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pro vyhledávání: '"Prouvost, Antoine"'
Autor:
Gasse, Maxime, Cappart, Quentin, Charfreitag, Jonas, Charlin, Laurent, Chételat, Didier, Chmiela, Antonia, Dumouchelle, Justin, Gleixner, Ambros, 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, Binbin, Chen, Minggui, He, Hao, Hao, Zhiyu, Zhang, Zhiwu, An, Kun, Mao
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 pr
Externí odkaz:
http://arxiv.org/abs/2203.02433
In this paper we describe Ecole (Extensible Combinatorial Optimization Learning Environments), a library to facilitate integration of machine learning in combinatorial optimization solvers. It exposes sequential decision making that must be performed
Externí odkaz:
http://arxiv.org/abs/2104.02828
Autor:
Prouvost, Antoine, Dumouchelle, Justin, Scavuzzo, Lara, Gasse, Maxime, Chételat, Didier, Lodi, Andrea
We present Ecole, a new library to simplify machine learning research for combinatorial optimization. Ecole exposes several key decision tasks arising in general-purpose combinatorial optimization solvers as control problems over Markov decision proc
Externí odkaz:
http://arxiv.org/abs/2011.06069
This paper surveys the recent attempts, both from the machine learning and operations research communities, at leveraging machine learning to solve combinatorial optimization problems. Given the hard nature of these problems, state-of-the-art algorit
Externí odkaz:
http://arxiv.org/abs/1811.06128
Publikováno v:
In European Journal of Operational Research 16 April 2021 290(2):405-421