Complete local search for propositional satisfiability

  • Authors:

  • Hai Fang;Wheeler Ruml

  • Affiliations:

  • Department of Computer Science, Yale University, New Haven, CT;Palo Alto Research Center, Palo Alto, CA

  • Venue:
  • AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
  • Year:
  • 2004

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Abstract

Algorithms based on following local gradient information are surprisingly effective for certain classes of constraint satisfaction problems. Unfortunately, previous local search algorithms are notoriously incomplete: They are not guaranteed to find a feasible solution if one exists and they cannot be used to determine unsatisfiability. We present an algorithmic framework for complete local search and discuss in detail an instantiation for the propositional satisfiability problem (SAT). The fundamental idea is to use constraint learning in combination with a novel objective function that converges during search to a surface without local minima. Although the algorithm has worst-case exponential space complexity, we present empirical resulls on challenging SAT competition benchmarks that suggest that our implementation can perform as well as state-of-the-art solvers based on more mature techniques. Our framework suggests a range of possible algorithms lying between tree-based search and local search.