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A heuristic to find initial values for stochastic local search in SAT using continuous extensions of Boolean formulas

P. 1–4.
Kascheev N. I., Путихин Н. С.

Stochastic Local Search (SLS) is one of the most popular approaches to Boolean satisfiability problem and solvers based on this algorithm have made a substantial progress over the years. However, nearly all state of the art SLS solvers do not attempt to find a good starting point, instead using random values. We present a heuristic for finding an initial assignment based on non-linear optimization of continuous extension of given Boolean formula. This heuristic works by optimizing continuous function that represents the formula and then converting the result into discrete values. We also provide experimental evaluation of new heuristic implemented in ProbSAT solver.

 

Language: English
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Keywords: nonlinear optimizationContinuous functionSAT solvingdiscrete valuesProbSAT solver initial valuesstochastic local searchBoolean formulasBoolean satisfiability problemrandom valuesSLS solvers

In book

Proceedings of XV IEEE East-West Design & Test Symposium (EWDTS'2017)
Piscataway: IEEE, 2017.
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