Chapter

Principles and Practice of Constraint Programming – CP 2000

Volume 1894 of the series Lecture Notes in Computer Science pp 489-494

Date:

Using Randomization and Learning to Solve Hard Real-World Instances of Satisfiability

  • Luís BaptistaAffiliated withDepartment of Informatics, Technical University of Lisbon, IST/INESC/CEL
  • , João Marques-SilvaAffiliated withDepartment of Informatics, Technical University of Lisbon, IST/INESC/CEL

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Abstract

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.