Proceedings of the 38th annual Design Automation Conference
Chaff: engineering an efficient SAT solver
Proceedings of the 38th annual Design Automation Conference
Proceedings of the conference on Design, automation and test in Europe
SAT '09 Proceedings of the 12th International Conference on Theory and Applications of Satisfiability Testing
Towards understanding and harnessing the potential of clause learning
Journal of Artificial Intelligence Research
Predicting learnt clauses quality in modern SAT solvers
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
A lightweight component caching scheme for satisfiability solvers
SAT'07 Proceedings of the 10th international conference on Theory and applications of satisfiability testing
SAT'08 Proceedings of the 11th international conference on Theory and applications of satisfiability testing
Zchaff2004: an efficient SAT solver
SAT'04 Proceedings of the 7th international conference on Theory and Applications of Satisfiability Testing
A clause-based heuristic for SAT solvers
SAT'05 Proceedings of the 8th international conference on Theory and Applications of Satisfiability Testing
Between restarts and backjumps
SAT'11 Proceedings of the 14th international conference on Theory and application of satisfiability testing
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Assignment stack shrinking is a technique that is intended to speed up the performance of modern complete SAT solvers. Shrinking was shown to be efficient in SAT’04 competition winners Jerusat and Chaff. However, existing studies lack the details of the shrinking algorithm. In addition, shrinking’s performance was not tested in conjunction with the most modern techniques. This paper provides a detailed description of the shrinking algorithm and proposes two new heursitics for it. We show that using shrinking is critical for solving well-known industrial benchmark families with the latest versions of Minisat and Eureka.