PIPS-SBB: A Parallel Distributed-Memory Branch-and-Bound Algorithm for Stochastic Mixed-Integer Programs

Journal Article · Sun May 01 00:00:00 EDT 2016 · Parallel and Distributed Processing Symposium Workshops, 2016 IEEE International

DOI:https://doi.org/10.1109/IPDPSW.2016.159· OSTI ID:1321445

Munguia, Lluis-Miquel ^[1]; Oxberry, Geoffrey ^[2]; Rajan, Deepak ^[2]

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1. Georgia Inst. of Technology, Atlanta, GA (United States)
2. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

Stochastic mixed-integer programs (SMIPs) deal with optimization under uncertainty at many levels of the decision-making process. When solved as extensive formulation mixed- integer programs, problem instances can exceed available memory on a single workstation. In order to overcome this limitation, we present PIPS-SBB: a distributed-memory parallel stochastic MIP solver that takes advantage of parallelism at multiple levels of the optimization process. We also show promising results on the SIPLIB benchmark by combining methods known for accelerating Branch and Bound (B&B) methods with new ideas that leverage the structure of SMIPs. Finally, we expect the performance of PIPS-SBB to improve further as more functionality is added in the future.

• Research Organization: Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
• Sponsoring Organization: USDOE
• Grant/Contract Number: AC52-07NA27344
• OSTI ID: 1321445
• Report Number(s): LLNL-JRNL-678917
• Journal Information: Parallel and Distributed Processing Symposium Workshops, 2016 IEEE International, Conference: 2016 International Parallel and Distributed Processing Symposium (IPDPS) Workshops, 23-27 May 2016
• Publisher: IEEE
• Country of Publication: United States
• Language: English

Related Subjects

97 MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE
two-stage stochastic mixed-integer programming
distributed memory algorithm
branch and bound
dual block-angular