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Heuristics for Fast Exact Model Counting

  • Tian Sang
  • Paul Beame
  • Henry Kautz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3569)

Abstract

An important extension of satisfiability testing is model-counting, a task that corresponds to problems such as probabilistic reasoning and computing the permanent of a Boolean matrix. We recently introduced Cachet, an exact model-counting algorithm that combines formula caching, clause learning, and component analysis. This paper reports on experiments with various techniques for improving the performance of Cachet, including component-selection strategies, variable-selection branching heuristics, randomization, backtracking schemes, and cross-component implications. The result of this work is a highly-tuned version of Cachet, the first (and currently, only) system able to exactly determine the marginal probabilities of variables in random 3-SAT formulas with 150+ variables. We use this to discover an interesting property of random formulas that does not seem to have been previously observed.

Keywords

Search Tree Unit Propagation Marginal Probability Satisfying Assignment Clause Learning 
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

  • Tian Sang
    • 1
  • Paul Beame
    • 1
  • Henry Kautz
    • 1
  1. 1.Computer Science and EngineeringUniversity of WashingtonSeattleUSA

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