Graph neural networks trained as oracles improve step counts and solved instances for stochastic local search SAT solvers on random and pseudo-industrial benchmarks while preserving theoretical bounds.
Battaglia, Razvan Pascanu, Matthew Lai, Danilo Rezende, and Koray Kavukcuoglu
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Using deep learning to construct stochastic local search SAT solvers with performance bounds
Graph neural networks trained as oracles improve step counts and solved instances for stochastic local search SAT solvers on random and pseudo-industrial benchmarks while preserving theoretical bounds.