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Towards Massively Parallel Local Search for SAT

(Poster Presentation)
  • Alejandro Arbelaez
  • Philippe Codognet
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7317)

Abstract

Parallel portfolio-based algorithms have become a standard methodology for building parallel algorithms for SAT. In this methodology, different algorithms (or the same one with different random seeds) compete to solve a given problem instance. Moreover, the portfolio is usually equipped with cooperation, this way algorithms exchange important knowledge acquired during the search to solve a given problem instance. Portfolio algorithms based on complete solvers exchange learned clauses which are incorporated within each search engine (e.g. ManySAT [1] and plingeling), while those based on incomplete solvers [2] exchange the best assignment for the variables found so far in order to properly craft a new assignment for the variables to restart from. These strategies range from a voting mechanism where each algorithm in the portfolio suggests a value for each variable to probabilistic constructions.

References

  1. 1.
    Hamadi, Y., Jabbour, S., Sais, L.: ManySAT: A Parallel SAT Solver. Journal on Satisfiability, Boolean Modeling and Computation, JSAT 6(4), 245–262 (2009)zbMATHGoogle Scholar
  2. 2.
    Arbelaez, A., Hamadi, Y.: Improving Parallel Local Search for SAT. In: Coello, C.A.C. (ed.) LION 2011. LNCS, vol. 6683, pp. 46–60. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  3. 3.
    Balint, A., Fröhlich, A.: Improving Stochastic Local Search for SAT with a New Probability Distribution. In: Strichman, O., Szeider, S. (eds.) SAT 2010. LNCS, vol. 6175, pp. 10–15. Springer, Heidelberg (2010)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Alejandro Arbelaez
    • 1
  • Philippe Codognet
    • 1
  1. 1.JFLI - CNRS / University of TokyoJapan

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