The smallest singular value of inhomogeneous square random matrices

TL;DR

This paper establishes bounds on the smallest singular value of inhomogeneous square random matrices with independent entries, extending prior results by removing assumptions of identical distribution and mean zero, using a novel anti-concentration technique.

Contribution

Introduces the Randomized Least Common Denominator (RLCD) to analyze anti-concentration in non-i.i.d. matrices, extending singular value bounds to inhomogeneous matrices.

Findings

Bounds on smallest singular value with high probability

RLCD effectively handles non-i.i.d. entries

Constructs nets with lattice structure for analysis

Abstract

We show that for an $n\times n$ random matrix $A$ with independent uniformly anti-concentrated entries, such that $\mathbb{E} ||A||^2_{HS}\leq K n^2$, the smallest singular value $\sigma_n(A)$ of $A$ satisfies $$ P\left( \sigma_n(A)\leq \frac{\varepsilon}{\sqrt{n}} \right) \leq C\varepsilon+2e^{-cn},\quad \varepsilon \ge 0. $$ This extends earlier results of Rudelson and Vershynin, and Rebrova and Tikhomirov by removing the assumption of mean zero and identical distribution of the entries across the matrix, as well as the recent result of Livshyts, where the matrix was required to have i.i.d. rows. Our model covers "inhomogeneus" matrices allowing different variances of the entries, as long as the sum of the second moments is of order $O(n^2)$. In the past advances, the assumption of i.i.d. rows was required due to lack of Littlewood--Offord--type inequalities for weighted sums of non-i.i.d. random variables. Here, we overcome this problem by introducing the Randomized Least Common Denominator (RLCD) which allows to study anti-concentration properties of weighted sums of independent but not identically distributed variables. We construct efficient nets on the sphere with lattice structure, and show that the lattice points typically have large RLCD. This allows us to derive strong anti-concentration properties for the distance between a fixed column of $A$ and the linear span of the remaining columns, and prove the main result.