Improving strong branching by domain propagation

  • One of the essential components of a branch-and-bound based mixed-integer linear programming (MIP) solver is the branching rule. Strong branching is a method used by many state-of-the-art branching rules to select the variable to branch on. It precomputes the dual bounds of potential child nodes by solving auxiliary linear programs (LPs) and thereby helps to take good branching decisions that lead to a small search tree. In this paper, we describe how these dual bound predictions can be improved by including domain propagation into strong branching. Domain propagation is a technique MIP solvers usually apply at every node of the branch-and-bound tree to tighten the local domains of variables. Computational experiments on standard MIP instances indicate that our improved strong branching method significantly improves the quality of the predictions and causes almost no additional effort. For a full strong branching rule, we are able to obtain substantial reductions of the branch-and-bound tree size as well as the solving time. Moreover, the state-of-the-art hybrid branching rule can be improved this way as well. This paper extends previous work by the author published in the proceedings of the CPAIOR 2013.

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Metadaten
Author:Gerald Gamrath
Document Type:Article
Parent Title (English):EURO Journal on Computational Optimization
Volume:2
Issue:3
First Page:99
Last Page:122
Publisher:Springer
Place of publication:Berlin Heidelberg
Year of first publication:2014
Pagenumber:24
Preprint:urn:nbn:de:0297-zib-42546
DOI:http://dx.doi.org/10.1007/s13675-014-0021-8

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