BIT Numerical Mathematics

, Volume 56, Issue 4, pp 1213–1236 | Cite as

Restarted Q-Arnoldi-type methods exploiting symmetry in quadratic eigenvalue problems

Article

Abstract

We investigate how to adapt the Q-Arnoldi method for the case of symmetric quadratic eigenvalue problems, that is, we are interested in computing a few eigenpairs \((\lambda ,x)\) of \((\lambda ^2M+\lambda C+K)x=0\) with MCK symmetric \(n\times n\) matrices. This problem has no particular structure, in the sense that eigenvalues can be complex or even defective. Still, symmetry of the matrices can be exploited to some extent. For this, we perform a symmetric linearization \(Ay=\lambda By\), where AB are symmetric \(2n\times 2n\) matrices but the pair (AB) is indefinite and hence standard Lanczos methods are not applicable. We implement a symmetric-indefinite Lanczos method and enrich it with a thick-restart technique. This method uses pseudo inner products induced by matrix B for the orthogonalization of vectors (indefinite Gram-Schmidt). The projected problem is also an indefinite matrix pair. The next step is to write a specialized, memory-efficient version that exploits the block structure of A and B, referring only to the original problem matrices MCK as in the Q-Arnoldi method. This results in what we have called the Q-Lanczos method. Furthermore, we define a stabilized variant analog of the TOAR method. We show results obtained with parallel implementations in SLEPc.

Keywords

Quadratic eigenvalue problem Pseudo-Lanczos Q-Arnoldi TOAR Thick-restart SLEPc 

Mathematics Subject Classification

65F15 15A18 65F50 

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Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  1. 1.Departament de Sistemes Informàtics i ComputacióUniversitat Politècnica de ValènciaValenciaSpain

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