Network-based heuristics for constraint-satisfaction problems
Artificial Intelligence
GRASP—a new search algorithm for satisfiability
Proceedings of the 1996 IEEE/ACM international conference on Computer-aided design
Multilevel hypergraph partitioning: application in VLSI domain
DAC '97 Proceedings of the 34th annual Design Automation Conference
A Computing Procedure for Quantification Theory
Journal of the ACM (JACM)
A machine program for theorem-proving
Communications of the ACM
Improved algorithms for hypergraph bipartitioning
ASP-DAC '00 Proceedings of the 2000 Asia and South Pacific Design Automation Conference
Chaff: engineering an efficient SAT solver
Proceedings of the 38th annual Design Automation Conference
Partition-based decision heuristics for image computation using SAT and BDDs
Proceedings of the 2001 IEEE/ACM international conference on Computer-aided design
Efficient Approximation for Triangulation of Minimum Treewidth
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
BerkMin: A Fast and Robust Sat-Solver
Proceedings of the conference on Design, automation and test in Europe
Exploiting hierarchy and structure to efficiently solve graph coloring as SAT
Proceedings of the 2007 IEEE/ACM international conference on Computer-aided design
Verification-driven slicing of UML/OCL models
Proceedings of the IEEE/ACM international conference on Automated software engineering
Variable ordering for efficient SAT search by analyzing constraint-variable dependencies
SAT'05 Proceedings of the 8th international conference on Theory and Applications of Satisfiability Testing
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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.