Sharp error bounds for approximate eigenvalues and singular values from subspace methods

TL;DR

This paper derives sharp quadratic error bounds for approximate eigenvalues and singular values obtained via subspace methods, improving robustness and applicability to clustered eigenvalues and various computational algorithms.

Contribution

It introduces novel quadratic error bounds for Ritz pairs, leveraging the structure of the perturbation matrix, and extends these bounds to singular values with demonstrated sharpness.

Findings

Bounds are robust to clustered Ritz values.

Bounds are asymptotically sharp.

Application to Krylov and randomized algorithms shows effectiveness.

Abstract

Subspace methods are commonly used for finding approximate eigenvalues and singular values of large-scale matrices. Once a subspace is found, the Rayleigh-Ritz method (for symmetric eigenvalue problems) and Petrov-Galerkin projection (for singular values) are the de facto method for extraction of eigenvalues and singular values. In this work we derive quadratic error bounds for approximate eigenvalues of symmetric matrices obtained via the Rayleigh-Ritz process. Our bounds take advantage of the fact that extremal eigenpairs tend to converge faster than the rest, hence having smaller residuals $\|A\widehat x_i-\theta_i\widehat x_i\|_2$, where $(\theta_i,\widehat x_i)$ is a Ritz pair (approximate eigenpair). The proof uses the structure of the perturbation matrix underlying the Rayleigh-Ritz method to bound the components of its eigenvectors. In this way, we obtain a bound of the form $c\frac{\|A\widehat x_i-\theta_i\widehat x_i\|_2^2}{\mbox{Gap}_i}$, where $\mbox{Gap}_i$ is roughly the gap between the $i$th Ritz value and the eigenvalues that are not approximated by the Ritz process, and $c> 1$ is a modest scalar. Our bound is adapted to each Ritz value and is robust to clustered Ritz values, which is a key improvement over existing results. We further show that the bound is asymptotically sharp, and generalize it to singular values of arbitrary real matrices. Finally, we apply these bounds to several methods for computing eigenvalues and singular values, and illustrate the sharpness of our bounds in a number of computational settings, including Krylov methods and randomized algorithms.