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Improved Constraint Propagation via Lagrangian Decomposition

  • David Bergman
  • Andre A. Cire
  • Willem-Jan van HoeveEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9255)

Abstract

Constraint propagation is inherently restricted to the local information that is available to each propagator. We propose to improve the communication between constraints by introducing Lagrangian penalty costs between pairs of constraints, based on the Lagrangian decomposition scheme. The role of these penalties is to force variable assignments in each of the constraints to correspond to one another. We apply this approach to constraints that can be represented by decision diagrams, and show that propagating Lagrangian cost information can help improve the overall bound computation as well as the solution time.

Keywords

Constraint Programming Lagrangian Relaxation Binary Decision Diagram Lagrangian Decomposition Optimal Lagrangian Multiplier 
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 International Publishing Switzerland 2015

Authors and Affiliations

  • David Bergman
    • 1
  • Andre A. Cire
    • 2
  • Willem-Jan van Hoeve
    • 3
    Email author
  1. 1.School of BusinessUniversity of ConnecticutMansfieldUSA
  2. 2.University of Toronto ScarboroughTorontoCanada
  3. 3.Tepper School of BusinessCarnegie Mellon UniversityPittsburghUSA

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