{"type":"ScholarlyArticle","title":"An Effective Learnt Clause Minimization Approach for CDCL SAT Solvers","authors":[{"name":"Mao Luo","slug":"mao-luo","given":"Mao","family":"Luo","affiliation":"School of Computer Science , Huazhong University of Science and Technology , Wuhan , China"},{"name":"Chu-Min Li","slug":"chu-min-li","given":"Chu-Min","family":"Li","affiliation":"MIS , University of Picardie Jules Verne , Amiens , France"},{"name":"Fan Xiao","slug":"fan-xiao","given":"Fan","family":"Xiao","affiliation":"School of Computer Science , Huazhong University of Science and Technology , Wuhan , China"},{"name":"Felip Manyà","slug":"felip-manya","given":"Felip","family":"Manyà","affiliation":"Artificial Intelligence Research Institute (IIIA-CSIC) , Barcelona , Spain; School of Computer Science , Huazhong University of Science and Technology , Wuhan , China"},{"name":"Zhipeng Lü","slug":"zhipeng-lu","given":"Zhipeng","family":"Lü","affiliation":""}],"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).","doi":"10.24963/ijcai.2017/98","url":"https://ijcai.org/proceedings/2017/98","pdf":"https://ijcai.org/proceedings/2017/0098.pdf","markdown":"https://ijcai.org/proceedings/2017/98.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/98","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Constraints and Satisfiability","track":"Main track","number":98,"pages":{"first":703,"last":711},"keywords":["Constraints and Satisfiability: Solvers and Tools","Constraints and Satisfiability: Constraints and Satisfiability","Constraints and Satisfiability: Satisfiability"],"videos":[],"references":[{"position":1,"raw":"[Ansótegui et al., 2012] Carlos Ansótegui, Jesús Giráldez- Cru, and Jordi Levy. 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