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Target Cuts from Relaxed Decision Diagrams

Author

Listed:
  • Christian Tjandraatmadja

    (Tepper School of Business, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213)

  • Willem-Jan van Hoeve

    (Tepper School of Business, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213)

Abstract

The most common approach to generate cuts in integer programming is to derive them from the linear programming relaxation. We study an alternative approach that extracts cuts from discrete relaxations known as relaxed decision diagrams. Through a connection between decision diagrams and polarity, the algorithm generates cuts that are facet defining for the convex hull of a decision diagram relaxation. As proof of concept, we provide computational evidence that this algorithm generates strong cuts for the maximum independent set problem and the minimum set covering problem.

Suggested Citation

  • Christian Tjandraatmadja & Willem-Jan van Hoeve, 2019. "Target Cuts from Relaxed Decision Diagrams," INFORMS Journal on Computing, INFORMS, vol. 31(2), pages 285-301, April.
    • Handle: RePEc:inm:orijoc:v:31:y:2019:i:2:p:285-301
    DOI: 10.1287/ijoc.2018.0830
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    References listed on IDEAS

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    1. R. Kipp Martin & Ronald L. Rardin & Brian A. Campbell, 1990. "Polyhedral Characterization of Discrete Dynamic Programming," Operations Research, INFORMS, vol. 38(1), pages 127-138, February.
    2. Andre A. Cire & Willem-Jan van Hoeve, 2013. "Multivalued Decision Diagrams for Sequencing Problems," Operations Research, INFORMS, vol. 61(6), pages 1411-1428, December.
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    Cited by:

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    2. Margarita P. Castro & Andre A. Cire & J. Christopher Beck, 2022. "Decision Diagrams for Discrete Optimization: A Survey of Recent Advances," INFORMS Journal on Computing, INFORMS, vol. 34(4), pages 2271-2295, July.

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