Volume 20, Issue 6
Experimental Study of the Asynchronous Multisplitting Relaxation Methods for the Linear Complementarity Problems

Zhong Zhi Bai

DOI:

J. Comp. Math., 20 (2002), pp. 561-574

Published online: 2002-12

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  • Abstract

We study the numerical behaviours of the relaxed asynchronous multisplitting methods for the linear complementarity problems by solving some typical problems from practical applications on a real multiprocessor system. Numerical results show that the parallel multisplitting relaxation methods always perform much better than the corresponding sequential alternatives, and that the asynchronous multisplitting relaxation methods often outperform their corresponding synchronous counterparts. Moreover, the two-sweep relaxed multisplitting methods have better convergence properties than their corresponding one-sweep relaxed ones in the sense that they have larger convergence domains and faster convergence speeds. Hence, the asynchronous multisplitting unsymmetric relaxation iterations should be the methods of choice for solving the large sparse linear complementarity problems in the parallel computing environments.

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Linear complementarity problem Matrix multisplitting Asynchronous iterative methods Numerical experiments

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@Article{JCM-20-561, author = {}, title = {Experimental Study of the Asynchronous Multisplitting Relaxation Methods for the Linear Complementarity Problems}, journal = {Journal of Computational Mathematics}, year = {2002}, volume = {20}, number = {6}, pages = {561--574}, abstract = { We study the numerical behaviours of the relaxed asynchronous multisplitting methods for the linear complementarity problems by solving some typical problems from practical applications on a real multiprocessor system. Numerical results show that the parallel multisplitting relaxation methods always perform much better than the corresponding sequential alternatives, and that the asynchronous multisplitting relaxation methods often outperform their corresponding synchronous counterparts. Moreover, the two-sweep relaxed multisplitting methods have better convergence properties than their corresponding one-sweep relaxed ones in the sense that they have larger convergence domains and faster convergence speeds. Hence, the asynchronous multisplitting unsymmetric relaxation iterations should be the methods of choice for solving the large sparse linear complementarity problems in the parallel computing environments. }, issn = {1991-7139}, doi = {https://doi.org/}, url = {http://global-sci.org/intro/article_detail/jcm/8941.html} }
TY - JOUR T1 - Experimental Study of the Asynchronous Multisplitting Relaxation Methods for the Linear Complementarity Problems JO - Journal of Computational Mathematics VL - 6 SP - 561 EP - 574 PY - 2002 DA - 2002/12 SN - 20 DO - http://doi.org/ UR - https://global-sci.org/intro/article_detail/jcm/8941.html KW - Linear complementarity problem KW - Matrix multisplitting KW - Asynchronous iterative methods KW - Numerical experiments AB - We study the numerical behaviours of the relaxed asynchronous multisplitting methods for the linear complementarity problems by solving some typical problems from practical applications on a real multiprocessor system. Numerical results show that the parallel multisplitting relaxation methods always perform much better than the corresponding sequential alternatives, and that the asynchronous multisplitting relaxation methods often outperform their corresponding synchronous counterparts. Moreover, the two-sweep relaxed multisplitting methods have better convergence properties than their corresponding one-sweep relaxed ones in the sense that they have larger convergence domains and faster convergence speeds. Hence, the asynchronous multisplitting unsymmetric relaxation iterations should be the methods of choice for solving the large sparse linear complementarity problems in the parallel computing environments.
Zhong Zhi Bai. (1970). Experimental Study of the Asynchronous Multisplitting Relaxation Methods for the Linear Complementarity Problems. Journal of Computational Mathematics. 20 (6). 561-574. doi:
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