Markov operators, classical orthogonal polynomial ensembles, and random matrices M. Ledoux, Institut de Mathématiques de Toulouse, France 5ecm Amsterdam, July 2008
recent study of random matrix and random growth models
recent study of random matrix and random growth models new asymptotics common, non central, rate (mean) 1/3 universal limiting Tracy-Widom distribution
recent study of random matrix and random growth models new asymptotics common, non central, rate (mean) 1/3 universal limiting Tracy-Widom distribution random matrices, longest increasing subsequence, random growth models, last passage percolation... P. Forrester, C. Tracy, H. Widom, J. Baik, P. Deift, K. Johansson
invariant random matrix models
invariant random matrix models P(dX ) = 1 Z exp ( Tr ( v(x ) )) dx, v : R R X = X N real symmetric or Hermitian N N matrix
invariant random matrix models P(dX ) = 1 Z exp ( Tr ( v(x ) )) dx, v : R R X = X N real symmetric or Hermitian N N matrix v(x) = β x 2 /4 Gaussian Orthogonal (β = 1) Unitary (β = 2) Ensembles
invariant random matrix models P(dX ) = 1 Z exp ( Tr ( v(x ) )) dx, v : R R X = X N real symmetric or Hermitian N N matrix v(x) = β x 2 /4 Gaussian Orthogonal (β = 1) Unitary (β = 2) Ensembles joint law of the eigenvalues (λ N 1 λn N ) of X N
invariant random matrix models P(dX ) = 1 Z exp ( Tr ( v(x ) )) dx, v : R R X = X N real symmetric or Hermitian N N matrix v(x) = β x 2 /4 Gaussian Orthogonal (β = 1) Unitary (β = 2) Ensembles joint law of the eigenvalues (λ N 1 λn N ) of X N P N (dx) = 1 Z N (x) β N i=1 dµ(x i ), x = (x 1,..., x N ) R N N (x) = i<j (x i x j ), dµ(x) = e v(x) dx
random growth models directed last passage percolation
random growth models directed last passage percolation w ij, 1 i, j N, iid geometric random variables
random growth models directed last passage percolation w ij, 1 i, j N, iid geometric random variables W N = max π (i,j) π w ij π up/right paths from (1, 1) to (N, N)
random growth models directed last passage percolation w ij, 1 i, j N, iid geometric random variables W N = max π (i,j) π w ij π up/right paths from (1, 1) to (N, N) K. Johansson (2000)
random growth models directed last passage percolation w ij, 1 i, j N, iid geometric random variables W N = max π (i,j) π w ij π up/right paths from (1, 1) to (N, N) P ( W N t) = K. Johansson (2000) {max x i t+n 1} 1 Z N(x) 2 N dµ(x i ) i=1
random growth models directed last passage percolation w ij, 1 i, j N, iid geometric random variables W N = max π (i,j) π w ij π up/right paths from (1, 1) to (N, N) P ( W N t) = K. Johansson (2000) {max x i t+n 1} 1 Z N(x) 2 N dµ(x i ) i=1 µ({x}) = (1 q)q x, x N geometric distribution
law of (λ N 1,..., λ N N ) : PN (dx) = 1 Z N (x) β N i=1 dµ(x i )
law of (λ N 1,..., λ N N ) : PN (dx) = 1 Z N (x) β global regime : spectral measure N i=1 dµ(x i )
law of (λ N 1,..., λ N N ) : PN (dx) = 1 Z N (x) β global regime : spectral measure N i=1 dµ(x i ) large deviation asymptotics (every β)
law of (λ N 1,..., λ N N ) : PN (dx) = 1 Z N (x) β global regime : spectral measure N i=1 dµ(x i ) P N (dx) = 1 Z exp ( N v(x i ) + β i<j i=1 ) log x i x j dx
law of (λ N 1,..., λ N N ) : PN (dx) = 1 Z N (x) β global regime : spectral measure N i=1 dµ(x i ) P N (dx) = 1 Z exp ( N v(x i ) + β i<j i=1 ) log x i x j dx spectral measure : λn i = λ N i suitably normalized N µ N = 1 N i=1 δ bλ N i
law of (λ N 1,..., λ N N ) : PN (dx) = 1 Z N (x) β global regime : spectral measure N i=1 dµ(x i ) P N (dx) = 1 Z exp ( N v(x i ) + β i<j i=1 ) log x i x j dx spectral measure : λn i = λ N i suitably normalized µ N = 1 N N i=1 δ bλ N i equilibrium measure
law of (λ N 1,..., λ N N ) : PN (dx) = 1 Z N (x) β global regime : spectral measure N i=1 dµ(x i ) P N (dx) = 1 Z exp ( N v(x i ) + β i<j i=1 ) log x i x j dx spectral measure : λn i = λ N i suitably normalized µ N = 1 N δ bλ N N i i=1 equilibrium measure minimizer of 2 v dν β log x y dν(x)dν(y) weighted logarithmic potential theory
local regime : individual behavior (spacings, extreme values)
local regime : individual behavior (spacings, extreme values) β = 2 orthogonal polynomial method
local regime : individual behavior (spacings, extreme values) β = 2 orthogonal polynomial method P l, l N orthogonal polynomials for µ
local regime : individual behavior (spacings, extreme values) β = 2 orthogonal polynomial method P l, l N orthogonal polynomials for µ N (x) = C n det ( P l 1 (x k ) ) 1 k,l N
local regime : individual behavior (spacings, extreme values) β = 2 orthogonal polynomial method P l, l N orthogonal polynomials for µ N (x) 2 = C 2 n det 2( P l 1 (x k ) ) 1 k,l N
local regime : individual behavior (spacings, extreme values) β = 2 orthogonal polynomial method P l, l N orthogonal polynomials for µ N (x) 2 = C 2 n det 2( P l 1 (x k ) ) 1 k,l N P N (dx) = 1 Z N(x) 2 N i=1 dµ(x i)
local regime : individual behavior (spacings, extreme values) β = 2 orthogonal polynomial method P l, l N orthogonal polynomials for µ N (x) 2 = C 2 n det 2( P l 1 (x k ) ) 1 k,l N P N determinantal random point field structure
local regime : individual behavior (spacings, extreme values) β = 2 orthogonal polynomial method P l, l N orthogonal polynomials for µ N (x) 2 = C 2 n det 2( P l 1 (x k ) ) 1 k,l N P N determinantal random point field structure marginals of P N : determinants of the kernel K N (x, y) = N 1 l=0 P l (x)p l (y)
local regime : individual behavior (spacings, extreme values) β = 2 orthogonal polynomial method P l, l N orthogonal polynomials for µ N (x) 2 = C 2 n det 2( P l 1 (x k ) ) 1 k,l N P N determinantal random point field structure marginals of P N : determinants of the kernel K N (x, y) = N 1 l=0 P l (x)p l (y) orthogonal polynomial ensemble
(common) asymptotics of orthogonal polynomials universal local regime : spacings and edge behavior
(common) asymptotics of orthogonal polynomials universal local regime : spacings and edge behavior top edge behavior largest eigenvalue (particle) fluctuates at the rate (mean) 1/3 limiting Tracy-Widom distribution F TW
(common) asymptotics of orthogonal polynomials universal local regime : spacings and edge behavior top edge behavior largest eigenvalue (particle) fluctuates at the rate (mean) 1/3 limiting Tracy-Widom distribution F TW C. Tracy, H. Widom, P. Deift, X. Zhou, J. Baik, K. Johansson,. Kriecherbauer, K. McLaughlin, P. Miller, S. Venakides, M. Vanlessen, D. Gioev, P. Bleher, A. Its, A. Kuijlaars, M. Shcherbina, L. Pastur...
example : Gaussian Unitary Ensemble (GUE)
example : Gaussian Unitary Ensemble (GUE) dµ(x) = e x2 /2 dx 2π, K N Hermite kernel, λn i = λ N i / N
example : Gaussian Unitary Ensemble (GUE) dµ(x) = e x2 /2 dx 2π, K N Hermite kernel, λn i = λ N i / N spectral measure µ N = 1 N N i=1 δ bλ N 1 4 x i 2π 2 dx on ( 2, +2) Wigner semi-circle law
example : Gaussian Unitary Ensemble (GUE) dµ(x) = e x2 /2 dx 2π, K N Hermite kernel, λn i = λ N i / N spectral measure µ N = 1 N N i=1 δ bλ N 1 4 x i 2π 2 dx on ( 2, +2) Wigner semi-circle law largest eigenvalue λ N N
example : Gaussian Unitary Ensemble (GUE) dµ(x) = e x2 /2 dx 2π, K N Hermite kernel, λn i = λ N i / N spectral measure µ N = 1 N N i=1 δ bλ N 1 4 x i 2π 2 dx on ( 2, +2) Wigner semi-circle law largest eigenvalue λ N N / N 2
example : Gaussian Unitary Ensemble (GUE) dµ(x) = e x2 /2 dx 2π, K N Hermite kernel, λn i = λ N i / N spectral measure µ N = 1 N N i=1 δ bλ N 1 4 x i 2π 2 dx on ( 2, +2) Wigner semi-circle law largest eigenvalue λ N N / N 2 N 1/6[ λ N N 2 N ] F TW Tracy-Widom distribution
example : Gaussian Unitary Ensemble (GUE) dµ(x) = e x2 /2 dx 2π, K N Hermite kernel, λn i = λ N i / N spectral measure µ N = 1 N N i=1 δ bλ N 1 4 x i 2π 2 dx on ( 2, +2) Wigner semi-circle law largest eigenvalue λ N N / N 2 N 1/6[ λ N N 2 N ] F TW Tracy-Widom distribution ( F TW (s) = exp u = 2u 3 + xu s (x s)u(x) 2 dx Painlevé II equation ), s R
example : last passage percolation W N = max π (i,j) π w ij
example : last passage percolation W N = max π (i,j) π w ij µ geometric, K N Meixner kernel, λn i = λ N i /N
example : last passage percolation W N = max π (i,j) π w ij µ geometric, K N Meixner kernel, λn i = λ N i /N spectral measure µ N = 1 N N δ bλ N equilibrium measure on (a, b) i i=1
example : last passage percolation W N = max π (i,j) π w ij µ geometric, K N Meixner kernel, λn i = λ N i /N spectral measure µ N = 1 N N δ bλ N equilibrium measure on (a, b) i i=1 largest particle λ N N W N
example : last passage percolation W N = max π (i,j) π w ij µ geometric, K N Meixner kernel, λn i = λ N i /N spectral measure µ N = 1 N N δ bλ N equilibrium measure on (a, b) i i=1 largest particle λ N N W N/N b
example : last passage percolation W N = max π (i,j) π w ij µ geometric, K N Meixner kernel, λn i = λ N i /N spectral measure µ N = 1 N N δ bλ N equilibrium measure on (a, b) i i=1 largest particle λ N N W N/N b N 1/3[ W N bn ] F TW Tracy-Widom distribution K. Johansson (2000)
Markov operator tools
Markov operator tools for classical orthogonal polynomial ensembles of random matrix and random growth models
Markov operator tools for classical orthogonal polynomial ensembles of random matrix and random growth models non asymptotic results
Markov operator tools for classical orthogonal polynomial ensembles of random matrix and random growth models non asymptotic results spectral description (universal arcsine law)
Markov operator tools for classical orthogonal polynomial ensembles of random matrix and random growth models non asymptotic results spectral description (universal arcsine law) recurrence equations for moments (map enumeration)
Markov operator tools for classical orthogonal polynomial ensembles of random matrix and random growth models non asymptotic results spectral description (universal arcsine law) recurrence equations for moments (map enumeration) recurrence equations for real symmetric models (Gaussian Orthogonal Ensemble)
Markov operator tools for classical orthogonal polynomial ensembles of random matrix and random growth models non asymptotic results spectral description (universal arcsine law) recurrence equations for moments (map enumeration) recurrence equations for real symmetric models (Gaussian Orthogonal Ensemble) non asymptotic tail inequalities for largest eigenvalues (optimal rate)
classical orthogonal polynomial ensembles Hermite, Laguerre, Jacobi, Charlier, Meixner, Krawtchouk, Hahn
classical orthogonal polynomial ensembles Hermite, Laguerre, Jacobi, Charlier, Meixner, Krawtchouk, Hahn differential equations and operators
classical orthogonal polynomial ensembles Hermite, Laguerre, Jacobi, Charlier, Meixner, Krawtchouk, Hahn differential equations and operators mean spectral measure µ N = E ( 1 N i=1 δ λ N ) i
classical orthogonal polynomial ensembles Hermite, Laguerre, Jacobi, Charlier, Meixner, Krawtchouk, Hahn differential equations and operators mean spectral measure µ N = E ( 1 N i=1 δ λ N ) i f, µ N = f (x) 1 N K N(x, x) dµ(x) = f N 1 1 Pl 2 N dµ l=0
classical orthogonal polynomial ensembles Hermite, Laguerre, Jacobi, Charlier, Meixner, Krawtchouk, Hahn differential equations and operators mean spectral measure µ N = E ( 1 N i=1 δ λ N ) i f, µ N = f (x) 1 N K N(x, x) dµ(x) = f N 1 1 Pl 2 N dµ l=0 example : Gaussian Unitary Ensemble (GUE)
classical orthogonal polynomial ensembles Hermite, Laguerre, Jacobi, Charlier, Meixner, Krawtchouk, Hahn differential equations and operators mean spectral measure µ N = E ( 1 N i=1 δ λ N ) i f, µ N = f (x) 1 N K N(x, x) dµ(x) = f N 1 1 Pl 2 N dµ l=0 example : Gaussian Unitary Ensemble (GUE) P l, l N Hermite polynomials for dµ(x) = e x2 /2 dx 2π normalized in L 2 (µ)
f, µ N = f N 1 1 Pl 2 N dµ l=0
f, µ N = f N 1 1 Pl 2 N dµ l=0 preliminary investigation : measure P 2 N dµ
f, µ N = f N 1 1 Pl 2 N dµ l=0 preliminary investigation : measure P 2 N dµ Laplace transform : ϕ(t) = e tx P 2 N dµ, t R
f, µ N = f N 1 1 Pl 2 N dµ l=0 preliminary investigation : measure P 2 N dµ Laplace transform : ϕ(t) = e tx P 2 N dµ, t R Hermite operator : Lf = f x f
f, µ N = f N 1 1 Pl 2 N dµ l=0 preliminary investigation : measure P 2 N dµ Laplace transform : ϕ(t) = e tx P 2 N dµ, t R Hermite operator : Lf = f x f Gaussian integration by parts : f ( Lg)dµ = f g dµ
f, µ N = f N 1 1 Pl 2 N dµ l=0 preliminary investigation : measure P 2 N dµ Laplace transform : ϕ(t) = e tx P 2 N dµ, t R Hermite operator : Lf = f x f Gaussian integration by parts : f ( Lg)dµ = f g dµ LP N = N P N eigenvectors
Laplace transform : ϕ(t) = e tx P 2 N dµ second order differential equation tϕ + ϕ t(t 2 + 4N + 2)ϕ = 0
Laplace transform : ϕ(t) = e tx P 2 N dµ second order differential equation tϕ + ϕ t(t 2 + 4N + 2)ϕ = 0 under the GUE scaling : t t/ N ( λ N i = λ N i / N)
Laplace transform : ϕ(t) = e tx P 2 N dµ second order differential equation tϕ + ϕ t(t 2 + 4N + 2)ϕ = 0 under the GUE scaling : t t/ N ( λ N i = λ N i / N) limiting differential equation t Φ + Φ 4t Φ = 0
Laplace transform : ϕ(t) = e tx P 2 N dµ second order differential equation tϕ + ϕ t(t 2 + 4N + 2)ϕ = 0 under the GUE scaling : t t/ N ( λ N i = λ N i / N) limiting differential equation t Φ + Φ 4t Φ = 0 Φ Laplace transform of the arcsine law dx π 4 x 2 on ( 2, +2)
common behavior, with the limiting arcsine law for the classical orthogonal polynomials of (normalized) measures P 2 N dµ
common behavior, with the limiting arcsine law for the classical orthogonal polynomials of (normalized) measures P 2 N dµ continuous variable : Hermite, Laguerre, Jacobi
common behavior, with the limiting arcsine law for the classical orthogonal polynomials of (normalized) measures P 2 N dµ continuous variable : Hermite, Laguerre, Jacobi discrete variable : Charlier, Meixner, Krawtchouk, Hahn
common behavior, with the limiting arcsine law for the classical orthogonal polynomials of (normalized) measures P 2 N dµ continuous variable : Hermite, Laguerre, Jacobi discrete variable : Charlier, Meixner, Krawtchouk, Hahn varying parameters (in N)
common behavior, with the limiting arcsine law for the classical orthogonal polynomials of (normalized) measures P 2 N dµ continuous variable : Hermite, Laguerre, Jacobi discrete variable : Charlier, Meixner, Krawtchouk, Hahn varying parameters (in N) compact case : A. Maté, P. Nevai, V. Totik (1985)
spectral measure : averaging procedure
spectral measure : averaging procedure GUE E ( f, µ N ) = 1 N N 1 l=0 ( ) l f N x Pl 2 dµ l
spectral measure : averaging procedure GUE E ( f, µ N ) = 1 N N 1 l=0 ( ) l f N x Pl 2 dµ l µ N = 1 N N i=1 δ bλ N i U ξ U uniform, ξ arcsine, independent
spectral measure : averaging procedure GUE E ( f, µ N ) = 1 N N 1 l=0 ( ) l f N x Pl 2 dµ l µ N = 1 N N i=1 δ bλ N i U ξ U uniform, ξ arcsine, independent U ξ semi-circle law on ( 2, +2)
spectral measure : averaging procedure GUE E ( f, µ N ) = 1 N N 1 l=0 ( ) l f N x Pl 2 dµ l µ N = 1 N N i=1 δ bλ N i U ξ U uniform, ξ arcsine, independent U ξ semi-circle law on ( 2, +2) Meixner example (µ geometric) : equilibrium measure
example : last passage percolation W N = max π (i,j) π w ij µ geometric, K N Meixner kernel, λn i = λ N i /N spectral measure µ N = 1 N N δ bλ N equilibrium measure on (a, b) i i=1 largest particle λ N N W N/N b N 1/3[ W N bn ] F TW Tracy-Widom distribution K. Johansson (2000)
spectral measure : averaging procedure GUE E ( f, µ N ) = 1 N N 1 l=0 ( ) l f N x Pl 2 dµ l µ N = 1 N N i=1 δ bλ N i U ξ U uniform, ξ arcsine, independent U ξ semi-circle law on ( 2, +2) Meixner example (µ geometric) : equilibrium measure
spectral measure : averaging procedure GUE E ( f, µ N ) = 1 N N 1 l=0 ( ) l f N x Pl 2 dµ l µ N = 1 N N i=1 δ bλ N i U ξ U uniform, ξ arcsine, independent U ξ semi-circle law on ( 2, +2) Meixner example (µ geometric) : equilibrium measure 1 ( [ ] ) qu(1 + U) ξ + U + q(1 + U) 1 q
moment recursion formula
moment recursion formula E ( e t Tr(X N ) ) = ψ(t) = N 1 e tx l=0 P 2 l dµ, t R differential equation
moment recursion formula E ( e t Tr(X N ) ) = ψ(t) = N 1 e tx l=0 P 2 l dµ, t R differential equation GUE tψ + 3ψ t(t 2 + 4N)ψ = 0 U. Haagerup, S. Thorbjørnsen (2003)
moment recursion formula E ( e t Tr(X N ) ) = ψ(t) = N 1 e tx l=0 P 2 l dµ, t R differential equation GUE tψ + 3ψ t(t 2 + 4N)ψ = 0 U. Haagerup, S. Thorbjørnsen (2003) recurrence equation on moments ( E Tr ( (X N ) p ) ) = N 1 x p l=0 P 2 l dµ, p N
( ap N = E Tr ( (X N ) 2p ) ), X N GUE three term recurrence equation (p + 1)a N p = (4p 2)Na N p 1 + (p 1)(2p 1)(2p 3)aN p 2 J. Harer, D. Zagier (1986)
( ap N = E Tr ( (X N ) 2p ) ), X N GUE three term recurrence equation (p + 1)a N p = (4p 2)Na N p 1 + (p 1)(2p 1)(2p 3)aN p 2 J. Harer, D. Zagier (1986) map enumeration (Wick products)
( ap N = E Tr ( (X N ) 2p ) ), X N GUE three term recurrence equation (p + 1)a N p = (4p 2)Na N p 1 + (p 1)(2p 1)(2p 3)aN p 2 J. Harer, D. Zagier (1986) map enumeration (Wick products) a N p = g 0 ε g (p) N p+1 2g genus series (oriented case)
( ap N = E Tr ( (X N ) 2p ) ), X N GUE three term recurrence equation (p + 1)a N p = (4p 2)Na N p 1 + (p 1)(2p 1)(2p 3)aN p 2 J. Harer, D. Zagier (1986) map enumeration (Wick products) a N p = g 0 ε g (p) N p+1 2g genus series (oriented case) ε 0 (p) Catalan numbers
Markov operator technology similar recursion formulas for the classical orthogonal polynomial ensembles
Markov operator technology similar recursion formulas for the classical orthogonal polynomial ensembles continuous variable : Hermite, Laguerre, Jacobi
Markov operator technology similar recursion formulas for the classical orthogonal polynomial ensembles continuous variable : Hermite, Laguerre, Jacobi discrete variables : Charlier, Meixner, Krawtchouk, Hahn explicit expressions for the (factorial) moments
Gaussian Orthogonal Ensemble β = 1
Gaussian Orthogonal Ensemble β = 1 P N (dx) = 1 Z N (x) N dµ(x i ), x = (x 1,..., x N ) R N i=1 dµ(x) = e x2 /2 dx 2π
Gaussian Orthogonal Ensemble β = 1 P N (dx) = 1 Z N (x) N dµ(x i ), x = (x 1,..., x N ) R N i=1 dµ(x) = e x2 /2 dx 2π Pfaffian instead of determinant
Gaussian Orthogonal Ensemble β = 1 P N (dx) = 1 Z N (x) N dµ(x i ), x = (x 1,..., x N ) R N i=1 dµ(x) = e x2 /2 dx 2π Pfaffian instead of determinant limiting spectral distribution : Wigner semi-circle law Tracy-Widom edge asymptotics
moment recursion formula?
moment recursion formula? mean spectral measure ( E Tr ( f (X N ) )) = f (x) µ N GOE(x)dµ(x)
moment recursion formula? mean spectral measure ( E Tr ( f (X N ) )) = f (x) µ N GOE(x)dµ(x) P l, l N Hermite polynomials µ N GOE = N 1 Pl 2 l=0
moment recursion formula? mean spectral measure ( E Tr ( f (X N ) )) = f (x) µ N GOE(x)dµ(x) P l, l N Hermite polynomials µ N GOE = N 1 l=0 P 2 l + ex2 /4 P N 1 e x 2 /4 P N 1 dµ 1 N odd πn + /4 8 ex2 P N 1 sgn (x y) e x2 /4 (y)p N (y)dµ(y)
Markov operator tools : differential equation on the Laplace transform of the spectral measure
Markov operator tools : differential equation on the Laplace transform of the spectral measure b N p moment recursion formula ( = E Tr ( (X N ) 2p ) ), p N
Markov operator tools : differential equation on the Laplace transform of the spectral measure b N p moment recursion formula ( = E Tr ( (X N ) 2p ) ), p N recursion formula coupled with the moments a N p of the GUE b N p = (4N 2)b N p 1 + 4(2p 2)(2p 3)b N p 2 + a N p (4N 3)a N p 1 (2p 2)(2p 3)a N p 2
effective values a0 N = N a1 N = N 2 GUE a2 N = 2N 3 + N a3 N = 5N 4 + 10N 2 a N 4 = 14N 5 + 70N 3 + 21N b0 N = N b1 N = N 2 + N GOE b2 N = 2N 3 + 5N 2 + 5N b3 N = 5N 4 + 22N 3 + 52N 2 + 41N b4 N = 14N 5 + 93N 4 + 374N 3 + 690N 2 + 509N
b N p ( = E Tr ( (X N ) 2p ) ), X N GOE recursion formula coupled with the moments a N p of the GUE b N p = (4N 2)b N p 1 + 4(2p 2)(2p 3)b N p 2 + a N p (4N 3)a N p 1 (2p 2)(2p 3)a N p 2
b N p ( = E Tr ( (X N ) 2p ) ), X N GOE recursion formula coupled with the moments a N p of the GUE b N p = (4N 2)b N p 1 + 4(2p 2)(2p 3)b N p 2 + a N p (4N 3)a N p 1 (2p 2)(2p 3)a N p 2 b N p five term recurrence equation
b N p ( = E Tr ( (X N ) 2p ) ), X N GOE recursion formula coupled with the moments a N p of the GUE b N p = (4N 2)b N p 1 + 4(2p 2)(2p 3)b N p 2 + a N p (4N 3)a N p 1 (2p 2)(2p 3)a N p 2 b N p five term recurrence equation closed form : I. Goulden, D. Jackson (1997)
b N p ( = E Tr ( (X N ) 2p ) ), X N GOE recursion formula coupled with the moments a N p of the GUE b N p = (4N 2)b N p 1 + 4(2p 2)(2p 3)b N p 2 + a N p (4N 3)a N p 1 (2p 2)(2p 3)a N p 2 b N p five term recurrence equation closed form : I. Goulden, D. Jackson (1997) map enumeration : unoriented case
b N p ( = E Tr ( (X N ) 2p ) ), X N GOE recursion formula coupled with the moments a N p of the GUE b N p = (4N 2)b N p 1 + 4(2p 2)(2p 3)b N p 2 + a N p (4N 3)a N p 1 (2p 2)(2p 3)a N p 2 b N p five term recurrence equation closed form : I. Goulden, D. Jackson (1997) map enumeration : unoriented case duality with Symplectic Ensemble (β = 4)
recursion formulas lead to sharp moment bounds
recursion formulas lead to sharp moment bounds Gaussian Unitary Ensemble (GUE) three term recurrence equation
recursion formulas lead to sharp moment bounds Gaussian Unitary Ensemble (GUE) three term recurrence equation ( E Tr ( (X N ) 2p)) C (4N) p e Cp3 /N 2, p 3 N 2
recursion formulas lead to sharp moment bounds Gaussian Unitary Ensemble (GUE) three term recurrence equation ( E Tr ( (X N ) 2p)) C (4N) p e Cp3 /N 2, p 3 N 2 similar bounds for classical orthogonal polynomial ensembles (β = 2) Gaussian Orthogonal Ensemble (GOE) (β = 1)
non asymptotic small deviation inequalities
non asymptotic small deviation inequalities P ( λ N N 2 N + s N 1/6) F TW (s), s R
non asymptotic small deviation inequalities P ( λ N N 2 N + s N 1/6) F TW (s), s R C 1 e C s3/2 1 F TW (s) C e s3/2 /C (s )
non asymptotic small deviation inequalities P ( λ N N 2 N + s N 1/6) F TW (s), s R C 1 e C s3/2 1 F TW (s) C e s3/2 /C (s ) moment bounds (GUE, GOE) P ( λ N N 2 N + ε ) C e N1/4 ε 3/2 /C, 0 < ε N
non asymptotic small deviation inequalities P ( λ N N 2 N + s N 1/6) F TW (s), s R C 1 e C s3/2 1 F TW (s) C e s3/2 /C (s ) moment bounds (GUE, GOE) P ( λ N N 2 N + ε ) C e N1/4 ε 3/2 /C, 0 < ε N fits the Tracy-Widom asymptotics (ε = s N 1/6 )
non asymptotic small deviation inequalities P ( λ N N 2 N + s N 1/6) F TW (s), s R C 1 e C s3/2 1 F TW (s) C e s3/2 /C (s ) moment bounds (GUE, GOE) P ( λ N N 2 N + ε ) C e N1/4 ε 3/2 /C, 0 < ε N fits the Tracy-Widom asymptotics (ε = s N 1/6 ) deviation inequality on last passage percolation W N K. Johansson (2000)
moment comparison Wigner matrices (independent entries)
moment comparison Wigner matrices (independent entries) example : sign matrix Y N = Y N ij = ±1
moment comparison Wigner matrices (independent entries) example : sign matrix Y N = Y N ij = ±1 asymptotic result : Y. Sinai, A. Soshnikov (1998-99) P ( λ N N (Y ) 2 N + s N 1/6) F TW (s), s R
moment comparison Wigner matrices (independent entries) example : sign matrix Y N = Y N ij = ±1 asymptotic result : Y. Sinai, A. Soshnikov (1998-99) P ( λ N N (Y ) 2 N + s N 1/6) F TW (s), s R ( E Tr ( (Y N ) 2p)) ( E Tr ( (X N ) 2p)), X N GOE
moment comparison Wigner matrices (independent entries) example : sign matrix Y N = Y N ij = ±1 asymptotic result : Y. Sinai, A. Soshnikov (1998-99) P ( λ N N (Y ) 2 N + s N 1/6) F TW (s), s R ( E Tr ( (Y N ) 2p)) ( E Tr ( (X N ) 2p)), X N GOE P ( λ N N (Y ) 2 N + ε ) C e N1/4 ε 3/2 /C, 0 < ε N