MINIMAXSAT: an efficient weighted max-SAT solver

  • Authors:
  • Federico Heras;Javier Larrosa;Albert Oliveras

  • Affiliations:
  • LSI Department, Technical University of Catalonia, Barcelona, Spain;LSI Department, Technical University of Catalonia, Barcelona, Spain;LSI Department, Technical University of Catalonia, Barcelona, Spain

  • Venue:
  • Journal of Artificial Intelligence Research
  • Year:
  • 2008

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Abstract

In this paper we introduce MINIMAXSAT, a new Max-SAT solver that is built on top of MIN-ISAT+.It incorporates the best current SAT and Max-SAT techniques. It can handle hard clauses (clauses of mandatory satisfaction as in SAT), soft clauses (clauses whose falsification is penalized by a cost as in Max-SAT) as well as pseudo-boolean objective functions and constraints. Its main features are: learning and backjumping on hard clauses; resolution-based and substraction-based lower bounding; and lazy propagation with the two-watched literal scheme. Our empirical evaluation comparing a wide set of solving alternatives on a broad set of optimization benchmarks indicates that the performance of MINIMAXSAT is usually close to the best specialized alternative and, in some cases, even better.