Opportunistic Specialization in Russian Doll Search

  • Pedro Mesegue
  • Martí Sánchez
  • Gérard Verfaillie
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2470)


Russian Doll Search (RDS) is a clever procedure to solve overconstrained problems. RDS solves a sequence of nested subproblems, each including one more variable than the previous, until the whole problem is solved. Specialized RDS (SRDS) solves each subproblem for every value of the new variable. SRDS lower bound is better than RDS lower bound, causing a higher efficiency. A natural extension is Full Specialized RDS (FSRDS), which solves each subproblem for every value of every variable. Although FSRDS lower bound is better than the SRDS one, the extra work performed by FSRDS renders it inefficient. However, much of the useless work can be avoided. With this aim, we present Opportunistic Specialization in RDS (OSRDS), an algorithm that lies between SRDS and FSRDS. In addition to specialize the values of one variable, OSRDS specializes some values of other variables that look promising to increase the lower bound in the current distribution of inconsistency counts. Experimental results on random and real problems show the benefits of this approach.


Lower Bound Constraint Satisfaction Problem Optimal Cost Opportunistic Specialization Frequency Assignment Problem 
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 2002

Authors and Affiliations

  • Pedro Mesegue
    • 1
  • Martí Sánchez
    • 1
  • Gérard Verfaillie
    • 2
  1. 1.IIIA-CSICBellaterraSpain
  2. 2.ONERAToulouse CedexFrance

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