Fast and backward stable computation of the eigenvalues and eigenvectors of matrix polynomials

TL;DR

This paper introduces a fast, backward stable algorithm for computing eigenvalues and eigenvectors of matrix polynomials using a factored Schur form of the companion pencil, combining stability with efficiency.

Contribution

A novel quadratic-in-degree, cubic-in-size algorithm for eigenvalue and eigenvector computation of matrix polynomials using a structured factorization approach.

Findings

Algorithm is backward stable after proper scaling.

Numerical experiments confirm stability and efficiency.

Eigenvectors can be computed via Schur form reordering.

Abstract

In the last decade matrix polynomials have been investigated with the primary focus on adequate linearizations and good scaling techniques for computing their eigenvalues and eigenvectors. In this article we propose a new method for computing a factored Schur form of the associated companion pencil. The algorithm has a quadratic cost in the degree of the polynomial and a cubic one in the size of the coefficient matrices. Also the eigenvectors can be computed at the same cost. The algorithm is a variant of Francis's implicitly shifted QR algorithm applied on the companion pencil. A preprocessing unitary equivalence is executed on the matrix polynomial to simultaneously bring the leading matrix coefficient and the constant matrix term to triangular form before forming the companion pencil. The resulting structure allows us to stably factor each matrix of the pencil as a product of $k$ matrices of unitary-plus-rank-one form, admitting cheap and numerically reliable storage. The problem is then solved as a product core chasing eigenvalue problem. A backward error analysis is included, implying normwise backward stability after a proper scaling. Computing the eigenvectors via reordering the Schur form is discussed as well. Numerical experiments illustrate stability and efficiency of the proposed methods.