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Distributed Domain Propagation

  • Portfolio parallelization is an approach that runs several solver instances in parallel and terminates when one of them succeeds in solving the problem. Despite its simplicity, portfolio parallelization has been shown to perform well for modern mixed-integer programming (MIP) and boolean satisfiability problem (SAT) solvers. Domain propagation has also been shown to be a simple technique in modern MIP and SAT solvers that effectively finds additional domain reductions after the domain of a variable has been reduced. In this paper we introduce distributed domain propagation, a technique that shares bound tightenings across solvers to trigger further domain propagations. We investigate its impact in modern MIP solvers that employ portfolio parallelization. Computational experiments were conducted for two implementations of this parallelization approach. While both share global variable bounds and solutions, they communicate differently. In one implementation the communication is performed only at designated points in the solving process and in the other it is performed completely asynchronously. Computational experiments show a positive performance impact of communicating global variable bounds and provide valuable insights in communication strategies for parallel solvers.
Author:Robert Lion GottwaldORCiD, Stephen J. Maher, Yuji ShinanoORCiD
Document Type:In Proceedings
Parent Title (English):16th International Symposium on Experimental Algorithms (SEA 2017)
First Page:6:1
Last Page:6:11
Series:Leibniz International Proceedings in Informatics (LIPIcs)
Year of first publication:2017
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