Foundations of Computational Mathematics

, Volume 8, Issue 6, pp 649–702 | Cite as

The Polynomial Method for Random Matrices

Article

Abstract

We define a class of “algebraic” random matrices. These are random matrices for which the Stieltjes transform of the limiting eigenvalue distribution function is algebraic, i.e., it satisfies a (bivariate) polynomial equation. The Wigner and Wishart matrices whose limiting eigenvalue distributions are given by the semicircle law and the Marčenko–Pastur law are special cases.

Algebraicity of a random matrix sequence is shown to act as a certificate of the computability of the limiting eigenvalue density function. The limiting moments of algebraic random matrix sequences, when they exist, are shown to satisfy a finite depth linear recursion so that they may often be efficiently enumerated in closed form.

In this article, we develop the mathematics of the polynomial method which allows us to describe the class of algebraic matrices by its generators and map the constructive approach we employ when proving algebraicity into a software implementation that is available for download in the form of the RMTool random matrix “calculator” package. Our characterization of the closure of algebraic probability distributions under free additive and multiplicative convolution operations allows us to simultaneously establish a framework for computational (noncommutative) “free probability” theory. We hope that the tools developed allow researchers to finally harness the power of infinite random matrix theory.

Keywords

Random matrices Stochastic eigenanalysis Free probability Algebraic functions Resultants D-finite series 

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Copyright information

© SFoCM 2007

Authors and Affiliations

  1. 1.MIT Department of Electrical Engineering and Computer ScienceCambridgeUSA
  2. 2.MIT Department of MathematicsCambridgeUSA

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