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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Bibliographic Details
Published in:Cybernetics and systems analysis Vol. 53; no. 6; pp. 937 - 949
Main Authors: Khimich, A. N., Popov, A. V., Chistyakov, O. V.
Format: Journal Article
Language:English
Published: New York Springer US 01.11.2017
Springer
Springer Nature B.V
Subjects:
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 Access:Get full text
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Summary: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.
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ISSN:1060-0396
1573-8337
DOI:10.1007/s10559-017-9996-5