Optimal speedup of Las Vegas algorithms
Information Processing Letters
GRASP—a new search algorithm for satisfiability
Proceedings of the 1996 IEEE/ACM international conference on Computer-aided design
Boosting combinatorial search through randomization
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
A machine program for theorem-proving
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Chaff: engineering an efficient SAT solver
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Width-Based Restart Policies for Clause-Learning Satisfiability Solvers
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Solution-guided multi-point constructive search for job shop scheduling
Journal of Artificial Intelligence Research
SAT-Solving in Practice, with a Tutorial Example from Supervisory Control
Discrete Event Dynamic Systems
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IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Clasp: a conflict-driven answer set solver
LPNMR'07 Proceedings of the 9th international conference on Logic programming and nonmonotonic reasoning
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CP'07 Proceedings of the 13th international conference on Principles and practice of constraint programming
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On the power of clause-learning SAT solvers with restarts
CP'09 Proceedings of the 15th international conference on Principles and practice of constraint programming
Adaptive restart strategies for conflict driven SAT solvers
SAT'08 Proceedings of the 11th international conference on Theory and applications of satisfiability testing
SAT'08 Proceedings of the 11th international conference on Theory and applications of satisfiability testing
Artificial Intelligence
Partial weighted MaxSAT for optimal planning
PRICAI'10 Proceedings of the 11th Pacific Rim international conference on Trends in artificial intelligence
A SAT-based Method for Solving the Two-dimensional Strip Packing Problem
Fundamenta Informaticae - RCRA 2008 Experimental Evaluation of Algorithms for Solving Problems with Combinatorial Explosion
On the power of clause-learning SAT solvers as resolution engines
Artificial Intelligence
Learning and using domain-specific heuristics in ASP solvers
AI Communications - Answer Set Programming
Propelling SAT and SAT-based BMC using careset
Proceedings of the 2010 Conference on Formal Methods in Computer-Aided Design
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SAT'11 Proceedings of the 14th international conference on Theory and application of satisfiability testing
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SAT'11 Proceedings of the 14th international conference on Theory and application of satisfiability testing
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SAT'11 Proceedings of the 14th international conference on Theory and application of satisfiability testing
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TACAS'10 Proceedings of the 16th international conference on Tools and Algorithms for the Construction and Analysis of Systems
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Artificial Intelligence
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CP'12 Proceedings of the 18th international conference on Principles and Practice of Constraint Programming
Counter implication restart for parallel SAT solvers
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Journal of Scheduling
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SAT'13 Proceedings of the 16th international conference on Theory and Applications of Satisfiability Testing
A survey of the satisfiability-problems solving algorithms
International Journal of Advanced Intelligence Paradigms
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FPGA acceleration of enhanced boolean constraint propagation for SAT solvers
Proceedings of the International Conference on Computer-Aided Design
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Given the common use of restarts in today's clause learning SAT solvers, the task of choosing a good restart policy appears to have attracted remarkably little interest. On the other hand, results have been reported on the use of different restart policies for combinatorial search algorithms. Such results are not directly applicable to clause learning SAT solvers, as the latter are now understood as performing a form of resolution, something fundamentally different from search (in the sense of backtracking search for satisfying assignments). In this paper we provide strong evidence that a clause learning SAT solver could benefit substantially from a carefully designed restart policy (which may not yet be available). We begin by pointing out that the restart policy works together with other aspects of a SAT solver in determining the sequence of resolution steps performed by the solver, and hence its efficiency. In this spirit we implement a prototype clause learning SAT solver that facilitates restarts at arbitrary points, and conduct experiments on an extensive set of industrial benchmarks using various restart policies, including those used by well-known SAT solvers as well as a universal policy proposed in 1993 by Luby et al. The results indicate a substantial impact of the restart policy on the efficiency of the solver, and provide motivation for the design of better restart policies, particularly dynamic ones.