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A parallel multisplitting method with self-adaptive weightings for solving H-matrix linear systems

In this paper, a parallel multisplitting iterative method with the self-adaptive weighting matrices is presented for the linear system of equations when the coefficient matrix is an H-matrix. The zero pattern in weighting matrices is determined in advance, while the non-zero entries of weighting mat...

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Detalles Bibliográficos
Autores principales: Wen, Ruiping, Duan, Hui
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5411408/
https://www.ncbi.nlm.nih.gov/pubmed/28529432
http://dx.doi.org/10.1186/s13660-017-1370-7
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author Wen, Ruiping
Duan, Hui
author_facet Wen, Ruiping
Duan, Hui
author_sort Wen, Ruiping
collection PubMed
description In this paper, a parallel multisplitting iterative method with the self-adaptive weighting matrices is presented for the linear system of equations when the coefficient matrix is an H-matrix. The zero pattern in weighting matrices is determined in advance, while the non-zero entries of weighting matrices are determined by finding the optimal solution in a hyperplane of α points generated by the parallel multisplitting iterations. Especially, the nonnegative restriction of weighting matrices is released. The convergence theory is established for the parallel multisplitting method with self-adaptive weightings. Finally, a numerical example shows that the parallel multisplitting iterative method with the self-adaptive weighting matrices is effective.
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spelling pubmed-54114082017-05-18 A parallel multisplitting method with self-adaptive weightings for solving H-matrix linear systems Wen, Ruiping Duan, Hui J Inequal Appl Research In this paper, a parallel multisplitting iterative method with the self-adaptive weighting matrices is presented for the linear system of equations when the coefficient matrix is an H-matrix. The zero pattern in weighting matrices is determined in advance, while the non-zero entries of weighting matrices are determined by finding the optimal solution in a hyperplane of α points generated by the parallel multisplitting iterations. Especially, the nonnegative restriction of weighting matrices is released. The convergence theory is established for the parallel multisplitting method with self-adaptive weightings. Finally, a numerical example shows that the parallel multisplitting iterative method with the self-adaptive weighting matrices is effective. Springer International Publishing 2017-05-01 2017 /pmc/articles/PMC5411408/ /pubmed/28529432 http://dx.doi.org/10.1186/s13660-017-1370-7 Text en © The Author(s) 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
spellingShingle Research
Wen, Ruiping
Duan, Hui
A parallel multisplitting method with self-adaptive weightings for solving H-matrix linear systems
title A parallel multisplitting method with self-adaptive weightings for solving H-matrix linear systems
title_full A parallel multisplitting method with self-adaptive weightings for solving H-matrix linear systems
title_fullStr A parallel multisplitting method with self-adaptive weightings for solving H-matrix linear systems
title_full_unstemmed A parallel multisplitting method with self-adaptive weightings for solving H-matrix linear systems
title_short A parallel multisplitting method with self-adaptive weightings for solving H-matrix linear systems
title_sort parallel multisplitting method with self-adaptive weightings for solving h-matrix linear systems
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5411408/
https://www.ncbi.nlm.nih.gov/pubmed/28529432
http://dx.doi.org/10.1186/s13660-017-1370-7
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