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

  • Luís Baptista
  • João Marques-Silva
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1894)


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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Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Luís Baptista
    • 1
  • João Marques-Silva
    • 1
  1. 1.Department of InformaticsTechnical University of Lisbon, IST/INESC/CELLisbonPortugal

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