1 + O(n −1/5 ) . e 2π −θ0 √ (iii) Tails completion. Since r ∼ n and θ0 = n −2/5 , we have Z +∞ Z +θ0 Z 2 1/5 2 2 1 1 +θ0 r −t 2 . (37) e dt = e−t dt + O e−n e−r θ dθ = r −θ0 r r −∞ −θ0

Finally, Equations (35), (36), and (37) give:

Proposition VIII.2. The number In of involutions satisfies √ e−1/4 In 1 = √ n −n/2 en/2+ n 1 + O . (38) n! 2 πn n 1/5 Comparing the saddle-point bound (34) to the true asymptotic form (38), we see that the former is only off by a factor of O(n 1/2 ). Here is a table further comparing the asymptotic estimate In⋆ provided by the right side of (38) to the exact value of In : n In In⋆

10

100

1000

9496 8839

2.40533 · 1082

2.14392 · 101296 2.12473 · 101296 .

2.34149 · 1082

√ The relative error is empirically close to 0.3/ n, a fact that could be proved by developing a complete asymptotic expansion along the lines expounded in the previous section, p. 557. The estimate (38) of In is given by Knuth in [378]: his derivation is carried out by means of the Laplace method applied to the explicit binomial sum that expresses In . Our complex analytic derivation follows Moser and Wyman’s in [448]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Example VIII.6. Set partitions and Bell numbers. The number of partitions of a set of n elements defines the Bell number Sn (p. 109) and one has Sn = n!e−1 [z n ]G(z)

where

The saddle-point equation relative to G(z)z −n−1 (in z-coordinates) is ζ eζ = n + 1.

z

G(z) = ee .

VIII. 4. THREE COMBINATORIAL EXAMPLES

561

This famous equation admits an asymptotic solution obtained by iteration (or “bootstrapping”): it suffices to write ζ = log(n + 1) − log ζ , and iterate (say, starting from ζ = 1), which provides the solution, ! log log n log2 log n (39) ζ ≡ ζ (n) = log n − log log n + +O log n log2 n (see [143, p. 26] for a detailed discussion). The corresponding saddle-point bound reads ζ

Sn ≤ n!

ee −1 . ζn

The approximate solution b ζ = log n yields in particular the simplified upper bound Sn ≤ n!

en−1 . (log n)n

which is enough to check that there are much fewer set partitions than permutations, the ratio being bounded from above by a quantity e−n log log n+O(n) . In order to implement the saddle-point strategy, integration will be carried out over a circle of radius r ≡ ζ . We then set G(z) f (z) = log n+1 = e z − (n + 1) log z, z and proceed to estimate the integral, Z 1 dz Jn = G(z) n+1 , 2iπ C z along the circle C of radius r . The usual saddle-point heuristic suggests that the range of the saddle-point is determined by a quantity θ0 ≡ θ0 (n) such that the quadratic terms in the expansion of f at r tend to infinity, while the cubic terms tend to zero. In order to carry out the calculations, it is convenient to express all quantities in terms of r alone, which is possible since n can be disposed of by means of the relation n + 1 = r er . We find: f ′′ (r ) = er (1 + r −1 ),

f ′′′ (r ) = er (1 − 2r 2 ).

Thus, θ0 should be chosen such that r 2 er θ02 → ∞, r 3 er θ03 → 0, and the choice r θ0 = e−2r/5 is suitable. (i) Tails pruning. First, observe that the function G(z) is strongly concentrated near the real axis since, with z = r eiθ , there holds z z r cos θ e e = er cos θ , . (40) e ≤ ee

In particular G(r eiθ ) is exponentially smaller than G(r ) for any fixed θ 6= 0, when r gets large. (ii) Central approximation. One then considers the central contribution, Z 1 dz (0) Jn := G(z) n+1 , 2iπ C (0) z

where C (0) is the part of the circle z = r eiθ such that |θ | ≤ θ0 ≡ e−2r/5 r −1 . Since on C (0) , the third derivative is uniformly O(er ), one has there 1 f (r eiθ ) = f (r ) − r 2 θ 2 f ′′ (r ) + O(r 3 θ 3 er ). 2 (0)

This approximation can then be transported into the integral Jn .

562

VIII. SADDLE-POINT ASYMPTOTICS

(iii) Tails completion. Tails can be completed in the usual way. The net effect is the estimate e f (r ) [z n ]G(z) = p 1 + O r 3 θ 3 er , 2π f ′′ (r ) which, upon making the error term explicit rephrases, as follows. Proposition VIII.3. The number Sn of set partitions of size n satisfies r ee −1 (41) Sn = n! n √ 1 + O(e−r/5 ) , r r 2πr (r + 1)e

where r is defined implicitly by r er = n + 1, so that r = log n − log log n + o(1).

Here is a numerical table of the exact values Sn compared to the main term Sn⋆ of the approximation (41): n

10

100

1000

Sn Sn⋆

115975 114204

4.75853 · 10115 4.75537 · 10115

2.98990 · 101927 2.99012 · 101927

The error is about 1.5% for n = 10, less than 10−3 and 10−4 for n = 100 and n = 1000. The asymptotic form in terms of r itself is the proper one as no back substitution of an asymptotic expansion of r (in terms of n and log n) can provide an asymptotic expansion for Sn solely in terms of n. Regarding explicit representations in terms of n, it is only log Sn that can be expanded as ! 1 log log n 1 log log n 2 . log Sn = log n − log log n − 1 + + +O n log n log n log n (Saddle-point estimates of coefficient integrals often involve such implicitly defined quantities.) This example probably constitutes the most famous application of saddle-point techniques to combinatorial enumeration. The first correct treatment by means of the saddle-point method is due to Moser and Wyman [447]. It is used for instance by de Bruijn in [143, pp. 104–108] as a lead example of the method. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Example VIII.7. Fragmented permutations. These correspond to F(z) = exp(z/(1 − z)). The example now illustrates the case of a singularity at a finite distance. We set as usual z f (z) = − (n + 1) log z, 1−z and start with saddle-point bounds. The saddle-point equation is ζ (42) = n + 1, (1 − ζ )2 so that ζ comes close to the singularity at 1 as n gets large: √ 1 2n + 3 − 4n + 5 1 ζ = =1− √ + + O(n −3/2 ). 2n + 2 2n n √ Here, the approximation b ζ (n) = 1 − 1/ n, leads to

(43)

√

[z n ]F(z) ≤ e−1/2 e2 n (1 + o(1)).

The saddle-point method is then applied with integration along a circle of radius r ≡ ζ . The saddle-point heuristic suggests to restrict the integral to a small sector of angle 2θ0 , and, since f ′′ (r ) = O(n 3/2 ) while f ′′′ (r ) = O(n 2 ), this means taking θ0 such that n 3/4 θ0 → ∞

VIII. 4. THREE COMBINATORIAL EXAMPLES

563

and n 2/3 θ0 → 0. For instance, the choice θ0 = n −7/10 is suitable. Concentration is easily verified: we have 1 − r cos θ 1/(1−z) = e · exp , e z=r eiθ 1 − 2r cos θ + r 2 which is a unimodal function of θ for θ ∈ (−π, π ). (The maximum of this function of θ is of order exp((1 − r )−1 ) and is attained at θ = 0; the minimum is O(1), attained at θ = π .) In particular, along the non-central part |θ | ≥ θ0 of the saddle-point circle, one has √ 1/(1−z) 1/10 , = O(exp (44) n − n e iθ z=r e

so that tails are exponentially small. Local expansions then enable us to justify the use of the general saddle-point formula in this case. The net result is the following. Proposition VIII.4. The number of fragmented permutations, Fn = n![z n ]F(z), satisfies √

e−1/2 e2 n Fn ∼ √ 3/4 . n! 2 πn

(45)

Quite characteristically, the corresponding saddle-point bound (43) turns out to be off the asymptotic estimate (45) only by a factor of order n 3/4 . The relative error of the approximation (45) is about 4%, 1%, 0.3% for n = 10, 100, 1000, respectively. The expansion above has been extended by E. Maitland Wright [618, 619] to several classes of functions with a singularity whose type is an exponential of a function of the form (1 − z)−ρ ; see Note VIII.7. (For the case of (45), Wright [618] refers to an earlier article of Perron published in 1914.) His interest was due, at least partly, to applications to generalized partition asymptotics, of which the basic cases are discussed in Section VIII. 6, p. 574. . . . .

VIII.7. Wright’s expansions. Consider the function F(z) = (1 − z)−β exp

A (1 − z)ρ

,

A > 0,

ρ > 0.

Then, a saddle-point analysis yields, when ρ < 1:

N β−1−ρ/2 exp A(ρ + 1)N ρ [z n ]F(z) ∼ , √ 2π Aρ(ρ + 1)

N :=

n Aρ

1 ρ+1

.

(The case ρ ≥ 1 involves more terms of the asymptotic expansion of the saddle-point.) The method generalizes to analytic and logarithmic multipliers, as well as to a sum of terms of the form A(1 − z)−ρ inside the exponential. See [618, 619] for details.

VIII.8. Some oscillating coefficients. Define the function s(z) = sin

z 1−z

.

The coefficients sn = [z n ]s(z) are seen to change sign at n = 6, 21, 46, 81, 125, 180, . . . . Do signs change infinitely many times? (Hint: Yes. there are two complex conjugate saddle-points √ a eb n with an oscillating and the associated asymptotic forms combine a growth of the type n √ factor similar to sin n.) The sum n X n (−1)k Un = k! k k=0

exhibits similar fluctuations.

564

VIII. SADDLE-POINT ASYMPTOTICS

VIII. 5. Admissibility The saddle-point method is a versatile approach to the analysis of coefficients of fast-growing generating functions, but one which is often cumbersome to apply step-by-step. Fortunately, it proves possible to encapsulate the conditions repeatedly encountered in our previous examples into a general framework. This leads to the notion of an admissible function presented in Subsection VIII. 5.1. By design, saddlepoint analysis applies to such functions and asymptotic forms for their coefficients can be systematically determined: this follows an approach initiated by Hayman in 1956. A great merit of abstraction in this context is that admissible functions satisfy useful closure properties, so that an infinite class of admissible functions of relevance to combinatorial applications can be determined—we develop this theme in Subsection VIII. 5.2, relative to enumeration. Finally, Subsection VIII. 5.3 presents an approach to the probabilistic problem known as depoissonization, which is much akin to admissibility. VIII. 5.1. Admissibility theory. The notion of admissibility is in essence an axiomatization of the conditions underlying Theorem VIII.3 particularized to the case of Cauchy coefficient integrals. In this section, we base our discussion on H –admissibility, the prefix H being a token of Hayman’s original contribution [325]. A crisp account of the theory is given in Section II.7 of Wong’s book [614] and in Odlyzko’s authoritative survey [461, Sec. 12]. We consider here a function G(z) that is analytic at the origin and whose coefficients [z n ]G(z) are to be estimated by Z dz 1 G(z) n+1 . gn ≡ [z n ]G(z) = 2iπ C z The switch to polar coordinates is natural, so that the expansion of G(r eiθ ) for small θ plays a central rˆole: with r a positive real number lying within the disc of analyticity of G(z), the fundamental expansion is

(46)

log G(r eiθ ) = log G(r ) +

∞ X ν=1

αν (r )

(iθ )ν . ν!

Not surprisingly, the most important quantities are the first two terms, and once G(z) has been put into exponential form, G(z) = eh(z) , a simple computation yields a(r ) := α1 (r ) = r h ′ (r ) (47) b(r ) := α2 (r ) = r 2 h ′′ (r ) + r h ′ (r ), with h(z) := log G(z). In terms of G, itself, one thus has (48)

G ′ (r ) , a(r ) = r G(r )

G ′ (r ) G ′′ (r ) b(r ) = r + r2 − r2 G(r ) G(r )

G ′ (r ) G(r )

2

.

Whenever G(z) has non-negative Taylor coefficients at the origin, b(r ) is positive for r > 0 and a(r ) increases as r → ρ, with ρ the radius of convergence of G. (This follows from the argument developed in Note VIII.4, p. 550.)

VIII. 5. ADMISSIBILITY

565

Definition VIII.1 (Hayman–admissibility). Let G(z) have radius of convergence ρ with 0 < ρ ≤ +∞ and be always positive on some subinterval (R0 , ρ) of (0, ρ). The function G(z) is said to be H –admissible (Hayman admissible) if, with a(r ) and b(r ) as defined in (47), it satisfies the following three conditions: H1 . [Capture condition] lim a(r ) = +∞ and lim b(r ) = +∞. r →ρ

r →ρ

H2 . [Locality condition] For some function θ0 (r ) defined over (R0 , ρ) and satisfying 0 < θ0 < π , one has G(r eiθ ) ∼ G(r )eiθa(r )−θ

2 b(r )/2

as r → ρ,

uniformly in |θ | ≤ θ0 (r ). H3 . [Decay condition] Uniformly in θ0 (r ) ≤ |θ | < π G(r ) iθ G(r e ) = o √ . b(r ) Note that the conditions in the definition are intrinsic to the function: they only make reference to the function’s values along circles, no parameter n being involved z yet. It can be easily verified, from the previous examples, that the functions e z , ee −1 , 2 and e z+z /2 are admissible with ρ = +∞, and that the function e z/(1−z) is admissible with ρ = 1 (refer in each case to the discussion of the behaviour of the modulus of 2 2 G(r eiθ ), as θ varies). By contrast, functions such as e z and e z +e z are not admissible since they attain values that are too large when arg(z) is near π . Coefficients of H –admissible functions can be systematically analysed to first asymptotic order, as expressed by the following theorem: Theorem VIII.4 (Coefficients of admissible functions). Let G(z) be an H –admissible function and ζ ≡ ζ (n) be the unique solution in the interval (R0 , ρ) of the equation (49)

ζ

G ′ (ζ ) = n. G(ζ )

The Taylor coefficients of G(z) satisfy, as n → ∞: (50) gn ≡ [z n ]G(z) ∼

G(ζ ) , √ n ζ 2π b(ζ )

b(z) := z 2

d d2 log G(z) + z log G(z). 2 dz dz

Proof. The proof simply amounts to transcribing the definition of admissibility into the conditions of Theorem VIII.3. Integration is carried out over a circle centred at the origin, of some radius r to be specified shortly. Under the change of variable z = r eiθ , the Cauchy coefficient formula becomes Z r −n +π (51) gn ≡ [z n ]G(z) = G(r eiθ )e−niθ dθ. 2π −π In order to obtain a quadratic approximation without a linear term, one chooses the radius of the circle as the positive solution ζ of the equation a(ζ ) = n, that is, a solution of Equation (49). (Thus ζ is a saddle-point of G(z)z −n .) By the capture condition H1 , we have ζ → ρ − as n → +∞. Following the general saddle-point strategy,

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VIII. SADDLE-POINT ASYMPTOTICS

we decompose the integration domain and set, with θ0 as specified in conditions H2 and H3 : Z +θ0 Z 2π −θ0 (0) iθ −niθ (1) J = G(ζ e )e dθ, J = G(ζ eiθ )e−niθ dθ. −θ0

θ0

(i) Tails pruning. By the decay condition H3 , we have a trivial bound, which suffices for our purposes: G(ζ ) (52) J (1) = o √ . b(ζ ) (ii) Central approximation. The uniformity of the locality condition H2 implies Z +θ0 2 (53) J (0) ∼ G(ζ ) e−θ b(ζ )/2 dθ. −θ0

(iii) Tails completion. A combination of the locality condition H2 and the decay condition H3 instantiated at θ = θ0 , shows that b(ζ )θ 2 → +∞ as n → +∞. There results that tails can be completed back, and Z +θ0 /√b(ζ ) Z +∞ Z +θ0 1 1 2 −b(r )θ 2 /2 −t 2 /2 e−t /2 dt. (54) e dθ ∼ √ e dt ∼ √ √ b(r ) −θ0 / b(ζ ) b(r ) −∞ −θ0 From (52), (53), and (54) (or equivalently via an application of Theorem VIII.3), the conclusion of the theorem follows. The usual comments regarding the choice of the function θ0 (r ) apply. Considering the expansion (46), we must have α2 (r )θ02 → ∞ and α3 (r )θ03 → 0. Thus, in order to succeed, the method necessitates a priori α3 (r )2 /α2 (r )3 → 0. Then, θ0 should be taken according to the saddle-point dimensioning heuristic, which can be figuratively summarized as6 1 1 (55) ≪ θ0 ≪ , α2 (r )1/2 α3 (r )1/3 −1/4 −1/6

a possible choice being the geometric mean of the two bounds θ0 = α2 α3 . The original proof by Hayman [325] contains in addition a general result that describes the shape of the individual terms gn r n in the Taylor expansion of G(r ) as r gets closer to its limit value ρ: these appear to exhibit a bell-shaped profile. Precisely, for G with non-negative coefficients, define a family of discrete random variables X (r ) indexed by r ∈ (0, R) as follows: P(X (r ) = n) =

gn r n . G(r )

The model in which a random F structure with GF G(z) is drawn with its size being the random value X (r ) is known as a Boltzmann model. Then: 6We occasionally write A ≪ B, equivalently, B ≫ A, if A = o(B).

VIII. 5. ADMISSIBILITY

567

0.06

0.06 0.05

0.05 0.04

0.04 0.03

0.03 0.02

0.02

0.01

0.01

0

0 0

20

40

60

80

100

120

0

20

40

60

80

100

120

Figure VIII.7. The families of Boltzmann distributions associated with involutions, 2 z G(z) = e z+z /2 with r = 4 . . 8, and set partitions, G(z) = ee −1 with r = 2 . . 3, obey an approximate Gaussian profile.

Proposition VIII.5. The Boltzmann probabilities associated to an admissible function G(z) satisfy, as r → ρ − , a “local” Gaussian estimate; namely, " # ! (a(r ) − n)2 gn r n 1 exp − (56) =√ + ǫn , G(r ) 2b(r ) 2π b(r ) where the error term satisfies ǫn = o(1) as r → ρ uniformly with respect to integers n; that is, limr →ρ supn |ǫn | = 0. The proof is entirely similar to that of Theorem VIII.4; see Note VIII.9 and Figure VIII.7 for a suggestive illustration.

VIII.9. Admissibility and Boltzmann models. The Boltzmann distribution is accessible from gn r n =

Z 2π −θ0 1 G(r eiθ )e−inθ dθ. 2π −θ0

The estimation of this integral is once more based on a fundamental split Z +θ0 Z 2π −θ0 1 1 , J (1) = , gn r n = J (0) + J (1) where J (0) = 2π −θ0 2π +θ0 and θ0 = θ0 (n) is as specified by the admissibility definition. Only the central approximation and tails completion deserves adjustments. The “locality” condition H2 gives uniformly in n, Z G(r ) +θ0 i(a(r )−n)θ− 1 b(r )θ 2 2 J (0) = e (1 + o(1)) dθ 2π "−θ0 # Z +∞ Z +θ0 (57) 1 1 2 2 G(r ) i(a(r )−n)θ− b(r )θ b(r )θ − 2 dθ . e dθ + o e 2 = 2π −θ0 −∞ and setting (a(r ) − n)(2/b(r ))1/2 = c, we obtain "Z # √ +θ0 b(r )/2 2 +ict G(r ) (0) −t (58) J = √ e dt + o(1) . π 2b(r ) −θ0 √b(r )/2

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VIII. SADDLE-POINT ASYMPTOTICS

The integral in (58) can then be routinely extended to a complete Gaussian integral, introducing only o(1) error terms, Z +∞ 2 G(r ) e−t +ict dt + o(1) . (59) J (0) = √ π 2b(r ) −∞ √ 2 Finally, the Gaussian integral evaluates to πe−c /4 , as is seen by completing the square and shifting vertically the integration line.

VIII.10. Hayman’s original. The condition H1 of Theorem VIII.4 can be replaced by H′1 . [Capture condition] lim b(r ) = +∞. r →ρ

That is, a(r ) → +∞ is a consequence of H′1 , H2 , and H3 . (See [325, §5].)

VIII.11. Non-admissible functions. Singularity analysis and H –admissibility conditions are in a sense complementary. Indeed, the function G(z) = (1 − z)−1 fails to be be admissible 1 !! e ∼√ , as the asymptotic form that Theorem VIII.4 would imply is the erroneous [z n ] 1−z 2π corresponding to a saddle-point near 1 − n −1 . The explanation of the discrepancy is as follows: Expansion (46) has αν (r ) of the order of (1 − r )−ν , so that the locality condition and the decay condition cannot be simultaneously satisfied. Singularity analysis salvages the situation by using a larger contour and by normalizing to a global Hankel Gamma integral instead of a more “local” Gaussian integral. This is also in accordance with the fact that the saddle-point formula gives, in the case of [z n ](1 − z)−1 , an estimate, which is within a constant factor of the true value 1. (More generally, functions of the form (1 − z)−β are typical instances with too slow a growth to be admissible.) Closure properties. An important aspect of Hayman’s work is that it leads to general theorems, which guarantee that large classes of functions are admissible. Theorem VIII.5 (Closure of H –admissible functions). Let G(z) and H (z) be admissible functions and let P(z) be a polynomial with real coefficients. Then: (i) The product G(z)H (z) and the exponential e G(z) are admissible functions. (ii) The sum G(z) + P(z) is admissible. If the leading coefficient of P(z) is positive then G(z)P(z) and P(G(z)) are admissible. (iii) If the Taylor coefficients of e P(z) are eventually positive, then e P(z) is admissible. Proof. (Sketch) The easy proofs essentially reduce to making an inspired guess for the choice of the θ0 function, which may be guided by Equation (55) in the usual way, and then routinely checking the conditions of the admissibility definition. For instance, in the case of the exponential, K (z) = e G(z) , the conditions H1 , H2 , H3 of Definition VIII.1 are satisfied if one takes θ0 (r ) = (G(r ))−2/5 . We refer to Hayman’s original paper [325] for details. Exponentials of polynomials. The closure theorem also implies as a very special case that any GF of the form e P(z) with P(z) a polynomial with positive coefficients can be subjected to saddle-point analysis, a fact first noted by Moser and Wyman [449, 450]. P Corollary VIII.2 (Exponentials of polynomials). Let P(z) = mj=1 a j z j have nonnegative coefficients and be aperiodic in the sense that gcd{j | a j 6= 0} = 1. Let

VIII. 5. ADMISSIBILITY

f (z) = e P(z) . Then, one has

e P(r ) f n ≡ [z ] f (z) ∼ √ , 2π λ r n n

1

where

569

d 2 P(r ), λ= r dr

d and r is a function of n given implicitly by r dr P(r ) = n.

The computations are in this case purely mechanical, since they only involve the asymptotic expansion (with respect to n) of an algebraic equation. Granted the basic admissibility theorem and closures properties, many functions are immediately seen to be admissible, including ez ,

ee

z −1

,

e z+z

2 /2

,

which have previously served as lead examples for illustrating the saddle-point method. Corollary VIII.2 also covers involutions, permutations of a fixed order in the symmetric group, permutations with cycles of bounded length, as well as set partitions with bounded block sizes: see Note VIII.12 below. More generally, Corollary VIII.2 applies to any labelled set construction, F = S ET(G), when the sizes of G–components are restricted to a finite set, in which case one has m j X z Gj . H⇒ F [m] (z) = exp F [m] = S ET ∪rj=1 G j , j! j=1

This covers all sorts of graphs (plain or functional) whose connected components are of bounded size.

VIII.12. Applications of “exponentials of polynomials”. Corollary VIII.2 applies to the following combinatorial situations: Permutations of order p (σ p = 1) Permutations with longest cycle ≤ p Partitions of sets with largest block ≤ p

P

zj P j | p jj p z f (z) = exp P j=1 jj p z f (z) = exp j=1 j! .

f (z) = exp

For instance, the number of solutions of σ p = 1 in the symmetric group is asymptotic to n n(1−1/ p) p−1/2 exp(n 1/ p ), e for any fixed prime p ≥ 3 (Moser and Wyman [449, 450]).

Complete asymptotic expansions. Harris and Schoenfeld have introduced in [323] a technical condition of admissibility that is stronger than Hayman admissibility and is called H S–admissibility. Under such H S–admissibility, a complete asymptotic expansion can be obtained. We omit the definition here due to its technical character but refer instead to the original paper [323] and to Odlyzko’s survey [461]. Odlyzko and Richmond [462] later showed that, if g(z) is H –admissible, then f (z) = e g(z) is H S– admissible. Thus, taking H –admissibility to mean at least exponential growth, full asymptotic expansions are to be systematically expected at double exponential growth and beyond. The principles of developing full asymptotic expansions are essentially the same as the ones explained on p. 557—only the discussion of the asymptotic scales involved becomes a bit intricate, at this level of generality.

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VIII. 5.2. Higher-level structures and admissibility. The concept of admissibility and its surrounding properties (Theorems VIII.4 and VIII.5, Corollary VIII.2) afford a neat discussion of which combinatorial classes should lead to counting sequences that are amenable to the saddle-point method. For simplicity, we restrict ourselves here to the labelled universe. Start from the first-level structures, namely S EQ(Z),

C YC(Z),

S ET(Z),

corresponding, respectively, to permutations, circular graphs, and urns, with EGFs 1 1 , log , ez . 1−z 1−z The first two are of singularity analysis class; the last is, as we saw, within the reach of the saddle-point method and is H –admissible. Next consider second-level structures defined by arbitrary composition of two constructions taken among S EQ, C YC, S ET; see Subsection II. 4.2, p. 124 for a preliminary discussion (In the case of the internal construction, it is understood that, for definiteness, the number of components is constrained to be ≥ 1.) There are three structures whose external construction is of the sequence type, namely, S EQ ◦ S EQ,

S EQ ◦ C YC,

S EQ ◦ S ET,

corresponding, respectively, to labelled compositions, alignments, and surjections. All three have a dominant singularity that is a pole; hence they are amenable to meromorphic coefficient asymptotics (Chapters IV and V), or, with weaker remainder estimates, to singularity analysis (Chapters VI and VII). Similarly there are three structures whose external construction is of the cycle type, namely, C YC ◦ S EQ, C YC ◦ C YC, C YC ◦ S ET, corresponding to cyclic versions of the previous ones. In that case, the EGFs have a logarithmic singularity; hence they are amenable to singularity analysis, or