In many applications it is important to sensitivity of eigenvalues of a matrix polynomial polynomial. The sensitivity commonly is described pseudospectra. However, the determination of pseudospectra of matrix polynomials is very demanding computationally. This paper describes a new approach to computing approximations of pseudospectra of matrix polynomials by using rank-one or projected rank-one perturbations. These perturbations are inspired by Wilkinson's analysis of eigenvalue sensitivity. This approach allows the approximation of both structured and unstructured pseudospectra. Computed examples show the method to perform much better than a method based on random rank-one perturbations both for the approximation of structured and unstructured (i.e., standard) polynomial pseudospectra.
Computing unstructured and structured polynomial pseudospectrum approximations / Noschese, Silvia; Reichel, Lothar. - In: JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS. - ISSN 0377-0427. - 350:(2019), pp. 57-68. [10.1016/j.cam.2018.09.033]
Computing unstructured and structured polynomial pseudospectrum approximations
Silvia Noschese
;
2019
Abstract
In many applications it is important to sensitivity of eigenvalues of a matrix polynomial polynomial. The sensitivity commonly is described pseudospectra. However, the determination of pseudospectra of matrix polynomials is very demanding computationally. This paper describes a new approach to computing approximations of pseudospectra of matrix polynomials by using rank-one or projected rank-one perturbations. These perturbations are inspired by Wilkinson's analysis of eigenvalue sensitivity. This approach allows the approximation of both structured and unstructured pseudospectra. Computed examples show the method to perform much better than a method based on random rank-one perturbations both for the approximation of structured and unstructured (i.e., standard) polynomial pseudospectra.File | Dimensione | Formato | |
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Noschese_Computing-unstructured_2019.pdf
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