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Pruning for the Minimum Constraint Family and for the Number of Distinct Values Constraint Family

  • Nicolas Beldiceanu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2239)

Abstract

The paper presents propagation rules that are common to the minimum constraint family and to the number of distinct values constraint family. One practical interest of the paper is to describe an implementation of the number of distinct values constraint. This is a quite common counting constraint that one encounters in many practical applications such as timetabling or frequency allocation problems. A second important contribution is to provide a pruning algorithm for the constraint “at most n distinct values for a set of variables”. This can be considered as the counterpart of Regin's algorithm for the all different constraint where one enforces having at least n distinct values for a given set of n variables.

Keywords

Bipartite Graph Versus Versus Versus Domain Variable Versus Versus Versus Versus Conditional Statement 
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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References

  1. 1.
    Beldiceanu, N.: Global Constraints as Graph Properties on Structured Network of Elementary Constraints of the Same Type. SICS Technical Report T2000/01, (2000).Google Scholar
  2. 2.
    Cormen, T. H., Leiserson, C. E., Rivest R. L.: Introduction to Algorithms. The MIT Press, (1990).Google Scholar
  3. 3.
    Costa, M-C.: Persistency in maximum cardinality bipartite matchings. Operation Research Letters 15, 143–149, (1994).zbMATHCrossRefGoogle Scholar
  4. 4.
    Damaschke, P., Müller, H., Kratsch, D.: Domination in convex and chordal bipartite graphs. Information Processing Letters 36, 231–236, (1990).zbMATHCrossRefMathSciNetGoogle Scholar
  5. 5.
    Garey, M. R., Johnson, D. S.: Computers and intractability. A guide to the Theory of NP-Completeness. W. H. Freeman and Company, (1979).Google Scholar
  6. 6.
    Pachet, F., Roy, P.: Automatic Generation of Music Programs. In Principles and Practice of Constraint Programming-CP’99, 5th International Conference, Alexandria, Virginia, USA, (October 11-14, 1999), Proceedings. Lecture Notes in Computer Science, Vol. 1713, Springer, (1999).Google Scholar
  7. 7.
    Règin, J-C.: A filtering algorithm for constraints of difference in CSP. In Proc. of the Twelfth National Conference on Artificial Intelligence (AAAI-94), 362–367, (1994).Google Scholar
  8. 8.
    Règin, J-C.: Développement d’outils algorithmiques pour l’Intelligence Artificielle. Application á la chimie organique, PhD Thesis of LIRMM, Montpellier, France, (1995). In French.Google Scholar
  9. 9.
    Steiner, G., Yeomans, J.S.: A Linear Time Algorithm for Maximum Matchings in Convex, Bipartite Graph. In Computers Math. Applic., Vol. 31, No. 12, 91–96, (1996).zbMATHCrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Nicolas Beldiceanu
    • 1
  1. 1.SICSUppsalaSweden

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