Vector Spaces of Linearizations for Matrix Polynomials

Mackey, D. Steven and Mackey, Niloufer and Mehl, Christian and Mehrmann, Volker (2006) Vector Spaces of Linearizations for Matrix Polynomials. SIAM J. Matrix Anal. Appl., 28 (4). pp. 971-1004. ISSN 0895-4798

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Abstract

The classical approach to investigating polynomial eigenvalue problems is linearization, where the polynomial is converted into a larger matrix pencil with the same eigenvalues. For any polynomial there are innitely many linearizations with widely varying properties, but in practice the companion forms are typically used. However, these companion forms are not always entirely satisfactory, and linearizations with special properties may sometimes be required. Given a matrix polynomial P, we develop a systematic approach to generating large classes of linearizations for P. We show how to simply construct two vector spaces of pencils that generalize the companion forms of P, and prove that almost all of these pencils are linearizations for P. Eigenvectors of these pencils are shown to be closely related to those of P. A distinguished subspace is then isolated, and the special properties of these pencils are investigated. These spaces of pencils provide a convenient arena in which to look for structured linearizations of structured polynomials, as well as to try to optimize the conditioning of linearizations [7], [8], [12].

Item Type:	Article
Uncontrolled Keywords:	matrix polynomial, matrix pencil, linearization, strong linearization, shifted sum, companion form
Subjects:	MSC 2010, the AMS's Mathematics Subject Classification > 15 Linear and multilinear algebra; matrix theory MSC 2010, the AMS's Mathematics Subject Classification > 65 Numerical analysis
Depositing User:	Nick Higham
Date Deposited:	19 Dec 2006
Last Modified:	20 Oct 2017 14:12
URI:	https://eprints.maths.manchester.ac.uk/id/eprint/670

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• Vector Spaces of Linearizations for Matrix Polynomials. (deposited 01 Dec 2005)
  • Vector Spaces of Linearizations for Matrix Polynomials. (deposited 19 Dec 2006) [Currently Displayed]

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