A machine program for theorem-proving
Communications of the ACM
Chaff: engineering an efficient SAT solver
Proceedings of the 38th annual Design Automation Conference
Towards understanding and harnessing the potential of clause learning
Journal of Artificial Intelligence Research
Effective preprocessing in SAT through variable and clause elimination
SAT'05 Proceedings of the 8th international conference on Theory and Applications of Satisfiability Testing
A Concurrent Portfolio Approach to SMT Solving
CAV '09 Proceedings of the 21st International Conference on Computer Aided Verification
SAT '09 Proceedings of the 12th International Conference on Theory and Applications of Satisfiability Testing
Predicting learnt clauses quality in modern SAT solvers
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Control-based clause sharing in parallel SAT solving
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
On Modern Clause-Learning Satisfiability Solvers
Journal of Automated Reasoning
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
Diversification and intensification in parallel SAT solving
CP'10 Proceedings of the 16th international conference on Principles and practice of constraint programming
On the power of clause-learning SAT solvers as resolution engines
Artificial Intelligence
Beyond unit propagation in SAT solving
SEA'11 Proceedings of the 10th international conference on Experimental algorithms
Generalized conflict-clause strengthening for satisfiability solvers
SAT'11 Proceedings of the 14th international conference on Theory and application of satisfiability testing
Dealing with Satisfiability and n-ary CSPs in a Logical Framework
Journal of Automated Reasoning
An overview of parallel SAT solving
Constraints
Producing and verifying extremely large propositional refutations
Annals of Mathematics and Artificial Intelligence
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This paper presents an extension of Conflict Driven Clauses Learning (CDCL). It relies on an extended notion of implication graph containing additional arcs, called inverse arcs. These are obtained by taking into account the satisfied clauses of the formula, which are usually ignored by conflict analysis. This extension captures more conveniently the whole propagation process, and opens new perspectives for CDCL-based approaches. Among other benefits, our extension leads to a new conflict analysis scheme that exploits the additional arcs to back-jump to higher levels. Experimental results show that the integration of our generalized conflict analysis scheme within two state-of-the-art solvers improves their performance.