Abstract

Abstract. One of the main differences between complete SAT solvers and exact Max-SAT solvers is that the former make an intensive use of unit propagation at each node of the proof tree while the latter, in order to ensure optimality, can only apply unit propagation to a restricted number of nodes. In this paper, we describe a branch and bound MaxSAT solver that applies unit propagation at each node of the proof tree to compute the lower bound instead of applying unit propagation to simplify the formula. The new lower bound captures the lower bound based on inconsistency counts that apply most of the state-of-the-art Max-SAT solvers as well as other improvements, like the start rule, that have been defined to get a lower bound of better quality. Moreover, our solver incorporates the Jeroslow-Wang variable selection heuristic, the pure literal and dominating unit clause rules, and novel preprocessing techniques. The experimental investigation we conducted to compare our solver with the most modern Max-SAT solvers provides experimental evidence that our solver is very competitive. 1 Introduction In recent years there has been a growing interest in defining formalisms and techniques for modeling and solving problems with soft constraints [13]. Even when most of the research has been around the Max-CSP problem, we observe an increasing activity around the Max-SAT problem and, in particular, in developing fast Max-SAT solvers [1, 2, 4, 6, 18, 20].

Citations