Artificial Intelligence
A logical approach to efficient Max-SAT solving
Artificial Intelligence
Exploiting Cycle Structures in Max-SAT
SAT '09 Proceedings of the 12th International Conference on Theory and Applications of Satisfiability Testing
SAT'10 Proceedings of the 13th international conference on Theory and Applications of Satisfiability Testing
A complete calculus for Max-SAT
SAT'06 Proceedings of the 9th international conference on Theory and Applications of Satisfiability Testing
Minimum satisfiability and its applications
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume One
Optimizing with minimum satisfiability
Artificial Intelligence
A new encoding from MinSAT into MaxSAT
CP'12 Proceedings of the 18th international conference on Principles and Practice of Constraint Programming
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We show that there exists a natural encoding which transforms Min-SAT instances into Max-SAT instances. Unlike previous encodings, this natural encoding keeps the same variables, and the optimal assignment for the Min-SAT instance is identical to the optimal assignment of the corresponding Max-SAT instance. In addition to that the encoding can be generalized to the Min-SAT variants with clause weights and hard clauses. We conducted experiments which give evidence that our encoding is practically relevant, as Min-2-SAT instances can be solved much faster by transforming them to Max-SAT and using a Max-SAT solver than by using the best Min-SAT solver directly.