Boosting SAT Solver Performance via a New Hybrid Approach1
Author(s) -
Lei Fang,
Michael S. Hsiao
Publication year - 2008
Publication title -
journal on satisfiability boolean modeling and computation
Language(s) - English
Resource type - Journals
eISSN - 1875-5011
pISSN - 1574-0617
DOI - 10.3233/sat190058
Subject(s) - boosting (machine learning) , computer science , parallel computing , artificial intelligence
Due to the widespread demands for efficient SAT solvers in Electronic Design Automation applications, methods to boost the performance of the SAT solver are highly desired. We propose a Hybrid Solution to boost SAT solver performance in this paper, via an integration of local and DPLL-based search approaches. A local search is used to identify a subset of clauses from the original formula to be passed to a DPLL SAT solver incrementally until all the clauses have been passed. In addition, the solution obtained by the DPLL solver on the subset of clauses is fed back to the local search solver to jump over any locally optimal points. The proposed solution is highly portable to the existing SAT solvers. For satisfiable instances, up to an order of magnitude speedup was obtained via the proposed hybrid solver. For unsatisfiable instances, the speedup was smaller due to the overhead.
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