Using Randomization and Learning to Solve Hard Real-World Instances of Satisfiability
This paper addresses the interaction between randomization, with restart strategies, and learning, an often crucial technique for proving unsatisfiability. We use instances of SAT from the hardware verification domain to provide evidence that randomization can indeed be essential in solving real-world satisfiable instances of SAT. More interestingly, our results indicate that randomized restarts and learning may cooperate in proving both satisfiability and unsatisfiability. Finally, we utilize and expand the idea of algorithm portfolio design to propose an alternative approach for solving hard unsatisfiable instances of SAT.
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