Khimich A.N., Popov A.V., Chistyakov O.V.
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 algorithms for test and practical problems is carried out. © 2017, Springer Science+Business Media, LLC, part of Springer Nature.

Keywords: algebraic eigenvalue problem, computer of hybrid architecture, conjugate gradient methods, efficiency of parallel algorithms, hybrid algorithm, subspace iteration method, Algebra, Conjugate gradient method, Efficiency, Hybrid computers, Iterative methods, Matrix algebra, Algebraic eigenvalue problems, Different structure, Generalized eigenvalue problems, Graphic processors, Hybrid algorithms, Hybrid architectures, Subspace iteration method, Symmetric positive definite, Eigenvalues and eigenfunctions

Cite:
Khimich A.N., Popov A.V., Chistyakov O.V. (2017). Hybrid algorithms for solving the algebraic eigenvalue problem with sparse matrices. Cybernetics and Systems Analysis, 53 (6), 132-146. doi: https://doi.org/10.1007/s10559-017-9996-5 http://jnas.nbuv.gov.ua/article/UJRN-0000782708 [In Russian].