A Computing Procedure for Quantification Theory
Journal of the ACM (JACM)
Redundancy in logic I: CNF propositional formulae
Artificial Intelligence
A simplifier for propositional formulas with many binary clauses
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Formalizing dangerous SAT encodings
SAT'07 Proceedings of the 10th international conference on Theory and applications of satisfiability testing
NiVER: non-increasing variable elimination resolution for preprocessing SAT instances
SAT'04 Proceedings of the 7th international conference on Theory and Applications of Satisfiability Testing
Effective preprocessing in SAT through variable and clause elimination
SAT'05 Proceedings of the 8th international conference on Theory and Applications of Satisfiability Testing
An overview of parallel SAT solving
Constraints
Coprocessor 2.0: a flexible CNF simplifier
SAT'12 Proceedings of the 15th international conference on Theory and Applications of Satisfiability Testing
IJCAR'12 Proceedings of the 6th international joint conference on Automated Reasoning
Concurrent clause strengthening
SAT'13 Proceedings of the 16th international conference on Theory and Applications of Satisfiability Testing
Knowledge compilation for model counting: affine decision trees
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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In this paper, we present a new way to preprocess Boolean formulae in Conjunctive Normal Form (CNF). In contrast to most of the current pre-processing techniques, our approach aims at improving the filtering power of the original clauses while producing a small number of additional and relevant clauses. More precisely, an incomplete redundancy check is performed on each original clauses through unit propagation, leading to either a sub-clause or to a new relevant one generated by the clause learning scheme. This preprocessor is empirically compared to the best existing one in terms of size reduction and the ability to improve a state-of-the-art satisfiability solver.