GRASP—a new search algorithm for satisfiability
Proceedings of the 1996 IEEE/ACM international conference on Computer-aided design
Chaff: engineering an efficient SAT solver
Proceedings of the 38th annual Design Automation Conference
On SAT Instance Classes and a Method for Reliable Performance Experiments with SAT Solvers
Annals of Mathematics and Artificial Intelligence
BerkMin: A fast and robust Sat-solver
Discrete Applied Mathematics
The effect of restarts on the efficiency of clause learning
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Adaptive restart strategies for conflict driven SAT solvers
SAT'08 Proceedings of the 11th international conference on Theory and applications of satisfiability testing
Statistical methodology for comparison of SAT solvers
SAT'10 Proceedings of the 13th international conference on Theory and Applications of Satisfiability Testing
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Experimentation of new algorithms is the usual companion section of papers dealing with SAT. However, the behavior of those algorithms is so unpredictable that even strong experiments (hundreds of benchmarks, dozen of solvers) can be still misleading. We present here a set of experiments of very small changes of a canonical Conflict Driven Clause Learning (CDCL) solver and show that even very close versions can lead to very different behaviors. In some cases, the best of them could perfectly have been used to convince the reader of the efficiency of a new method for SAT. This observation can be explained by the lack of real experimental studies of CDCL solvers.