NLocalSAT: Boosting Local Search with Solution Prediction
This addresses a bottleneck in SAT solving for computer science applications, but it is incremental as it enhances existing SLS methods rather than introducing a new paradigm.
The paper tackles the problem of random initialization in stochastic local search (SLS) solvers for Boolean satisfiability (SAT) by proposing NLocalSAT, which uses a neural network to predict initial assignments. The results show that NLocalSAT improves five SLS solvers by 27% to 62% on SAT Competition 2018 instances.
The Boolean satisfiability problem (SAT) is a famous NP-complete problem in computer science. An effective way for solving a satisfiable SAT problem is the stochastic local search (SLS). However, in this method, the initialization is assigned in a random manner, which impacts the effectiveness of SLS solvers. To address this problem, we propose NLocalSAT. NLocalSAT combines SLS with a solution prediction model, which boosts SLS by changing initialization assignments with a neural network. We evaluated NLocalSAT on five SLS solvers (CCAnr, Sparrow, CPSparrow, YalSAT, and probSAT) with instances in the random track of SAT Competition 2018. The experimental results show that solvers with NLocalSAT achieve 27% ~ 62% improvement over the original SLS solvers.