An effective learnt clause minimization approach for CDCL SAT solvers
Authors: 
Mao Luo, Chu-Min Li, Fan Xiao, Felip Manyà, Zhipeng LüAuthors Info & Claims
Mao Luo, Chu-Min Li, Fan Xiao, Felip Manyà, Zhipeng LüAuthors Info & ClaimsPages 703 - 711
Abstract
Learnt clauses in CDCL SAT solvers often contain redundant literals. This may have a negative impact on performance because redundant literals may deteriorate both the effectiveness of Boolean constraint propagation and the quality of subsequent learnt clauses. To overcome this drawback, we define a new inprocessing SAT approach which eliminates redundant literals from learnt clauses by applying Boolean constraint propagation. Learnt clause minimization is activated before the SAT solver triggers some selected restarts, and affects only some learnt clauses during the search process. Moreover, we conducted an empirical evaluation on instances coming from the hard combinatorial and application categories of recent SAT competitions. The results show that a remarkable number of additional instances are solved when the approach is incorporated into five of the best performing CDCL SAT solvers (Glucose, TC_Glucose, COMiniSatPS, MapleCOMSPS and MapleCOMSPS_LRB).
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- Australian Comp Soc: Australian Computer Society
- NSF: National Science Foundation
- Griffith University
- University of Technology Sydney
- AI Journal: AI Journal
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AAAI Press
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Published: 19 August 2017
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