Mathematical Programming Computation· 2017Q1
Parallelizing the dual revised simplex method
- 356citations
- Q1SCImago
- 2017year
Short summary
Two new parallel dual simplex solvers, PAMI and SIP, achieve significant speedups for large-scale linear programming problems, with PAMI outperforming existing solvers and SIP complementing PAMI.
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Key points
- Introduced PAMI, a parallel dual simplex solver extending suboptimization for multi-iteration parallelism.
- Introduced SIP, a parallel dual simplex solver exploiting single-iteration parallelism by overlapping components.
- PAMI demonstrated superior performance compared to the leading open-source simplex solver.
- SIP complements PAMI, providing speedups when PAMI leads to slowdown.
- PAMI's techniques have been integrated into the FICO Xpress simplex solver, showing proven value.
AI-generated from the title and abstract; the full text is not read.
Abstract
This paper introduces the design and implementation of two parallel dual simplex solvers for general large scale sparse linear programming problems. One approach, called PAMI, extends a relatively unknown pivoting strategy called suboptimization and exploits parallelism across multiple iterations. The other, called SIP, exploits purely single iteration parallelism by overlapping computational components when possible. Computational results show that the performance of PAMI is superior to that of the leading open-source simplex solver, and that SIP complements PAMI in achieving speedup when PAMI results in slowdown. One of the authors has implemented the techniques underlying PAMI within the FICO Xpress simplex solver and this paper presents computational results demonstrating their value. In developing the first parallel revised simplex solver of general utility, this work represents a significant achievement in computational optimization.
The authors' abstract, as published at the source. Mathematical Programming Computation, 2017 · DOI ↗
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Field: Numerical Analysis
Numerical AnalysisMathematics