GRASP: A Search Algorithm for Propositional Satisfiability
IEEE Transactions on Computers
Symbolic model checking using SAT procedures instead of BDDs
Proceedings of the 36th annual ACM/IEEE Design Automation Conference
A boolean satisfiability-based incremental rerouting approach with application to FPGAs
Proceedings of the conference on Design, automation and test in Europe
Proceedings of the 38th annual Design Automation Conference
Chaff: engineering an efficient SAT solver
Proceedings of the 38th annual Design Automation Conference
Solving difficult SAT instances in the presence of symmetry
Proceedings of the 39th 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
Shatter: efficient symmetry-breaking for boolean satisfiability
Proceedings of the 40th annual Design Automation Conference
BerkMin: A Fast and Robust Sat-Solver
Proceedings of the conference on Design, automation and test in Europe
Using Problem Symmetry in Search Based Satisfiability Algorithms
Proceedings of the conference on Design, automation and test in Europe
Exploiting structure in symmetry detection for CNF
Proceedings of the 41st annual Design Automation Conference
Combinational test generation using satisfiability
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Efficient SAT solving: beyond supercubes
Proceedings of the 42nd annual Design Automation Conference
B-Cubing: New Possibilities for Efficient SAT-Solving
IEEE Transactions on Computers
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Learning is an essential pruning technique in modern SAT solvers, but it exploits a relatively small amount of information that can be deduced from the conflicts. Recently a new pruning technique called supercubing was proposed [1]. Supercubing can exploit functional symmetries that are abundant in industrial SAT instances. We point out the significant difficulties of integrating supercubing with learning and propose solutions. Our experimental solver is the first supercubing-based solver with performance comparable to leading edge solvers.