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E-raamat: Combinatorics and Random Matrix Theory

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Over the last fifteen years a variety of problems in combinatorics has been solved in terms of random matrix theory. More precisely, the situation is as follows: the problems at hand are probabilistic in nature and, in an appropriate scaling limit, it turns out that certain key quantities associated with these problems behave statistically like the eigenvalues of a (large) random matrix. Said differently, random matrix theory provides a ``stochastic special function theory'' for a broad and growing class of problems in combinatorics. The goal of this book is to analyze in detail two key examples of this phenomenon, viz., Ulam's problem for increasing subsequences of random permutations and domino tilings of the Aztec diamond. Other examples are also described along the way, but in less detail.

Techniques from many different areas in mathematics are needed to analyze these problems. These areas include combinatorics, probability theory, functional analysis, complex analysis, and the theory of integrable systems. The book is self-contained, and along the way we develop enough of the theory we need from each area that a general reader with, say, two or three years experience in graduate school can learn the subject directly from the text.

Arvustused

The book is self-contained, and along the way we develop enough of the theory we need from each area that a general reader with, say, two or three years experience in graduate school can learn the subject directly from the text." - Zentralblatt Math

"The book covers exciting results, and has a wealth of information." - Milós Bóna, MAA Reviews

"[ T]he book is carefully written and will serve as an excellent reference." - Terence Tao, Mathematical Reviews

Preface xi
Chapter 1 Introduction
1(18)
§1.1 Ulam's Problem: Random Permutations
2(10)
§1.2 Random Tilings of the Aztec Diamond
12(2)
§1.3 General Remarks
14(5)
Chapter 2 Poissonization and De-Poissonization
19(8)
§2.1 Hammersley's Poissonization of Ulam's Problem
19(2)
§2.2 De-Poissonization Lemmas
21(6)
Chapter 3 Permutations and Young Tableaux
27(50)
§3.1 The Robinson-Schensted Correspondence
28(21)
§3.2 The Number of Standard Young Tableaux
49(14)
§3.3 Applications and Equivalent Models
63(14)
Chapter 4 Bounds on the Expected Value of lN
77(18)
§4.1 Lower Bound
77(1)
§4.2 Existence of c
78(4)
§4.3 Young Diagrams in a Markov Chain and an Optimal Upper Bound
82(5)
§4.4 Asymptotics of the Conjugacy Classes of the Symmetric Group
87(8)
Chapter 5 Orthogonal Polynomials, Riemann-Hilbert Problems, and Toeplitz Matrices
95(44)
§5.1 Orthogonal Polynomials on the Real Line (OPRL)
95(4)
§5.2 Some Classical Orthogonal Polynomials
99(1)
§5.3 The Riemann-Hilbert Problem (RHP) for Orthogonal Polynomials
100(6)
§5.4 Orthogonal Polynomials on the Unit Circle (OPUC) and Toeplitz Matrices
106(5)
§5.5 RHP: Precise Description
111(7)
§5.6 Integrable Operators
118(3)
§5.7 The Strong Szego Limit Theorem
121(9)
§5.8 Inverses of Large Toeplitz Matrices
130(9)
Chapter 6 Random Matrix Theory
139(26)
§6.1 Unitary Ensembles and the Eigenvalue Density Function
139(3)
§6.2 Andreief's Formula and the Computation of Basic Statisitcs
142(4)
§6.3 Gap Probabilities and Correlation Functions
146(6)
§6.4 Scaling Limits and Universality
152(5)
§6.5 The Tracy-Widom Distribution Function
157(8)
Chapter 7 Toeplitz Determinant Formula
165(22)
§7.1 First Proof
167(2)
§7.2 Second Proof
169(1)
§7.3 Recurrence Formulae and Differential Equations
170(14)
§7.4 Heuristic Argument for Convergence of the Scaled Distribution for L(t) to the Tracy-Widom Distribution
184(3)
Chapter 8 Fredholm Determinant Formula
187(20)
§8.1 First Proof: Borodin-Okounkov-Geronimo-Case Identity
190(10)
§8.2 Second Proof
200(7)
Chapter 9 Asymptotic Results
207(46)
§9.1 Exponential Upper Tail Estimate
208(6)
§9.2 Exponential Lower Tail Estimate
214(10)
§9.3 Convergence of L(t)/t and lN/√N
224(2)
§9.4 Central Limit Theorem
226(13)
§9.5 Uniform Tail Estimates and Convergence of Moments
239(1)
§9.6 Transversal Fluctuations
240(13)
Chapter 10 Schur Measure and Directed Last Passage Percolation
253(52)
§10.1 Schur Functions
253(20)
§10.2 RSK and Directed Last Passage Percolation
273(7)
§10.3 Special Cases of Directed Last Passage Percolation
280(10)
§10.4 Gessel's Formula for Schur Measure
290(4)
§10.5 Fredholm Determinant Formula
294(4)
§10.6 Asymptotics of Directed Last Passage Percolation
298(3)
§10.7 Equivalent Models
301(4)
Chapter 11 Determinantal Point Processes
305(12)
Chapter 12 Tiling of the Aztec Diamond
317(60)
§12.1 Nonintersecting Lattice Paths
318(16)
§12.2 Density Function
334(13)
§12.3 Asymptotics
347(30)
Chapter 13 The Dyson Process and the Brownian Dyson Process
377(44)
§13.1 Dyson Process
379(1)
§13.2 Brownian Dyson Process
380(1)
§13.3 Derivation of the Dyson Process and the Brownian Dyson Process
381(8)
§13.4 Noncolliding Property of the Eigenvalues of Matrix Brownian Motion
389(6)
§13.5 Noncolliding Property of the Eigenvalues of the Matrix Ornstein-Uhlenbeck Process
395(7)
§13.6 Nonintersecting Processes
402(19)
Appendix A Theory of Trace Class Operators and Fredholm Determinants 421(10)
Appendix B Steepest-descent Method for the Asymptotic Evaluation of Integrals in the Complex Plane 431(6)
Appendix C Basic Results of Stochastic Calculus 437(8)
Bibliography 445(14)
Index 459
Jinho Baik, University of Michigan, Ann Arbor, MI, USA.

Percy Deift, Courant Institute, New York University, NY, USA.