Randomized methods for computing joint eigenvalues, with applications to multiparameter eigenvalue problems and root finding

TL;DR

This paper introduces a randomized approach for numerically computing joint eigenvalues of nearly commuting matrices, improving accuracy and performance in solving multiparameter eigenvalue problems and polynomial systems.

Contribution

It proposes a simple randomized method using Rayleigh quotients for joint eigenvalue approximation, with analysis and numerical validation of its effectiveness.

Findings

Randomized methods accurately compute semisimple joint eigenvalues.

Approach enhances performance of existing eigenvalue solvers.

Numerical examples demonstrate robustness and efficiency.

Abstract

It is well known that a family of $n\times n$ commuting matrices can be simultaneously triangularized by a unitary similarity transformation. The diagonal entries of the triangular matrices define the $n$ joint eigenvalues of the family. In this work, we consider the task of numerically computing approximations to such joint eigenvalues for a family of (nearly) commuting matrices. This task arises, for example, in solvers for multiparameter eigenvalue problems and systems of multivariate polynomials, which are our main motivations. We propose and analyze a simple approach that computes eigenvalues as one-sided or two-sided Rayleigh quotients from eigenvectors of a random linear combination of the matrices in the family. We provide some analysis and numerous numerical examples, showing that such randomized approaches can compute semisimple joint eigenvalues accurately and lead to improved performance of existing solvers.