Abstract
It is well known that the Davis-Putnam-Logemann-Loveland (DPLL) algorithm for satisfiability (SAT) can be extended to an algorithm for maximum SAT (max-SAT). In this paper, we propose a number of strategies to significantly improve this max-SAT method. The first strategy is a set of unit propagation rules; the second is an effective lookahead heuristic based on linear programming; and the third strategy is a dynamic variable ordering that exploits problem constrainedness during search. We integrate these strategies in an efficient complete solver for both max-SAT and weighted max-SAT. Our experimental results on random problem instances and many instances from SATLIB demonstrate the efficacy of these strategies and show that the new solver is able to significantly outperform most of the existing complete max-SAT solvers, with a few orders of magnitude of improvement in running time in many cases.
This research was supported in part by NSF grants IIS-0196057 and ITR/EIA-0113618, and in part by DARPA Cooperative Agreement F30602-00-2-0531. We thank Zhongsheng Guo for an early implementation of the DPLL algorithm and Fadi Aloul, Javier Larrosa and Jordi Plane for making their programs available to us for this research.
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Xing, Z., Zhang, W. (2004). Efficient Strategies for (Weighted) Maximum Satisfiability. In: Wallace, M. (eds) Principles and Practice of Constraint Programming – CP 2004. CP 2004. Lecture Notes in Computer Science, vol 3258. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30201-8_50
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DOI: https://doi.org/10.1007/978-3-540-30201-8_50
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