1 code implementation • 13 Dec 2023 • Mark Turner, Antonia Chmiela, Thorsten Koch, Michael Winkler
Due to the large amount of available ML frameworks and the complexity of many ML predictors, formulating such predictors into MIPs is a highly non-trivial task.
no code implementations • 4 Apr 2023 • Antonia Chmiela, Ambros Gleixner, Pawel Lichocki, Sebastian Pokutta
In this work, we propose an online learning approach that adapts the application of heuristics towards the single instance at hand.
2 code implementations • 4 Mar 2022 • Maxime Gasse, Quentin Cappart, Jonas Charfreitag, Laurent Charlin, Didier Chételat, Antonia Chmiela, Justin Dumouchelle, Ambros Gleixner, Aleksandr M. Kazachkov, Elias Khalil, Pawel Lichocki, Andrea Lodi, Miles Lubin, Chris J. Maddison, Christopher Morris, Dimitri J. Papageorgiou, Augustin Parjadis, Sebastian Pokutta, Antoine Prouvost, Lara Scavuzzo, Giulia Zarpellon, Linxin Yang, Sha Lai, Akang Wang, Xiaodong Luo, Xiang Zhou, Haohan Huang, Shengcheng Shao, Yuanming Zhu, Dong Zhang, Tao Quan, Zixuan Cao, Yang Xu, Zhewei Huang, Shuchang Zhou, Chen Binbin, He Minggui, Hao Hao, Zhang Zhiyu, An Zhiwu, Mao Kun
Combinatorial optimization is a well-established area in operations research and computer science.
1 code implementation • NeurIPS 2021 • Antonia Chmiela, Elias Boutros Khalil, Ambros Gleixner, Andrea Lodi, Sebastian Pokutta
Compared to the default settings of a state-of-the-art academic MIP solver, we are able to reduce the average primal integral by up to 49% on two classes of challenging instances.
1 code implementation • NeurIPS 2021 • Antonia Chmiela, Elias B. Khalil, Ambros Gleixner, Andrea Lodi, Sebastian Pokutta
In this work, we propose the first data-driven framework for scheduling heuristics in an exact MIP solver.