Multi-flip networks: Parallelizing genSAT
Local hill-climbing algorithms to solve the satisfiability problem have shown to be more efficient than complete systematic methods in many aspects. Many variants and refinements have been developed in the last few years. We present a neural network approach to evaluate such local search algorithms in a parallel manner, i.e. enlarging the neighbourhood of each possible move in the search space. We present an approach which allows the simultaneous change of truth value assignment for more than one variable at a time, such that the theoretical properties of the considered algorithms are preserved, and give experimental evidence that this algorithm is indeed faster than the respective sequential variants.
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