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An a priori bound for Automated Multi-Level Substructuring
Citation Link: https://doi.org/10.15480/882.63
Publikationstyp
Preprint
Date Issued
2004-09
Sprache
English
Author(s)
Voß, Heinrich
Institut
TORE-DOI
First published in
Preprints des Institutes für Mathematik;Bericht 81
Number in series
81
Citation
Preprint. Published in: SIAM. J. Matrix Anal. & Appl., 28.2006,2, 386–397
Publisher DOI
Scopus ID
The Automated Multi-Level Substructuring (AMLS) method has been developed to reduce the computational demands of frequency response analysis and has recently been proposed as an alternative to iterative projection methods like Lanczos or Jacobi–Davidson for computing a large number of eigenvalues for matrices of very large dimension. Based on Schur complements and modal approximations of submatrices on several levels AMLS constructs a projected eigenproblem which yields good approximations of eigenvalues at the lower end of the spectrum. Rewriting the original problem as a rational eigenproblem of the same dimension as the projected problem, and taking advantage of a minmax characterization for the rational eigenproblem we derive an a priori bound for the AMLS approximation of eigenvalues.
Subjects
Eigenvalues
AMLS
substructuring
nonlinear eigenproblem
minmax characterization
DDC Class
510: Mathematik
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