Manipulating MDD Relaxations for Combinatorial Optimization

  • David Bergman
  • Willem-Jan van Hoeve
  • John N. Hooker
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6697)


We study the application of limited-width MDDs (multi-valued decision diagrams) as discrete relaxations for combinatorial optimization problems. These relaxations are used for the purpose of generating lower bounds. We introduce a new compilation method for constructing such MDDs, as well as algorithms that manipulate the MDDs to obtain stronger relaxations and hence provide stronger lower bounds. We apply our methodology to set covering problems, and evaluate the strength of MDD relaxations to relaxations based on linear programming. Our experimental results indicate that the MDD relaxation is particularly effective on structured problems, being able to outperform state-of-the-art integer programming technology by several orders of magnitude.


Feasible Solution Constraint Programming Constraint Matrix Binary Decision Diagram Partial Assignment 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • David Bergman
    • 1
  • Willem-Jan van Hoeve
    • 1
  • John N. Hooker
    • 1
  1. 1.Tepper School of BusinessCarnegie Mellon UniversityPittsburghU.S.A.

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