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- Title
Enhanced algebraic substructuring for symmetric generalized eigenvalue problems.
- Authors
Kalantzis, Vassilis; Horesh, Lior
- Abstract
This article proposes a new substructuring algorithm to approximate the algebraically smallest eigenvalues and corresponding eigenvectors of a symmetric positive‐definite matrix pencil (A,M)$$ \left(A,M\right) $$. The proposed approach partitions the graph associated with (A,M)$$ \left(A,M\right) $$ into a number of algebraic substructures and builds a Rayleigh–Ritz projection subspace by combining spectral information associated with the interior and interface variables of the algebraic domain. The subspace associated with interior variables is built by computing substructural eigenvectors and truncated Neumann series expansions of resolvent matrices. The subspace associated with interface variables is built by computing eigenvectors and associated leading derivatives of linearized spectral Schur complements. The proposed algorithm can take advantage of multilevel partitionings when the size of the pencil. Experiments performed on problems stemming from discretizations of model problems showcase the efficiency of the proposed algorithm and verify that adding eigenvector derivatives can enhance the overall accuracy of the approximate eigenpairs, especially those associated with eigenvalues located near the origin.
- Subjects
MATRIX pencils; SCHUR complement; ALGEBRAIC numbers; SYMMETRIC matrices; MATRICES (Mathematics); EIGENVECTORS
- Publication
Numerical Linear Algebra with Applications, 2023, Vol 30, Issue 2, p1
- ISSN
1070-5325
- Publication type
Article
- DOI
10.1002/nla.2473