GRASP—a new search algorithm for satisfiability
Proceedings of the 1996 IEEE/ACM international conference on Computer-aided design
Some consequences of cryptographical conjectures for S12 and EF
Information and Computation - Special issue: logic and computational complexity
On Interpolation and Automatization for Frege Systems
SIAM Journal on Computing
Chaff: engineering an efficient SAT solver
Proceedings of the 38th annual Design Automation Conference
On the lengths of proofs in the propositional calculus (Preliminary Version)
STOC '74 Proceedings of the sixth annual ACM symposium on Theory of computing
Resolution is Not Automatizable Unless W[P] is Tractable
FOCS '01 Proceedings of the 42nd IEEE symposium on Foundations of Computer Science
Implementing a generalized version of resolution
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Clause learning can effectively P-simulate general propositional resolution
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
A new clause learning scheme for efficient unsatisfiability proofs
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
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
CP'07 Proceedings of the 13th international conference on Principles and practice of constraint programming
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
Extended resolution proofs for conjoining BDDs
CSR'06 Proceedings of the First international computer science conference on Theory and Applications
Effective preprocessing in SAT through variable and clause elimination
SAT'05 Proceedings of the 8th international conference on Theory and Applications of Satisfiability Testing
Extended resolution proofs for symbolic SAT solving with quantification
SAT'06 Proceedings of the 9th 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
SAT and SMT are still resolution: questions and challenges
IJCAR'12 Proceedings of the 6th international joint conference on Automated Reasoning
IJCAR'12 Proceedings of the 6th international joint conference on Automated Reasoning
Factoring out assumptions to speed up MUS extraction
SAT'13 Proceedings of the 16th international conference on Theory and Applications of Satisfiability Testing
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The past decade has seen clause learning as the most successful algorithm for SAT instances arising from real-world applications. This practical success is accompanied by theoretical results showing clause learning as equivalent in power to resolution. There exist, however, problems that are intractable for resolution, for which clause-learning solvers are hence doomed. In this paper, we present extended clause learning, a practical SAT algorithm that surpasses resolution in power. Indeed, we prove that it is equivalent in power to extended resolution, a proof system strictly more powerful than resolution. Empirical results based on an initial implementation suggest that the additional theoretical power can indeed translate into substantial practical gains.