PSATO: a distributed propositional prover and its application to quasigroup problems
Journal of Symbolic Computation - Special issue on parallel symbolic computation
Specifying Latin square problems in propositional logic
Automated reasoning and its applications
GRASP: A Search Algorithm for Propositional Satisfiability
IEEE Transactions on Computers
A Computing Procedure for Quantification Theory
Journal of the ACM (JACM)
A machine program for theorem-proving
Communications of the ACM
Implementing the Davis–Putnam Method
Journal of Automated Reasoning
Using Randomization and Learning to Solve Hard Real-World Instances of Satisfiability
CP '02 Proceedings of the 6th International Conference on Principles and Practice of Constraint Programming
SATO: An Efficient Propositional Prover
CADE-14 Proceedings of the 14th International Conference on Automated Deduction
Completing the spectrum of r-orthogonal Latin squares
Discrete Mathematics
Between restarts and backjumps
SAT'11 Proceedings of the 14th international conference on Theory and application of satisfiability testing
Hi-index | 0.00 |
The restart strategy can improve the effectiveness of SAT solvers for satisfiable problems. In 2002, we proposed the so-called random jump strategy, which outperformed the restart strategy in most experiments. One weakness shared by both the restart strategy and the random jump strategy is the ineffectiveness for unsatisfiable problems: A job which can be finished by a SAT solver in one day cannot not be finished in a couple of days if either strategy is used by the same SAT solver. In this paper, we propose a simple and effective technique which makes the random jump strategy as effective as the original SAT solvers. The technique works as follows: When we jump from the current position to another position, we remember the skipped search space in a simple data structure called “guiding path”. If the current search runs out of search space before running out of the allotted time, the search can be recharged with one of the saved guiding paths and continues. Because the overhead of saving and loading guiding paths is very small, the SAT solvers is as effective as before for unsatisfiable problems when using the proposed technique.