Improving Implementation of SLS Solvers for SAT and New Heuristics for k-SAT with Long Clauses

  • Adrian Balint
  • Armin Biere
  • Andreas Fröhlich
  • Uwe Schöning
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8561)


Stochastic Local Search (SLS) solvers are considered one of the best solving technique for randomly generated problems and more recently also have shown great promise for several types of hard combinatorial problems. Within this work, we provide a thorough analysis of different implementation variants of SLS solvers on random and on hard combinatorial problems. By analyzing existing SLS implementations, we are able to discover new improvements inspired by CDCL solvers, which can speed up the search of all types of SLS solvers. Further, our analysis reveals that the multilevel break values of variables can be easily computed and used within the decision heuristic. By augmenting the probSAT solver with the new heuristic, we are able to reach new state-of-the-art performance on several types of SAT problems, especially on those with long clauses. We further provide a detailed analysis of the clause selection policy used in focused search SLS solvers.


Stochastic Local Search Decision Heuristic Candidate Clause Improve Implementation Stochastic Local Search Algorithm 
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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© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Adrian Balint
    • 1
  • Armin Biere
    • 2
  • Andreas Fröhlich
    • 2
  • Uwe Schöning
    • 1
  1. 1.Institute of Theoretical Computer ScienceUlm UniversityUlmGermany
  2. 2.Inst. Formal Models and VerificationJohannes Kepler UniversityLinzAustria

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