GRASP—a new search algorithm for satisfiability
Proceedings of the 1996 IEEE/ACM international conference on Computer-aided design
A machine program for theorem-proving
Communications of the ACM
Chaff: engineering an efficient SAT solver
Proceedings of the 38th annual Design Automation Conference
Heavy-Tailed Phenomena in Satisfiability and Constraint Satisfaction Problems
Journal of Automated Reasoning
The effect of restarts on the efficiency of clause learning
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
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
Adaptive restart strategies for conflict driven SAT solvers
SAT'08 Proceedings of the 11th international conference on Theory and applications of satisfiability testing
Concurrent clause strengthening
SAT'13 Proceedings of the 16th international conference on Theory and Applications of Satisfiability Testing
Factoring out assumptions to speed up MUS extraction
SAT'13 Proceedings of the 16th international conference on Theory and Applications of Satisfiability Testing
Improving glucose for incremental SAT solving with assumptions: application to MUS extraction
SAT'13 Proceedings of the 16th international conference on Theory and Applications of Satisfiability Testing
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So-called Modern SAT solvers are built upon a few --- but essential --- ingredients: branching, learning, restarting and clause database cleaning. Most of them have been greatly improved since their first introduction, more than ten years ago. In many cases, the initial reasons that lead to their introduction do not explain anymore their current usage (for instance: very rapid restarts, aggressive clause database cleaning). Modern SAT solvers themselves share fewer and fewer properties with their ancestor, the classical backtrack search DPLL procedure. In this paper, we explore restart strategies in the light of a new vision of SAT solvers. Following the successful results of Glucose, we consider CDCL solvers as resolution-based producers of clauses. We show that this vision is particularly salient for targeting UNSAT formulae. In a second part, we show how detecting sudden increases in the number of variable assignments can help the solver to target SAT instances too. By varying our restart strategy, we show an important improvement over Glucose 2.0, the winner of the 2011 SAT Competition, category Application SAT+UNSAT formulae. Finally we would like to point out that this new version of Glucose was the winner of the SAT Challenge 2012.