Abstract
Coarse grain parallelism inherent in the solution of Linear Programming (LP) problems with block angular constraint matrices has been exploited in recent research works. However, these approaches suffer from unscalability and load imbalance since they exploit only the existing block angular structure of the LP constraint matrix. In this paper, we consider decomposing LP constraint matrices to obtain block angular structures with specified number of blocks for scalable parallelization. We propose hypergraph models to represent LP constraint matrices for decomposition. In these models, the decomposition problem reduces to the well-known hypergraph partitioning problem. A Kernighan-Lin based multiway hypergraph partitioning heuristic is implemented for experimenting with the performance of the proposed hypergraph models on the decomposition of the LP problems selected from NETLIB suite. Initial results are promising and justify further research on other hypergraph partitioning heuristics for decomposing large LP problems.
This work is partially supported by the Commission of the European Communities, Directorate General for Industry under contract ITDC 204-82166.
Supported in part through grant no. 9500764 by the Danish Natural Science Council.
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© 1996 Springer-Verlag Berlin Heidelberg
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Pinar, A., Çatalyürek, Ü.V., Aykanat, C., Pinar, M. (1996). Decomposing linear programs for parallel solution. In: Dongarra, J., Madsen, K., Waśniewski, J. (eds) Applied Parallel Computing Computations in Physics, Chemistry and Engineering Science. PARA 1995. Lecture Notes in Computer Science, vol 1041. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-60902-4_50
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DOI: https://doi.org/10.1007/3-540-60902-4_50
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