Lower bounds for the smallest singular value of structured random matrices

TL;DR

This paper establishes lower bounds on the smallest singular value of structured random matrices with independent entries, extending previous results to more general distributions and matrix structures using novel combinatorial and linear algebraic tools.

Contribution

It introduces new polynomial bounds for the smallest singular value of structured matrices with non-identical distributions, utilizing Szemerédi's Regularity Lemma and the Restricted Invertibility Theorem.

Findings

Polynomial bounds on the smallest singular value for matrices with bounded entries in A and scaled diagonal B.

Extension of Rudelson and Zeitouni's results to non-Gaussian distributions under moment conditions.

Application of combinatorial and linear algebraic tools in random matrix theory.

Abstract

We obtain lower tail estimates for the smallest singular value of random matrices with independent but non-identically distributed entries. Specifically, we consider $n\times n$ matrices with complex entries of the form \[ M = A\circ X + B = (a_{ij}\xi_{ij} + b_{ij}) \] where $X=(\xi_{ij})$ has iid centered entries of unit variance and $A$ and $B$ are fixed matrices. In our main result we obtain polynomial bounds on the smallest singular value of $M$ for the case that $A$ has bounded (possibly zero) entries, and $B= Z\sqrt{n}$ where $Z$ is a diagonal matrix with entries bounded away from zero. As a byproduct of our methods we can also handle general perturbations $B$ under additional hypotheses on $A$, which translate to connectivity hypotheses on an associated graph. In particular, we extend a result of Rudelson and Zeitouni for Gaussian matrices to allow for general entry distributions satisfying some moment hypotheses. Our proofs make use of tools which (to our knowledge) were previously unexploited in random matrix theory, in particular Szemer\'edi's Regularity Lemma, and a version of the Restricted Invertibility Theorem due to Spielman and Srivastava.