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
Using CSP look-back techniques to solve real-world SAT instances
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Effective preprocessing in SAT through variable and clause elimination
SAT'05 Proceedings of the 8th international conference on Theory and Applications of Satisfiability Testing
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In [3], SAT conflict analysis graphs were used to learn additional clauses, which we refer to as back-clauses. These clauses may be viewed as enabling the powerful notion of "probing": Back-clauses make inferences that would normally have to be deduced by setting a variable deliberately the other way and observing that unit propagation leads to a conflict. We show that short-cutting this process can in fact improve the performance of modern SAT solvers in theory and in practice. Based on out numerical results, it is suprising that back-clauses, proposed over a decade ago, are not yet part of standard clause-learning SAT solvers.