Matrix Concentration Inequalities and Free Probability

The talk is based on the paper, Matrix Concentration Inequalities and Free Probability by Afonso S. Bandeira, March T. Boedihardjo, and, Ramon van Handel. arXiv link.

Abstract (from the paper)

A central tool in the study of nonhomogeneous random matrices, the noncommutative Khintchine inequality of Lust-Piquard and Pisier, yields a nonasymptotic bound on the spectral norm of general Gaussian random matrices \(X= \sum_i g_i A_i\) where \(g_i\) are independent standard Gaussian variables and \(A_i\) are matrix coefficients. This bound exhibits a logarithmic dependence on dimension that is sharp when the matrices \(A_i\) commute, but often proves to be suboptimal in the presence of noncommutativity. In this paper, we develop nonasymptotic bounds on the spectrum of arbitrary Gaussian random matrices that can capture noncommutativity. These bounds quantify the degree to which the deterministic matrices \(A_i\) behave as though they are freely independent. This “intrinsic freeness” phenomenon provides a powerful tool for the study of various questions that are outside the reach of classical methods of random matrix theory. Our nonasymptotic bounds are easily applicable in concrete situations, and yield sharp results in examples where the noncommutative Khintchine inequality is suboptimal. When combined with a linearization argument, our bounds imply strong asymptotic freeness (in the sense of Haagerup-Thorbjørnsen) for a remarkably general class of Gaussian random matrix models, including matrices that may be very sparse and that lack any special symmetries.
Beyond the Gaussian setting, we develop matrix concentration inequalities that capture noncommutativity for general sums of independent random matrices, which arise in many problems of pure and applied mathematics.