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
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
The Impact of Branching Heuristics in Propositional Satisfiability Algorithms
EPIA '99 Proceedings of the 9th Portuguese Conference on Artificial Intelligence: Progress in Artificial Intelligence
FORCE: a fast and easy-to-implement variable-ordering heuristic
Proceedings of the 13th ACM Great Lakes symposium on VLSI
BerkMin: A Fast and Robust Sat-Solver
Proceedings of the conference on Design, automation and test in Europe
Guiding CNF-SAT search via efficient constraint partitioning
Proceedings of the 2004 IEEE/ACM International conference on Computer-aided design
A structure-based variable ordering heuristic for SAT
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Exploiting hierarchy and structure to efficiently solve graph coloring as SAT
Proceedings of the 2007 IEEE/ACM international conference on Computer-aided design
A lightweight component caching scheme for satisfiability solvers
SAT'07 Proceedings of the 10th international conference on Theory and applications of satisfiability testing
| Hi-index | 0.00 |
This paper presents a new technique to derive an initial static variable ordering for efficient SAT search. Our approach not only exploits variable activity and connectivity information simultaneously, but it also analyzes how tightly the variables are related to each other. For this purpose, a new metric is proposed – the degree of correlation among pairs of variables. Variable activity and correlation information is modeled (implicitly) as a weighted graph. A topological analysis of this graph generates an order for SAT search. Also, the effect of decision-assignments on clause-variable dependencies is taken into account during this analysis. An algorithm called ACCORD (ACtivity – CORrelation – ORDering) is proposed for this purpose. Using efficient implementations of the above, experiments are conducted over a wide range of benchmarks. The results demonstrate that: (i) the variable order generated by our approach significantly improves the performance of SAT solvers; (ii) time to derive this order is a fraction of the overall solving time. As a result, our approach delivers faster performance as compared to contemporary approaches.