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E-grāmata: Random Matrices: High Dimensional Phenomena

(Lancaster University)
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An introduction to the behaviour of random matrices. Suitable for postgraduate students and non-experts.

This book focuses on the behavior of large random matrices. Standard results are covered, and the presentation emphasizes elementary operator theory and differential equations, so as to be accessible to graduate students and other non-experts. The introductory chapters review material on Lie groups and probability measures in a style suitable for applications in random matrix theory. Later chapters use modern convexity theory to establish subtle results about the convergence of eigenvalue distributions as the size of the matrices increases. Random matrices are viewed as geometrical objects with large dimension. The book analyzes the concentration of measure phenomenon, which describes how measures behave on geometrical objects with large dimension. To prove such results for random matrices, the book develops the modern theory of optimal transportation and proves the associated functional inequalities involving entropy and information. These include the logarithmic Sobolev inequality, which measures how fast some physical systems converge to equilibrium.

Recenzijas

"The book under review is somewhat special in that it is not so much an introduction to the standard models and topics of random matrix theory, but rather to a set of functional analytic issues that are relevant to random matrices." Michael Stolz, Mathematical Reviews

Papildus informācija

An introduction to the behaviour of random matrices. Suitable for postgraduate students and non-experts.
Introduction 1(3)
Metric measure spaces
4(38)
Weak convergence on compact metric spaces
4(6)
Invariant measure on a compact metric group
10(6)
Measures on non-compact Polish spaces
16(6)
The Brunn-Minkowski inequality
22(3)
Gaussian measures
25(2)
Surface area measure on the spheres
27(4)
Lipschitz functions and the Hausdorff metric
31(2)
Characteristic functions and Cauchy transforms
33(9)
Lie groups and matrix ensembles
42(42)
The classical groups, their eigenvalues and norms
42(7)
Determinants and functional calculus
49(7)
Linear Lie groups
56(7)
Connections and curvature
63(3)
Generalized ensembles
66(6)
The Weyl integration formula
72(6)
Dyson's circular ensembles
78(3)
Circular orthogonal ensemble
81(2)
Circular symplectic ensemble
83(1)
Entropy and concentration of measure
84(48)
Relative entropy
84(9)
Concentration of measure
93(6)
Transportation
99(4)
Transportation inequalities
103(3)
Transportation inequalities for uniformly convex potentials
106(3)
Concentration of measure in matrix ensembles
109(5)
Concentration for rectangular Gaussian matrices
114(9)
Concentration on the sphere
123(3)
Concentration for compact Lie groups
126(6)
Free entropy and equilibrium
132(45)
Logarithmic energy and equilibrium measure
132(2)
Energy spaces on the disc
134(8)
Free versus classical entropy on the spheres
142(5)
Equilibrium measures for potentials on the real line
147(7)
Equilibrium densities for convex potentials
154(5)
The quartic model with positive leading term
159(5)
Quartic models with negative leading term
164(5)
Displacement convexity and relative free entropy
169(3)
Toeplitz determinants
172(5)
Convergence to equilibrium
177(19)
Convergence to arclength
177(2)
Convergence of ensembles
179(4)
Mean field convergence
183(6)
Almost sure weak convergence for uniformly convex potentials
189(4)
Convergence for the singular numbers from the Wishart distribution
193(3)
Gradient flows and functional inequalities
196(31)
Variation of functionals and gradient flows
196(7)
Logarithmic Sobolev inequalities
203(3)
Logarithmic Sobolev inequalities for uniformly convex potentials
206(4)
Fisher's information and Shannon's entropy
210(3)
Free information and entropy
213(5)
Free logarithmic Sobolev inequality
218(3)
Logarithmic Sobolev and spectral gap inequalities
221(2)
Inequalities for Gibbs measures on Riemannian manifolds
223(4)
Young tableaux
227(26)
Group representations
227(2)
Young diagrams
229(8)
The Vershik Ω distribution
237(6)
Distribution of the longest increasing subsequence
243(7)
Inclusion-exclusion principle
250(3)
Random point fields and random matrices
253(28)
Determinantal random point fields
253(8)
Determinantal random point fields on the real line
261(9)
Determinantal random point fields and orthogonal polynomials
270(4)
De Branges's spaces
274(4)
Limits of kernels
278(3)
Integrable operators and differential equations
281(40)
Integrable operators and Hankel integral operators
281(8)
Hankel integral operators that commute with second order differential operators
289(4)
Spectral bulk and the sine kernel
293(6)
Soft edges and the Airy kernel
299(5)
Hard edges and the Bessel kernel
304(6)
The spectra of Hankel operators and rational approximation
310(5)
The Tracy-Widom distribution
315(6)
Fluctuations and the Tracy-Widom distribution
321(31)
The Costin-Lebowitz central limit theorem
321(6)
Discrete Tracy-Widom systems
327(1)
The discrete Bessel kernel
328(6)
Plancherel measure on the partitions
334(9)
Fluctuations of the longest increasing subsequence
343(2)
Fluctuations of linear statistics over unitary ensembles
345(7)
Limit groups and Gaussian measures
352(21)
Some inductive limit groups
352(5)
Hua-Pickrell measure on the infinite unitary group
357(8)
Gaussian Hilbert space
365(4)
Gaussian measures and fluctuations
369(4)
Hermite polynomials
373(19)
Tensor products of Hilbert space
373(2)
Hermite polynomials and Mehler's formula
375(6)
The Ornstein-Uhlenbeck semigroup
381(3)
Hermite polynomials in higher dimensions
384(8)
From the Ornstein---Uhlenbeck process to the Burgers equation
392(19)
The Ornstein-Uhlenbeck process
392(4)
The logarithmic Sobolev inequality for the Ornstein-Uhlenbeck generator
396(2)
The matrix Ornstein-Uhlenbeck process
398(3)
Solutions for matrix stochastic differential equations
401(7)
The Burgers equation
408(3)
Noncommutative probability spaces
411(13)
Noncommutative probability spaces
411(3)
Tracial probability spaces
414(4)
The semicircular distribution
418(6)
References 424(9)
Index 433
Gordon Blower is currently Head of the Department of Mathematics and Statistics at Lancaster University, and Professor of Mathematical Analysis.