Rates of convergence for empirical spectral measures: a soft approach

TL;DR

This paper introduces a flexible, soft approach using probabilistic and analytical tools to bound convergence rates of empirical spectral measures across various random matrix ensembles, including some new results.

Contribution

The paper presents a systematic, broadly applicable method for bounding convergence rates of empirical spectral measures, with new results for Wigner and Wishart matrices.

Findings

Established asymptotic almost sure convergence rates for multiple ensembles

Collected and unified existing results with some new details in key cases

Demonstrated the method's broad applicability across diverse random matrix models

Abstract

Understanding the limiting behavior of eigenvalues of random matrices is the central problem of random matrix theory. Classical limit results are known for many models, and there has been significant recent progress in obtaining more quantitative, non-asymptotic results. In this paper, we describe a systematic approach to bounding rates of convergence and proving tail inequalities for the empirical spectral measures of a wide variety of random matrix ensembles. We illustrate the approach by proving asymptotically almost sure rates of convergence of the empirical spectral measure in the following ensembles: Wigner matrices, Wishart matrices, Haar-distributed matrices from the compact classical groups, powers of Haar matrices, randomized sums and random compressions of Hermitian matrices, a random matrix model for the Hamiltonians of quantum spin glasses, and finally the complex Ginibre ensemble. Many of the results appeared previously and are being collected and described here as illustrations of the general method; however, some details (particularly in the Wigner and Wishart cases) are new. Our approach makes use of techniques from probability in Banach spaces, in particular concentration of measure and bounds for suprema of stochastic processes, in combination with more classical tools from matrix analysis, approximation theory, and Fourier analysis. It is highly flexible, as evidenced by the broad list of examples. It is moreover based largely on "soft" methods, and involves little hard analysis.