Parallel Distributed-Memory Simplex for Large-Scale Stochastic LP Problems

TitleParallel Distributed-Memory Simplex for Large-Scale Stochastic LP Problems
Publication TypeJournal Article
Year of Publication2013
AuthorsLubin, M, Hall, JAJulian, Petra, CG, Anitescu, M
JournalComputational Optimization and Applications
Volume55
Issue3
Pagination571-596
Date Published04/2012
Other NumbersANL/MCS-P2075-0412
Abstract

We present a parallelization of the revised simplex method for large extensive forms of two-stage stochastic linear programming (LP) problems. These problems have been considered too large to solve with the simplex method; instead, decomposition approaches based on Benders decomposition or, more recently, interiorpoint methods are generally used. However, these approaches do not provide optimal basic solutions, which allow for efficient hot-starts (e.g., in a branch-and-bound context) and can provide important sensitivity information. Our approach exploits the dual block-angular structure of these problems inside the linear algebra of the revised simplex method in a manner suitable for high-performance distributed-memory clusters or supercomputers. While this paper focuses on stochastic LPs, the work is applicable to all problems with a dual block-angular structure. Our implementation is competitive in serial with highly efficient sparsity-exploiting simplex codes and achieves significant relative speed-ups when run in parallel. Additionally, very large problems with hundreds of millions of variables have been successfully solved to optimality. This is the largest-scale parallel sparsity-exploiting revised simplex implementation that has been developed to date and the first truly distributed solver. It is built on novel analysis of the linear algebra for dual block-angular LP problems when solved by using the revised simplex method and a novel parallel scheme for applying product form updates.

URLhttp://link.springer.com/article/10.1007%2Fs10589-013-9542-y#page-1
PDFhttp://www.mcs.anl.gov/papers/pipss.pdf