{"type":"ScholarlyArticle","title":"Deep Cooperation of CDCL and Local Search for SAT (Extended Abstract)","authors":[{"name":"Shaowei Cai","slug":"shaowei-cai","given":"Shaowei","family":"Cai","affiliation":"State Key Laboratory of Computer Science , Institute of Software , Chinese Academy of Sciences , China; School of Computer Science and Technology , University of Chinese Academy of Sciences , China"},{"name":"Xindi Zhang","slug":"xindi-zhang","given":"Xindi","family":"Zhang","affiliation":"State Key Laboratory of Computer Science , Institute of Software , Chinese Academy of Sciences , China; School of Computer Science and Technology , University of Chinese Academy of Sciences , China"}],"abstract":"Modern SAT solvers are based on a paradigm named conflict driven clause learning (CDCL), while local search is an important alternative. Although there have been attempts combining these two methods, this work proposes deeper cooperation techniques. First, we relax the CDCL framework by extending  promising branches to complete assignments and calling a local search solver to search for a model nearby. More importantly, the local search assignments and the conflict frequency of variables in local search are exploited in the phase selection and branching heuristics of CDCL. We use our techniques to improve three typical CDCL solvers (glucose, MapleLCMDistChronoBT and Kissat). Experiments on  benchmarks from the Main tracks of SAT Competitions 2017-2020 and a real world benchmark of spectrum allocation show that the  techniques bring significant improvements, particularly on satisfiable instances.  A resulting solver won the Main Track SAT category in SAT Competition 2020 and also performs very well on the spectrum allocation benchmark. As far as we know, this is the first work that meets the standard of the  challenge  ``Demonstrate the successful combination of stochastic search and systematic search techniques, by the creation of a new algorithm that outperforms the best previous examples of both approaches.'' (AAAI 1997) on standard application benchmarks.","doi":"10.24963/ijcai.2022/734","url":"https://ijcai.org/proceedings/2022/734","pdf":"https://ijcai.org/proceedings/2022/0734.pdf","markdown":"https://ijcai.org/proceedings/2022/734.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/734","volume":{"slug":"2022","title":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2022","url":"https://ijcai.org/proceedings/2022/","year":2022,"location":"Vienna","dates":"23-29 July 2022","editors":"Luc De Raedt","isbn":"978-1-956792-00-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2022"},"section":"Sister Conferences Best Papers","track":"Sister Conferences Best Papers","number":734,"pages":{"first":5274,"last":5278},"keywords":["Artificial Intelligence: General"],"videos":[{"label":"Video #1","url":"https://slideslive.com/38987263","length_sec":103,"provider":"slideslive"},{"label":"Video #2","url":"https://slideslive.com/38985406","length_sec":627,"provider":"slideslive"}],"references":[{"position":1,"raw":"[Audemard and Simon, 2009] Gilles Audemard and Laurent Simon. 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