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Extending Clause Learning of SAT Solvers with Boolean Gröbner Bases

  • Christoph Zengler
  • Wolfgang Küchlin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6244)

Abstract

We extend clause learning as performed by most modern SAT Solvers by integrating the computation of Boolean Gröbner bases into the conflict learning process. Instead of learning only one clause per conflict, we compute and learn additional binary clauses from a Gröbner basis of the current conflict. We used the Gröbner basis engine of the logic package Redlog contained in the computer algebra system Reduce to extend the SAT solver MiniSAT with Gröbner basis learning. Our approach shows a significant reduction of conflicts and a reduction of restarts and computation time on many hard problems from the SAT 2009 competition.

Keywords

Computer Algebra System Conjunctive Normal Form Decision Node Unit Clause Bound Model Check 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Christoph Zengler
    • 1
  • Wolfgang Küchlin
    • 1
  1. 1.Symbolic Computation Group, W. Schickard-Institute for InformaticsUniversität TübingenGermany

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