Work

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

Year: 2016

Type: conference-paper

Abstract: 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. To overcome this limitation, we present PIPS-SBB: an exact distributed-memory parallel stochastic MIP solver that takes advantage of parallelism at multiple levels of the optimization process. We show promising results on instances from the SIPLIB benchmark by combining methods known for accelerating Branch and Bound (B&B) methods with new ideas that leverage the structure of SMIPs. We expect the performance of PIPS-SBB to improve further as more functionality is added in the future.

Source: IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)

DOI: 10.1109/ipdpsw.2016.159

Authors: Lluis-Miquel Munguia, Geoffrey Oxberry, Deepak Rajan

Institutions: Georgia Institute of Technology, Lawrence Livermore National Laboratory

Language: English

Topic: Advanced Optimization Algorithms Research

Subfield: Numerical Analysis

Field: Mathematics

Domain: Physical Sciences

Open Access status: green

Funders: U.S. Department of Energy, Argonne National Laboratory, Lawrence Livermore National Laboratory

OpenAlex ID: W2476080091

FWCI: 4.472

Cites: 40

Cited by: 12

Related to: 10