Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review
Parallel combined chebyshev and least squares iterations in the krylov subspaces. / Gurieva, Yana; Il’in, Valery.
Parallel Computational Technologies - 14th International Conference, PCT 2020, Revised Selected Papers. ed. / Leonid Sokolinsky; Mikhail Zymbler. Springer Gabler, 2020. p. 162-177 (Communications in Computer and Information Science; Vol. 1263 CCIS).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review
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TY - GEN
T1 - Parallel combined chebyshev and least squares iterations in the krylov subspaces
AU - Gurieva, Yana
AU - Il’in, Valery
N1 - Publisher Copyright: © Springer Nature Switzerland AG 2020.
PY - 2020/1/1
Y1 - 2020/1/1
N2 - The combined Chebyshev−Least Squares iterative processes in the Krylov subspaces to solve symmetric and non-symmetric systems of linear algebraic equations (SLAEs) are proposed. This approach is a generalization of the Anderson acceleration of the Jacobi iterative method as an efficient alternative to the Krylov methods. The algorithms proposed are based on constructing some basis of the Krylov subspaces and a minimization of the residual vector norm by means of the least squares procedure. The general process includes periodical restarts and can be considered to be an implicit implementation of the Krylov procedure which can be efficiently parallelized. A comparative analysis of the methods proposed and the classic Krylov approaches is presented. A parallel implementation of the iterative methods on multi-processor computer systems is discussed. The efficiency of the algorithms is demonstrated via the results of numerical experiments on a set of model SLAEs.
AB - The combined Chebyshev−Least Squares iterative processes in the Krylov subspaces to solve symmetric and non-symmetric systems of linear algebraic equations (SLAEs) are proposed. This approach is a generalization of the Anderson acceleration of the Jacobi iterative method as an efficient alternative to the Krylov methods. The algorithms proposed are based on constructing some basis of the Krylov subspaces and a minimization of the residual vector norm by means of the least squares procedure. The general process includes periodical restarts and can be considered to be an implicit implementation of the Krylov procedure which can be efficiently parallelized. A comparative analysis of the methods proposed and the classic Krylov approaches is presented. A parallel implementation of the iterative methods on multi-processor computer systems is discussed. The efficiency of the algorithms is demonstrated via the results of numerical experiments on a set of model SLAEs.
KW - Anderson acceleration
KW - Chebyshev iterative algorithms
KW - Convergence of iterations
KW - Krylov subspaces
KW - Least squares
KW - Numerical experiments
KW - Numerical stability
UR - http://www.scopus.com/inward/record.url?scp=85089317012&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-55326-5_12
DO - 10.1007/978-3-030-55326-5_12
M3 - Conference contribution
AN - SCOPUS:85089317012
SN - 9783030553258
T3 - Communications in Computer and Information Science
SP - 162
EP - 177
BT - Parallel Computational Technologies - 14th International Conference, PCT 2020, Revised Selected Papers
A2 - Sokolinsky, Leonid
A2 - Zymbler, Mikhail
PB - Springer Gabler
T2 - 14th International Scientific Conference on Parallel Computational Technologies, PCT 2020
Y2 - 27 May 2020 through 29 May 2020
ER -
ID: 24954840