mlsanityData to 7 Oct 2026

Parallelizing the dual revised simplex method

arXiv 1503.01889 ↗First posted 6 Mar 2015math.OC cs.NA math.NAcited by 83

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Abstract (arXiv)

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. This performance increase is sufficiently valuable for the achievement to be used as the basis of promotional material by FICO. In developing the first parallel revised simplex solver of general utility and commercial importance, this work represents a significant achievement in computational optimization.

Key facts. Cited by 83 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Mar 2015.

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Citing papers per year: 2021: 2; 2022: 3; 2023: 7; 2024: 11; 2025: 31; 2026: 29.

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