A note on eigenvector error bounds for polynomial eigenvalue problems
Abstract
The standard approach for finding eigenvalues and eigenvectors of matrix polynomials starts by embedding the coefficients of the polynomial into a matrix pencil, known as linearization. Building on the pioneering work of Nakatsukasa and Tisseur, we present error bounds for the computed eigenvectors of matrix polynomials. Our error bounds are applicable to any linearization satisfying two properties. First, eigenvectors of the original matrix polynomial can be recovered from those of the linearization without any arithmetic computation. Second, the linearization presents onesided factorizations, which relate the residual for the linearization with the residual for the polynomial. Linearizations satisfying these two properties include the family of block Kronecker linearizations. The error bounds imply that an eigenvector has been computed accurately when the residual norm is small, provided that the computed associated eigenvalue is wellseparated from the rest of the spectrum of the linearization. The theory is illustrated by numerical examples.
 Publication:

arXiv eprints
 Pub Date:
 August 2018
 DOI:
 10.48550/arXiv.1808.04012
 arXiv:
 arXiv:1808.04012
 Bibcode:
 2018arXiv180804012P
 Keywords:

 Mathematics  Numerical Analysis;
 15A18;
 15A22;
 65F15