San Jose, CA, USA
Nov. 7, 2004 to Nov. 11, 2004
V. Durairaj , Dept. of Electr. & Comput. Eng., Utah Univ., Salt Lake City, UT, USA
P. Kalla , Dept. of Electr. & Comput. Eng., Utah Univ., Salt Lake City, UT, USA
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.
V. Durairaj, P. Kalla, "Guiding CNF-SAT search via efficient constraint partitioning", ICCAD, 2004, ICCAD 2004. International Conference on Computer Aided Design, ICCAD 2004. International Conference on Computer Aided Design 2004, pp. 498-501, doi:10.1109/ICCAD.2004.1382629