Journal
SIAM JOURNAL ON SCIENTIFIC COMPUTING
Volume 38, Issue 5, Pages S385-S411Publisher
SIAM PUBLICATIONS
DOI: 10.1137/15M1022458
Keywords
matrix polynomial; eigenvalues; companion linearization; Krylov subspace; nonmonomial bases; spectral transformation; parallel computing; SLEPc
Categories
Funding
- Spanish Ministry of Economy and Competitiveness [TIN2013-41049-P]
- Spanish Ministry of Education, Culture and Sport [AP2012-0608]
Ask authors/readers for more resources
Polynomial eigenvalue problems are often found in scientific computing applications. When the coefficient matrices of the polynomial are large and sparse, usually only a few eigenpairs are required and projection methods are the best choice. We focus on Krylov methods that operate on the companion linearization of the polynomial but exploit the block structure with the aim of being memory-efficient in the representation of the Krylov subspace basis. The problem may appear in the form of a low-degree polynomial (quartic or quintic, say) expressed in the monomial basis, or a high-degree polynomial (coming from interpolation of a nonlinear eigenproblem) expressed in a nonmonomial basis. We have implemented a parallel solver in SLEPc covering both cases that is able to compute exterior as well as interior eigenvalues via spectral transformation. We discuss important issues such as scaling and restart and illustrate the robustness and performance of the solver with some numerical experiments.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available