Effective Preprocessing with Hyper-Resolution and Equality Reduction

  • Fahiem Bacchus
  • Jonathan Winter
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2919)


HypBinRes, a particular form of hyper-resolution, was first employed in the SAT solver 2cls+eq. In 2cls+eq, HypBinRes and equality reduction are used at every node of a DPLL search tree, pruning much of the search tree. This allowed 2cls+eq to display the best all-around performance in the 2002 SAT solver competition. In particular, it was the only solver to qualify for the second round of the competition in all three benchmark categories. In this paper we investigate the use of HypBinRes and equality reduction in a preprocessor that can be used to simplify a CNF formula prior to SAT solving. We present empirical evidence demonstrating that such a preprocessor can be extremely effective on large structured problems, making some previously unsolvable problems solvable. The preprocessor is also able to solve a number of non-trivial instances by itself. Since the preprocessor does not have to worry about undoing changes on backtrack, nor about keeping track of reasons for intelligent backtracking, we are able to develop a new algorithm for applying HypBinRes that can be orders of magnitude more efficient than the algorithm employed inside of 2cls+eq. The net result is a technique that improves our ability to solve hard problems SAT problems.


Inference Rule Unit Propagation Transitive Closure Graph Search Unit Clause 
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 2004

Authors and Affiliations

  • Fahiem Bacchus
    • 1
  • Jonathan Winter
    • 1
  1. 1.Department of Computer ScienceUniversity of Of TorontoTorontoCanada

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