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Improving Strong Branching by Propagation

  • Conference paper
Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems (CPAIOR 2013)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7874))

Abstract

Strong branching is an important component of most variable selection rules in branch-and-bound based mixed-integer linear programming solvers. It predicts the dual bounds of potential child nodes by solving auxiliary LPs and thereby helps to keep the branch-and-bound tree small. In this paper, we describe how these dual bound predictions can be improved by including domain propagation into strong branching. Computational experiments on standard MIP instances indicate that this is beneficial in three aspects: It helps to reduce the average number of LP iterations per strong branching call, the number of branch-and-bound nodes, and the overall solving time.

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Author information

Authors and Affiliations

  1. Zuse Institute Berlin, Takustr. 7, 14195, Berlin, Germany

    Gerald Gamrath

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  1. Gerald Gamrath
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Editor information

Editors and Affiliations

  1. Department of Computer Science, Cornell University, Ithaca, NY, USA

    Carla Gomes

  2. Thomas J. Watson Research Center, Yorktown Heights, NY, USA

    Meinolf Sellmann

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Gamrath, G. (2013). Improving Strong Branching by Propagation. In: Gomes, C., Sellmann, M. (eds) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems. CPAIOR 2013. Lecture Notes in Computer Science, vol 7874. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38171-3_25

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  • DOI: https://doi.org/10.1007/978-3-642-38171-3_25

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-38170-6

  • Online ISBN: 978-3-642-38171-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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