full
search.png

Search

close.png

Cancel

arrow
Volume 21, Issue 6
A Class of Asynchronous Parallel Multisplitting Relaxation Methods for Large Sparse Linear Complementarity Problems

Zhongzhi Bai & Yuguang Huang

J. Comp. Math., 21 (2003), pp. 773-790.

Published online: 2003-12

Preview Full PDF 2605 24577

Cited by

google scholar semantic scholar
Export citation
  • 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.

  • Keywords

Linear complementarity problem, Matrix multisplitting, Relaxation method, Asynchronous iteration, Convergence theory.

  • AMS Subject Headings

  • Copyright

COPYRIGHT: © Global Science Press

  • Email address
  • BibTex
  • RIS
  • TXT
@Article{JCM-21-773, author = {Bai , Zhongzhi and Huang , Yuguang}, title = {A Class of Asynchronous Parallel Multisplitting Relaxation Methods for Large Sparse Linear Complementarity Problems}, journal = {Journal of Computational Mathematics}, year = {2003}, volume = {21}, number = {6}, pages = {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.

}, issn = {1991-7139}, doi = {https://doi.org/}, url = {http://global-sci.org/intro/article_detail/jcm/10234.html} }
TY - JOUR T1 - A Class of Asynchronous Parallel Multisplitting Relaxation Methods for Large Sparse Linear Complementarity Problems AU - Bai , Zhongzhi AU - Huang , Yuguang JO - Journal of Computational Mathematics VL - 6 SP - 773 EP - 790 PY - 2003 DA - 2003/12 SN - 21 DO - http://doi.org/ UR - https://global-sci.org/intro/article_detail/jcm/10234.html KW - Linear complementarity problem, Matrix multisplitting, Relaxation method, Asynchronous iteration, Convergence theory. AB -

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.

Bai , Zhongzhi and Huang , Yuguang. (2003). A Class of Asynchronous Parallel Multisplitting Relaxation Methods for Large Sparse Linear Complementarity Problems. Journal of Computational Mathematics. 21 (6). 773-790. doi:
Copy to clipboard
BibteX RIS TXT
The citation has been copied to your clipboard