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Guiding CNF-SAT search via efficient constraint partitioning

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2 Author(s)
Durairaj, V. ; Dept. of Electr. & Comput. Eng., Utah Univ., Salt Lake City, UT, USA ; Kalla, P.

Contemporary techniques to identify a good variable order for SAT rely on identifying minimum tree-width decompositions. However, the problem of finding a minimal width tree decomposition for an arbitrary graph is NP complete. The available tools and methods are impractical, as they cannot handle large and hard-to-solve CNF-SAT instances. This work proposes a hypergraph partitioning based constraint decomposition technique as an alternative to contemporary methods. We model the CNF-SAT problem on a hypergraph and apply min-cut based bi-partitioning. Clause-variable statistics across the partitions are analyzed to further decompose the problem, iteratively. The resulting tree-like decomposition provides a variable order for guiding CNF-SAT search. Experiments demonstrate that our partitioning procedure is fast, scalable and the derived variable order results in significant increase in performance of the SAT engine.

Published in:

Computer Aided Design, 2004. ICCAD-2004. IEEE/ACM International Conference on

Date of Conference:

7-11 Nov. 2004