{"type":"ScholarlyArticle","title":"Two-goal Local Search and Inference Rules for Minimum Dominating Set","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":"Wenying Hou","slug":"wenying-hou","given":"Wenying","family":"Hou","affiliation":"School of Software and Microelectronics , Peking University , China"},{"name":"Yiyuan Wang","slug":"yiyuan-wang","given":"Yiyuan","family":"Wang","affiliation":"School of Computer Science and Information Technology , Northeast Normal University , China"},{"name":"Chuan Luo","slug":"chuan-luo","given":"Chuan","family":"Luo","affiliation":"Microsoft Research , China"},{"name":"Qingwei Lin","slug":"qingwei-lin","given":"Qingwei","family":"Lin","affiliation":"Microsoft Research , China"}],"abstract":"Minimum dominating set (MinDS)  is a canonical NP-hard combinatorial optimization problem with applications. For large and hard instances one must resort to heuristic approaches to obtain good solutions within reasonable time. This paper develops an efficient local search algorithm for MinDS, which has two main ideas. The first one is a novel local search framework, while the second is a construction procedure with inference rules. Our algorithm named FastDS is evaluated on 4 standard benchmarks and 3 massive graphs benchmarks. FastDS obtains the best performance for almost all benchmarks, and obtains better solutions than state-of-the-art algorithms on massive graphs.","doi":"10.24963/ijcai.2020/204","url":"https://ijcai.org/proceedings/2020/204","pdf":"https://ijcai.org/proceedings/2020/0204.pdf","markdown":"https://ijcai.org/proceedings/2020/204.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/204","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Heuristic Search and Game Playing","track":"Main track","number":204,"pages":{"first":1467,"last":1473},"keywords":["Heuristic Search and Game Playing: Heuristic Search","Heuristic Search and Game Playing: Combinatorial Search and Optimisation"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/24694/file_24694.m3u8","length_sec":294,"provider":"hls"}],"references":[{"position":1,"raw":"[Abu-Khzam et al., 2017] Faisal N. 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