1. The Random Matrix Technique of Ghosts and Shadows Alan Edelman Dept of Mathematics Computer Science and AI Laboratories Massachusetts Institute of Technology (with thanks to Plamen Koev)

2. Short followed by the Movie Some interesting computational techniques – Random Matrix Theory = Theorems, Applications, and Software A new application can be more valuable than a theorem! A well crafted experiment or package is not a theorem but it can be as important or even more to the field! The Main Show: The Method of Ghosts and Shadows in Random Matrix Theory – Yet another nail in the threefold way “coffin”

3. Semi-Circle Law Naïve Way: MATLAB®: A=randn(n); S=(A+A’)/sqrt(2*n);eig(S) R: A=matrix(rnorm(n*n),ncol=n);S=(a+t(a))/sqrt(2*n);eigen(S,symmetric=T,only.values=T)$values; Mathematica: A=RandomArray[NormalDistribution[],{n,n}];S=(A+Transpose[A])/Sqrt[n];Eigenvalues[s]

4. Compute All the Eigenvalues Sym Tridiagonal: β=1:real, β=2:complex, β=4:quaternion, β=2½? Diagonals N(0,2), Off-diagonals “chi” random-variables : N=2000: 12 seconds vs. 0.2 seconds (factor of 60!!) (Dumitriu & E: 2002)

5. Histogram without Histogramming: Sturm Sequences Count #eigs < 0.5: Count sign changes in Det (A-0.5*I)[1:k,1:k] Count #eigs in [x,x+h] Take difference in number of sign changes at x+h and x Mentioned in Dumitriu and E 2006, Used theoretically in Albrecht, Chan, and E 2008

6. A good computational trick is a good theoretical trick! Finite Semi-Circle Laws for Any Beta! Finite Tracy-Widom Laws for Any Beta!

7. Stochastic Differential Eigen-Equations Tridiagonal Models Suggest SDE’s with Brownian motion as infinite limit: – E: 2002 – E and Sutton 2005, 2007 – Brian Rider and (Ramirez, Cambronero, Virag, etc.) (Lots of beautiful results!) – (Not today’s talk)

8. “Tracy-Widom” Computations Eigenvalues without the whole matrix! E: 2003 Persson and E: 2005 Never construct the entire tridiagonal matrix! Just say the upper 10n 1/3 by 10n 1/3 Compute largest eigenvalue of that, perhaps using Lanczos with shift and invert strategy! Can compute for n amazingly large!!!!! And any beta.

9. Other Computational Results Infinite Random Matrix Theory – The Free Probability Calculator (Raj) Finite Random Matrix Theory – MOPS (Ioana Dumitriu) (β: orthogonal polynomials) – Hypergeometrics of Matrix Argument (Plamen Koev) (β: distribution functions for finite stats such as the finite Tracy-Widom laws for Laguerre) Good stuff, but not today.

10. Ghosts and Shadows

11. Scary Ideas in Mathematics Zero Negative Radical Irrational Imaginary Ghosts: Something like a commutative algebra of random variables that generalizes Reals, Complexes, and Quaternions and inspires theoretical results and numerical computation

12. RMT Densities Hermite: c ∏|λ i - λ j | β e -∑λ i 2 /2 (Gaussian Ensemble) Laguerre: c ∏|λ i -λ j | β ∏λ i m e -∑λ i (Wishart Matrices) Jacobi: c ∏|λ i -λ j | β ∏λ i m 1 ∏(1-λ i ) m 2 (Manova Matrices) Fourier: c ∏|λ i -λ j | β (on the complex unit circle) “Jack Polynomials” Traditional Story: Count the real parameters β=1,2,4 for real, complex, quaternion Applications: β=1: All of Multivariate Statistics β=2:parked cars in London, wireless networks β=4: There and almost nobody cares Dyson 1962 “Threefold Way” Three Division Rings

13. β-Ghosts β=1 has one real Gaussian (G) β=2 has two real Gaussians (G+iG) β=4 has four real Gaussians (G+iG+jG+kG) β≥1 has one real part and (β-1) “Ghost” parts

14. Introductory Theory There is an advanced theory emerging (some other day) Informally: – A β-ghost is a spherically symmetric random variable defined on R β – A shadow is a derived real or complex quantity

15. Goals Continuum of Haar Measureas generalizing orthogonal, unitary, symplectic New Definition of Jack Polynomials generalizing the zonals Computations! E.g. Moments of the Haar Measures Place finite random matrix theory “β”into same framework as infinite random matrix theory: specifically β as a knob to turn down the randomness, e.g. Airy Kernel –d 2 /dx 2 +x+(2/β ½ )dW  White Noise

16. Formally Let S n =2π/Γ(n/2)=“suface area of sphere” Defined at any n= β>0. A β-ghost x is formally defined by a function f x (r) such that ∫ ∞ f x (r) r β-1 S β-1 dr=1. Note: For β integer, the x can be realized as a random spherically symmetric variable in β dimensions Example: A β-normal ghost is defined by f(r)=(2π) -β/2 e - r 2 /2 Example: Zero is defined with constant*δ(r). Can we do algebra? Can we do linear algebra? Can we add? Can we multiply? r=0

17. A few more operations ΙΙxΙΙ is a real random variable whose density is given by f x (r) (x+x’)/2 is real random variable given by multiplying ΙΙxΙΙ by a beta distributed random variable representing a coordinate on the sphere

18. Representations I’ve tried a few on for size. My favorite right now is – A complex number z with ΙΙzΙΙ, the radius and Re(z), the real part. Addition of Independent Ghosts: Addition returns a spherically symmetric object Have an integral formula Prefer: Add the real part, imaginary part completed to keep spherical symmetry

19. Multiplication of Independent Ghosts Just multiply ΙΙzΙΙ’s and plug in spherical symmetry Multiplication is commutative – (Important Example: Quaternions don’t commute, but spherically symmetric random variables do!)

20. Shadow Example Given a ghost Gaussian, G β, the length is a real chi-beta variable χ β variable.

21. Linear Algebra Example Given a ghost Gaussian, G β, the length is a real chi-beta variable χ β variable. Gram-Schmidt:    or = [Q] * GβGβ GβGβ GβGβ GβGβ GβGβ GβGβ GβGβ GβGβ GβGβ χ3βχ3β GβGβ GβGβ GβGβ GβGβ GβGβ GβGβ χ3βχ3β GβGβ GβGβ χ 2β GβGβ GβGβ χ3βχ3β GβGβ GβGβ GβGβ ΧβΧβ H3H3 H2H2 H1H1 GβGβ GβGβ GβGβ GβGβ GβGβ GβGβ GβGβ GβGβ GβGβ χ3βχ3β GβGβ GβGβ GβGβ ΧβΧβ Q has β - Haar Measure! We have computed moments! (more later)

22. Tridiagonalizing Example GβGβ GβGβ GβGβ …GβGβ GβGβ GβGβ GβGβ …GβGβ GβGβ GβGβ GβGβ …GβGβ GβGβ GβGβ GβGβ GβGβ GβGβ  Symmetric Part of

23. Understanding ∏|λ i -λ j | β Define volume element (dx)^ by (r dx)^=r β (dx)^ (β-dim volume, like fractals, but don’t really see any fractal theory here) Jacobians: A=QΛQ’ (Sym Eigendecomposition) Q’dAQ=dΛ+(Q’dQ)Λ- Λ(Q’dQ) (dA)^=(Q’dAQ)^= diagonal ^ strictly-upper diagonal = ∏dλ i =(dΛ)^ off-diag = ∏ ( (Q’dQ) ij (λ i -λ j ) ) ^=(Q’dQ)^ ∏|λ i -λ j | β

24. Haar Measure β=1: E Q (trace(AQBQ’) k )=∑C κ (A)C κ (B)/C κ (I) Forward Method: Suppose you know C κ ’s a-priori. (Jack Polynomials!) Let A and B be diagonal indeterminants (Think Generating Functions) Then can formally obtain moments of Q: Example: E(|q 11 | 2 |q 22 | 2 ) = (n+α-1)/(n(n-1)(n+α)) α:=2/ β Can Gram-Schmidt the ghosts. Same answers coming up!

25. Further Uses of Ghosts Multivariate Othogonal Polynomials Largest Eigenvalues/Smallest Eigenvalues Noncentral Distributions Expect Lots of Uses to be discovered…