A new efficient parallel revised relaxation algorithm

Authors:
Jianjun Zhang;Qinghua Li;Yexin Song;Yong Qu
Affiliations:
Department of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, China;Department of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, China;College of Science, Naval University of Engineering, Wuhan, Hubei, China;College of Science, Naval University of Engineering, Wuhan, Hubei, China
Venue:
ICIC'06 Proceedings of the 2006 international conference on Intelligent Computing - Volume Part I
Year:
2006

Citing 4
Cited 0

Integer and combinatorial optimization

Integer and combinatorial optimization
Investigating the sparse simplex algorithm on a distributed memory multiprocessor

Parallel Computing - High performance computing in operations research
Computational results with a primal-dual subproblem simplex method

Operations Research Letters
A parallel primal-dual simplex algorithm

Operations Research Letters

Quantified Score

Hi-index	0.00

Visualization

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.