Abstract
Traditionally, the propositional satisfiability problem (SAT) was attacked with systematic search algorithms, but more recently, local search methods were shown to be very effective for solving large and hard SAT instances. Generally, it is not well understood which type of algorithm performs best on a specific type of SAT instances. Here, we present results of a comprehensive empirical study, comparing the performance of some of the best performing stochastic local search and systematic search algorithms for SAT on a wide range of problem instances. Our experimental results suggest that, considering the specific strengths and weaknesses of both approaches, hybrid algorithms or portfolio combinations might be most effective for solving SAT problems in practice.
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