Core-Guided Binary Search Algorithms for Maximum Satisfiability - Heras, Federico; Morgado, Antonio; Marques-Silva, Joao (Proceedings of the AAAI Conference on Artificial Intelligence, 2011)
Several MaxSAT algorithms based on iterative SAT solving have been proposed in recent years. These algorithms are in general the most efﬁcient for real-world applications. Existing data indicates that, among MaxSAT algorithms based on iterative SAT solving, the most efﬁcient ones are core-guided, i.e. algorithms which guide the search by iteratively computing unsatisﬁable subformulas (or cores). For weighted MaxSAT, core-guided algorithms exhibit a number of important drawbacks, including a possibly exponential number of iterations and the use of a large number of auxiliary variables. This paper develops two new algorithms for (weighted) MaxSAT that address these two drawbacks. The ﬁrst MaxSAT algorithm implements core-guided iterative SAT solving with binary search. The second algorithm extends the ﬁrst one by exploiting disjoint cores. The empirical evaluation shows that core-guided binary search is competitive with current MaxSAT solvers.