Tree Decomposition with Function Filtering

  • Martí Sánchez
  • Javier Larrosa
  • Pedro Meseguer
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3709)


Besides search, complete inference methods can also be used to solve soft constraint problems. Their main drawback is the high spatial complexity. To improve its practical usage, we present an approach to decrease memory consumtion in tree decomposition methods, a class of complete inference algorithms. This approach, called function filtering, allows to detect and remove some tuples that appear to be consistent (with a cost below the upper bound) but that will become inconsistent (with a cost exceeding the upper bound) when extended to other variables. Using this idea, we have developed new algorithms CTEf, MCTEf and IMCTEf, standing for cluster, mini-cluster and iterative mini-cluster tree elimination with function filtering. We demonstrate empirically the benefits of our approach.


Tree Decomposition Constraint Optimization Problem Crossword Puzzle Valuation Structure Complete Inference 
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 2005

Authors and Affiliations

  • Martí Sánchez
    • 1
  • Javier Larrosa
    • 2
  • Pedro Meseguer
    • 1
  1. 1.Institut d’Investigació en Intel.ligència ArtificialConsejo Superior de Investigaciones CientíficasBellaterraSpain
  2. 2.Dep. Llenguatges i Sistemes InformàticsUniversitat Politècnica de CatalunyaBarcelonaSpain

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