Regularization of non-normal matrices by Gaussian noise - the banded Toeplitz and twisted Toeplitz cases

TL;DR

This paper studies the eigenvalue distribution of large deterministic matrices perturbed by Gaussian noise, establishing a deterministic equivalence and analyzing specific Toeplitz and twisted Toeplitz cases.

Contribution

It provides a general deterministic equivalence theorem linking eigenvalue measures to singular values and computes limits for Toeplitz matrices with finite symbols.

Findings

Eigenvalue measures converge to the law of a sum of scaled powers of a uniform variable.

Explicit limit laws are derived for Toeplitz matrices with finite symbols.

Results extend to twisted Toeplitz matrices and matrices with i.i.d. diagonal entries.

Abstract

We consider the spectrum of additive, polynomially vanishing random perturbations of deterministic matrices, as follows. Let $M_N$ be a deterministic $N\times N$ matrix, and let $G_N$ be a complex Ginibre matrix. We consider the matrix $\mathcal{M}_N=M_N+N^{-\gamma}G_N$, where $\gamma>1/2$. With $L_N$ the empirical measure of eigenvalues of $\mathcal{M}_N$, we provide a general deterministic equivalence theorem that ties $L_N$ to the singular values of $z-M_N$, with $z\in \mathbb{C}$. We then compute the limit of $L_N$ when $M_N$ is an upper triangular Toeplitz matrix of finite symbol: if $M_N=\sum_{i=0}^{\mathfrak{d}} a_i J^i$ where $\mathfrak{d}$ is fixed, $a_i\in\mathcal{C}$ are deterministic scalars and $J$ is the nilpotent matrix $J(i,j)={\bf 1}_{j=i+1}$, then $L_N$ converges, as $N\to\infty$, to the law of $\sum_{i=0}^{\mathfrak{d}} a_i U^i$ where $U$ is a uniform random variable on the unit circle in the complex plane. We also consider the case of slowly varying diagonals (twisted Toeplitz matrices), and, when $\mathfrak{d}=1$, also of i.i.d.~entries on the diagonals in $M_N$.