The Machine Learning for Combinatorial Optimization Competition (ML4CO): Results and Insights
Combinatorial optimization is a well-established area in operations research and computer science. Until recently, its methods have focused on solving problem instances in isolation, ignoring that they often stem from related data distributions in practice. However, recent years have seen a surge of...
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| creator | Gasse, Maxime Cappart, Quentin Charfreitag, Jonas Charlin, Laurent Chételat, Didier Chmiela, Antonia Dumouchelle, Justin Ambros Gleixner Kazachkov, Aleksandr M Khalil, Elias Lichocki, Pawel Lodi, Andrea Lubin, Miles Maddison, Chris J Morris, Christopher Papageorgiou, Dimitri J Parjadis, Augustin Pokutta, Sebastian Prouvost, Antoine Scavuzzo, Lara Zarpellon, Giulia Yang, Linxin Lai, Sha Wang, Akang Luo, Xiaodong Zhou, Xiang Huang, Haohan Shao, Shengcheng Zhu, Yuanming Zhang, Dong Quan, Tao Cao, Zixuan Xu, Yang Huang, Zhewei Zhou, Shuchang Chen, Binbin He Minggui Hao, Hao Zhang, Zhiyu An Zhiwu Mao Kun |
| description | Combinatorial optimization is a well-established area in operations research and computer science. Until recently, its methods have focused on solving problem instances in isolation, ignoring that they often stem from related data distributions in practice. However, recent years have seen a surge of interest in using machine learning as a new approach for solving combinatorial problems, either directly as solvers or by enhancing exact solvers. Based on this context, the ML4CO aims at improving state-of-the-art combinatorial optimization solvers by replacing key heuristic components. The competition featured three challenging tasks: finding the best feasible solution, producing the tightest optimality certificate, and giving an appropriate solver configuration. Three realistic datasets were considered: balanced item placement, workload apportionment, and maritime inventory routing. This last dataset was kept anonymous for the contestants. |
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Until recently, its methods have focused on solving problem instances in isolation, ignoring that they often stem from related data distributions in practice. However, recent years have seen a surge of interest in using machine learning as a new approach for solving combinatorial problems, either directly as solvers or by enhancing exact solvers. Based on this context, the ML4CO aims at improving state-of-the-art combinatorial optimization solvers by replacing key heuristic components. The competition featured three challenging tasks: finding the best feasible solution, producing the tightest optimality certificate, and giving an appropriate solver configuration. Three realistic datasets were considered: balanced item placement, workload apportionment, and maritime inventory routing. This last dataset was kept anonymous for the contestants.</description><identifier>EISSN: 2331-8422</identifier><language>eng</language><publisher>Ithaca: Cornell University Library, arXiv.org</publisher><subject>Combinatorial analysis ; Competition ; Computer networks ; Datasets ; Machine learning ; Operations research ; Optimization ; Solvers</subject><ispartof>arXiv.org, 2022-03</ispartof><rights>2022. This work is published under http://arxiv.org/licenses/nonexclusive-distrib/1.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>781,785</link.rule.ids></links><search><creatorcontrib>Gasse, Maxime</creatorcontrib><creatorcontrib>Cappart, Quentin</creatorcontrib><creatorcontrib>Charfreitag, Jonas</creatorcontrib><creatorcontrib>Charlin, Laurent</creatorcontrib><creatorcontrib>Chételat, Didier</creatorcontrib><creatorcontrib>Chmiela, Antonia</creatorcontrib><creatorcontrib>Dumouchelle, Justin</creatorcontrib><creatorcontrib>Ambros Gleixner</creatorcontrib><creatorcontrib>Kazachkov, Aleksandr M</creatorcontrib><creatorcontrib>Khalil, Elias</creatorcontrib><creatorcontrib>Lichocki, Pawel</creatorcontrib><creatorcontrib>Lodi, Andrea</creatorcontrib><creatorcontrib>Lubin, Miles</creatorcontrib><creatorcontrib>Maddison, Chris J</creatorcontrib><creatorcontrib>Morris, Christopher</creatorcontrib><creatorcontrib>Papageorgiou, Dimitri J</creatorcontrib><creatorcontrib>Parjadis, Augustin</creatorcontrib><creatorcontrib>Pokutta, Sebastian</creatorcontrib><creatorcontrib>Prouvost, Antoine</creatorcontrib><creatorcontrib>Scavuzzo, Lara</creatorcontrib><creatorcontrib>Zarpellon, Giulia</creatorcontrib><creatorcontrib>Yang, Linxin</creatorcontrib><creatorcontrib>Lai, Sha</creatorcontrib><creatorcontrib>Wang, Akang</creatorcontrib><creatorcontrib>Luo, Xiaodong</creatorcontrib><creatorcontrib>Zhou, Xiang</creatorcontrib><creatorcontrib>Huang, Haohan</creatorcontrib><creatorcontrib>Shao, Shengcheng</creatorcontrib><creatorcontrib>Zhu, Yuanming</creatorcontrib><creatorcontrib>Zhang, Dong</creatorcontrib><creatorcontrib>Quan, Tao</creatorcontrib><creatorcontrib>Cao, Zixuan</creatorcontrib><creatorcontrib>Xu, Yang</creatorcontrib><creatorcontrib>Huang, Zhewei</creatorcontrib><creatorcontrib>Zhou, Shuchang</creatorcontrib><creatorcontrib>Chen, Binbin</creatorcontrib><creatorcontrib>He Minggui</creatorcontrib><creatorcontrib>Hao, Hao</creatorcontrib><creatorcontrib>Zhang, Zhiyu</creatorcontrib><creatorcontrib>An Zhiwu</creatorcontrib><creatorcontrib>Mao Kun</creatorcontrib><title>The Machine Learning for Combinatorial Optimization Competition (ML4CO): Results and Insights</title><title>arXiv.org</title><description>Combinatorial optimization is a well-established area in operations research and computer science. 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Chmiela, Antonia ; Dumouchelle, Justin ; Ambros Gleixner ; Kazachkov, Aleksandr M ; Khalil, Elias ; Lichocki, Pawel ; Lodi, Andrea ; Lubin, Miles ; Maddison, Chris J ; Morris, Christopher ; Papageorgiou, Dimitri J ; Parjadis, Augustin ; Pokutta, Sebastian ; Prouvost, Antoine ; Scavuzzo, Lara ; Zarpellon, Giulia ; Yang, Linxin ; Lai, Sha ; Wang, Akang ; Luo, Xiaodong ; Zhou, Xiang ; Huang, Haohan ; Shao, Shengcheng ; Zhu, Yuanming ; Zhang, Dong ; Quan, Tao ; Cao, Zixuan ; Xu, Yang ; Huang, Zhewei ; Zhou, Shuchang ; Chen, Binbin ; He Minggui ; Hao, Hao ; Zhang, Zhiyu ; An Zhiwu ; Mao Kun</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-proquest_journals_26370433463</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Combinatorial analysis</topic><topic>Competition</topic><topic>Computer networks</topic><topic>Datasets</topic><topic>Machine learning</topic><topic>Operations 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| subjects | Combinatorial analysis Competition Computer networks Datasets Machine learning Operations research Optimization Solvers |
| title | The Machine Learning for Combinatorial Optimization Competition (ML4CO): Results and Insights |
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