Abstract
The relaxation algorithm for linear programming is revised in this paper. Based on cluster structure, a parallel revised algorithm is presented. Its performance is analyzed. The experimental results on DAWNING 3000 are also given. Theoretical analysis and experimental results show that the revised relaxation algorithm improves the performance of the relaxation algorithm, and it has good parallelism and is very robust. Therefore, it can expect to be applied to the solution of the large-scale linear programming problems rising from practical application.
This work is supported by National Natural Science Foundation of China Grant #6027307, Grant #70471031 and Scientific Research Foundation of Naval University of Engineering Grant #HGDJJ05005.
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© 2006 Springer-Verlag Berlin Heidelberg
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Zhang, J., Li, Q., Song, Y., Qu, Y. (2006). A New Efficient Parallel Revised Relaxation Algorithm. In: Huang, DS., Li, K., Irwin, G.W. (eds) Intelligent Computing. ICIC 2006. Lecture Notes in Computer Science, vol 4113. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11816157_98
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DOI: https://doi.org/10.1007/11816157_98
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37271-4
Online ISBN: 978-3-540-37273-8
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