A Class of Asynchronous Parallel Multisplitting Relaxation Methods for Large Sparse Linear Complementarity Problems
Year: 2003
Author: Zhongzhi Bai, Yuguang Huang
Journal of Computational Mathematics, Vol. 21 (2003), Iss. 6 : pp. 773–790
Abstract
Asynchronous parallel multisplitting relaxation methods for solving large sparse linear complementarity problems are presented, and their convergence is proved when the system matrices are H-matrices having positive diagonal elements. Moreover, block and multi-parameter variants of the new methods, together with their convergence properties, are investigated in detail. Numerical results show that these new methods can achieve high parallel efficiency for solving the large sparse linear complementarity problems on multiprocessor systems.
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Journal Article Details
Publisher Name: Global Science Press
Language: English
DOI: https://doi.org/2003-JCM-10234
Journal of Computational Mathematics, Vol. 21 (2003), Iss. 6 : pp. 773–790
Published online: 2003-01
AMS Subject Headings:
Copyright: COPYRIGHT: © Global Science Press
Pages: 18
Keywords: Linear complementarity problem Matrix multisplitting Relaxation method Asynchronous iteration Convergence theory.