Please use this identifier to cite or link to this item: https://doi.org/10.15480/882.63
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Publisher DOI: 10.1137/040616097
Title: An a priori bound for Automated Multi-Level Substructuring
Language: English
Authors: Voß, Heinrich 
Elssel, Kolja 
Keywords: Eigenvalues; AMLS; substructuring; nonlinear eigenproblem; minmax characterization
Issue Date: Sep-2004
Source: Preprint. Published in: SIAM. J. Matrix Anal. & Appl., 28.2006,2, 386–397
Abstract (german): 
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.
URI: http://tubdok.tub.tuhh.de/handle/11420/65
DOI: 10.15480/882.63
Institute: Mathematik E-10 
Document Type: Preprint
License: In Copyright In Copyright
Part of Series: Preprints des Institutes für Mathematik 
Volume number: 81
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