Randomized estimation of spectral densities of large matrices made accurate

TL;DR

This paper introduces a novel randomized method that leverages correlated information from multiple vectors and polynomial filtering to accurately estimate spectral densities of large matrices, surpassing previous accuracy limits.

Contribution

The paper presents a spectrum sweeping technique that overcomes the $ ext{O}(1/\sqrt{N_v})$ accuracy barrier by exploiting correlations in random vectors through polynomial filtering.

Findings

Achieves spectral density estimation with $ ext{O}(N^2)$ computational cost.

Significantly improves accuracy over existing randomized methods.

Enables accurate trace computation of smooth matrix functions.

Abstract

For a large Hermitian matrix $A\in \mathbb{C}^{N\times N}$, it is often the case that the only affordable operation is matrix-vector multiplication. In such case, randomized method is a powerful way to estimate the spectral density (or density of states) of $A$. However, randomized methods developed so far for estimating spectral densities only extract information from different random vectors independently, and the accuracy is therefore inherently limited to $\mathcal{O}(1/\sqrt{N_{v}})$ where $N_{v}$ is the number of random vectors. In this paper we demonstrate that the "$\mathcal{O}(1/\sqrt{N_{v}})$ barrier" can be overcome by taking advantage of the correlated information of random vectors when properly filtered by polynomials of $A$. Our method uses the fact that the estimation of the spectral density essentially requires the computation of the trace of a series of matrix functions that are numerically low rank. By repeatedly applying $A$ to the same set of random vectors and taking different linear combination of the results, we can sweep through the entire spectrum of $A$ by building such low rank decomposition at different parts of the spectrum. Under some assumptions, we demonstrate that a robust and efficient implementation of such spectrum sweeping method can compute the spectral density accurately with $\mathcal{O}(N^2)$ computational cost and $\mathcal{O}(N)$ memory cost. Numerical results indicate that the new method can significantly outperform existing randomized methods in terms of accuracy. As an application, we demonstrate a way to accurately compute a trace of a smooth matrix function, by carefully balancing the smoothness of the integrand and the regularized density of states using a deconvolution procedure.