Hybrid Algorithms for Solving the Algebraic Eigenvalue Problem with Sparse Matrices

Hybrid algorithms for solving the partial generalized eigenvalue problem for symmetric positive definite sparse matrices of different structures by hybrid computers with graphic processors are proposed, coefficients for the efficiency of the algorithms are obtained, and approbation of the developed...

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Veröffentlicht in:Cybernetics and systems analysis Jg. 53; H. 6; S. 937 - 949
Hauptverfasser: Khimich, A. N., Popov, A. V., Chistyakov, O. V.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.11.2017
Springer
Springer Nature B.V
Schlagworte:
Algorithms
Analysis
Artificial Intelligence
Control
Eigenvalues
Hybrid computers
Mathematical analysis
Mathematics
Mathematics and Statistics
Matrix methods
Processor Architectures
Processors
Software Engineering/Programming and Operating Systems
Software–Hardware Systems
Sparse matrices
Sparsity
Systems Theory
computer of hybrid architecture
conjugate gradient methods
algebraic eigenvalue problem
efficiency of parallel algorithms
hybrid algorithm
subspace iteration method
ISSN:1060-0396, 1573-8337
Online-Zugang:Volltext
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Zusammenfassung:Hybrid algorithms for solving the partial generalized eigenvalue problem for symmetric positive definite sparse matrices of different structures by hybrid computers with graphic processors are proposed, coefficients for the efficiency of the algorithms are obtained, and approbation of the developed algorithms for test and practical problems is carried out.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:1060-0396
1573-8337
DOI:10.1007/s10559-017-9996-5