Lu Zhang, Ge Li, Wenjie Zhang, Qihao Zhu, Zeyu Sun, Yingfei Xiong, Shaowei Cai
We have not lifted any functions out of this paper's repositories yet, so there is nothing we have run. If it links a repository, it is listed below.
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.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2001.09398")
get_code_for_paper("2001.09398")
have("2001.09398")
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