GRASP: A Search Algorithm for Propositional Satisfiability
IEEE Transactions on Computers
Chaff: engineering an efficient SAT solver
Proceedings of the 38th annual Design Automation Conference
Computers and Intractability; A Guide to the Theory of NP-Completeness
Computers and Intractability; A Guide to the Theory of NP-Completeness
The Design and Analysis of Computer Algorithms
The Design and Analysis of Computer Algorithms
Efficient conflict driven learning in a boolean satisfiability solver
Proceedings of the 2001 IEEE/ACM international conference on Computer-aided design
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
Compressing propositional proofs by common subproof extraction
EUROCAST'07 Proceedings of the 11th international conference on Computer aided systems theory
Artificial Intelligence
On freezing and reactivating learnt clauses
SAT'11 Proceedings of the 14th international conference on Theory and application of satisfiability testing
Empirical study of the anatomy of modern sat solvers
SAT'11 Proceedings of the 14th international conference on Theory and application of satisfiability testing
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Even though the CDCL algorithm and current SAT solvers perform tremendously well for many industrial instances, the performance is highly sensitive to specific parameter settings. Slight modifications may cause completely different solving behaviors for the same benchmark. A fast run is often related to learning of 'good' clauses. Our tool CoPAn allows the user for an in-depth analysis of conflicts and the process of creating learnt clauses. Particularly we focus on isomorphic patterns within the resolution operation for different conflicts. Common proof logging output of any CDCL solver can be adapted to configure the analysis of CoPAn in multiple ways.