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An algorithm for the complete solution of quadratic eigenvalue problems

Published: 03 May 2013 Publication History

Abstract

We develop a new algorithm for the computation of all the eigenvalues and optionally the right and left eigenvectors of dense quadratic matrix polynomials. It incorporates scaling of the problem parameters prior to the computation of eigenvalues, a choice of linearization with favorable conditioning and backward stability properties, and a preprocessing step that reveals and deflates the zero and infinite eigenvalues contributed by singular leading and trailing matrix coefficients. The algorithm is backward-stable for quadratics that are not too heavily damped. Numerical experiments show that our MATLAB implementation of the algorithm, quadeig, outperforms the MATLAB function polyeig in terms of both stability and efficiency.

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    Published In

    cover image ACM Transactions on Mathematical Software
    ACM Transactions on Mathematical Software  Volume 39, Issue 3
    April 2013
    149 pages
    ISSN:0098-3500
    EISSN:1557-7295
    DOI:10.1145/2450153
    Issue’s Table of Contents
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 03 May 2013
    Accepted: 01 September 2012
    Revised: 01 May 2012
    Received: 01 December 2011
    Published in TOMS Volume 39, Issue 3

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    Author Tags

    1. Quadratic eigenvalue problem
    2. backward error
    3. companion form
    4. condition number
    5. deflation
    6. eigenvector
    7. linearization
    8. scaling

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    • (2023)Regularized Normalization Methods for Solving Linear and Nonlinear Eigenvalue ProblemsMathematics10.3390/math1118399711:18(3997)Online publication date: 20-Sep-2023
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    • (2022)Strongly Minimal Self-Conjugate Linearizations for Polynomial and Rational MatricesSIAM Journal on Matrix Analysis and Applications10.1137/21M145354243:3(1354-1381)Online publication date: 1-Jan-2022
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