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Engineering a Lightweight and Efficient Local Search SAT Solver

  • Adrian Balint
  • Uwe SchöningEmail author
Chapter
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9220)

Abstract

One important category of SAT solver implementations use stochastic local search (SLS, for short). These solvers try to find a satisfying assignment for the input Boolean formula (mostly, required to be in CNF) by modifying the (mostly randomly chosen) initial assignment by bit flips until a satisfying assignment is possibly reached. Usually such SLS type algorithms proceed in a greedy fashion by increasing the number of satisfied clauses until some local optimum is reached. Trying to find its way out of such local optima typically requires the use of randomness. We present an easy, straightforward SLS type SAT solver, called probSAT, which uses just one simple strategy being based on biased probabilistic flips. Within an extensive empirical study we evaluate the current state-of-the-art solvers on a wide range of SAT problems, and show that our approach is able to exceed the performance of other solving techniques.

Notes

Acknowledgments

We would like to thank the BWGrid [8] project for providing the computational resources. This project was funded by the Deutsche Forschungsgemeinschaft (DFG) under the number SCHO 302/9-1. We thank Daniel Diepold and Simon Gerber for implementing the F-race configurator and providing different analysis tools within the EDACC framework. We would also like to thank Andreas Fröhlich for fruitful discussions on this topic and Armin Biere for helpful suggestions regarding code optimizations.

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© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Institute of Theoretical Computer ScienceUlm UniversityUlmGermany

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