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Exploiting unit propagation to compute lower bounds in branch and bound max-SAT solvers. (English) Zbl 1153.68470
van Beek, Peter (ed.), Principles and practice of constraint programming – CP 2005. 11th international conference, CP 2005, Sitges, Spain, October 1–5, 2005. Proceedings. Berlin: Springer (ISBN 978-3-540-29238-8/pbk). Lecture Notes in Computer Science 3709, 403-414 (2005).
Summary: 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 Max-SAT 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.
For the entire collection see [Zbl 1140.68002].

MSC:
68T20 Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.)
Software:
BerkMin; SATO
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