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
Chaff: engineering an efficient SAT solver
Proceedings of the 38th annual Design Automation Conference
Efficient conflict driven learning in a boolean satisfiability solver
Proceedings of the 2001 IEEE/ACM international conference on Computer-aided design
Width-Based Restart Policies for Clause-Learning Satisfiability Solvers
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
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
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
Using CSP look-back techniques to solve real-world SAT instances
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Effective preprocessing in SAT through variable and clause elimination
SAT'05 Proceedings of the 8th international conference on Theory and Applications of Satisfiability Testing
TACAS'10 Proceedings of the 16th international conference on Tools and Algorithms for the Construction and Analysis of Systems
An empirical study of learning and forgetting constraints
AI Communications - 18th RCRA International Workshop on “Experimental evaluation of algorithms for solving problems with combinatorial explosion”
Revisiting clause exchange in parallel SAT solving
SAT'12 Proceedings of the 15th international conference on Theory and Applications of Satisfiability Testing
CoPAn: exploring recurring patterns in conflict analysis of CDCL SAT solvers
SAT'12 Proceedings of the 15th international conference on Theory and Applications of Satisfiability Testing
Clause sharing in parallel MaxSAT
LION'12 Proceedings of the 6th international conference on Learning and Intelligent Optimization
Soundness of inprocessing in clause sharing SAT solvers
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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In this paper, we propose a new dynamic management policy of the learnt clause database in modern SAT solvers. It is based on a dynamic freezing and activation principle of the learnt clauses. At a given search state, using a relevant selection function, it activates the most promising learnt clauses while freezing irrelevant ones. In this way, clauses learned at previous steps can be frozen at the current step and might be activated again in future steps of the search process. Our strategy tries to exploit pieces of information gathered from the past to deduce the relevance of a given clause for the remaining search steps. This policy contrasts with all the well-known deletion strategies, where a given learned clause is definitely eliminated. Experiments on SAT instances taken from the last competitions demonstrate the efficiency of our proposed technique.