Kronecker-product random matrices and a matrix least squares problem

TL;DR

This paper analyzes the eigenvalue distribution and resolvent of a Kronecker-product random matrix model involving Wigner matrices, providing approximations, sharp estimates, and implications for a related matrix least squares problem.

Contribution

It introduces a detailed spectral analysis of Kronecker-product random matrices with Wigner components and applies findings to characterize solutions of a matrix least squares problem.

Findings

Quantitative approximation of the Stieltjes transform by a free operator

Sharp operator norm estimates for resolvent blocks

Asymptotic characterization of the least squares minimizer

Abstract

We study the eigenvalue distribution and resolvent of a Kronecker-product random matrix model $A \otimes I_{n \times n}+I_{n \times n} \otimes B+\Theta \otimes \Xi \in \mathbb{C}^{n^2 \times n^2}$, where $A,B$ are independent Wigner matrices and $\Theta,\Xi$ are deterministic and diagonal. For fixed spectral arguments, we establish a quantitative approximation for the Stieltjes transform by that of an approximating free operator, and a diagonal deterministic equivalent approximation for the resolvent. We further obtain sharp estimates in operator norm for the $n \times n$ resolvent blocks, and show that off-diagonal resolvent entries fall on two differing scales of $n^{-1/2}$ and $n^{-1}$ depending on their locations in the Kronecker structure. Our study is motivated by consideration of a matrix-valued least-squares optimization problem $\min_{X \in \mathbb{R}^{n \times n}} \frac{1}{2}\|XA+BX\|_F^2+\frac{1}{2}\sum_{ij} \xi_i\theta_j x_{ij}^2$ subject to a linear constraint. For random instances of this problem defined by Wigner inputs $A,B$, our analyses imply an asymptotic characterization of the minimizer $X$ and its associated minimum objective value as $n \to \infty$.