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Lower Bounds for Non-binary Constraint Optimization Problems

  • Pedro Meseguer
  • Javier Larrosa
  • Martí Sánchez
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2239)

Abstract

The necessity of non-binary constraint satisfaction algorithms is increasing because many real problems are inherently nonbinary. Considering overconstrained problems (and Partial Forward Checking as the solving algorithm), we analyze several lower bounds proposed in the binary case, extending them for the non-binary case. We show that techniques initially developed in the context of reversible DAC can be applied in the general case, to deal with constraints of any arity. We discuss some of the issues raised for non-binary lower bounds, and we study their computational complexity. We provide experimental results of the use of the new lower bounds on overconstrained random problems, including constraints with different weights.

Keywords

Constraint Satisfaction Problem Soft Constraint Limited Version Future Variable Current 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 2001

Authors and Affiliations

  • Pedro Meseguer
    • 1
  • Javier Larrosa
    • 2
  • Martí Sánchez
    • 1
  1. 1.IIIA-CSICCampus UABBellaterraSpain
  2. 2.Dep. LSIUPCBarcelonaSpain

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