Fetching the paper…
Reading the bibliography…
We present Ecole, a new library to simplify machine learning research for combinatorial optimization.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J Williams · 1992
Earlier work this paper cites.
Branch-and-cut algorithms for combinatorial optimization problems
John E Mitchell · 2002
Earlier work this paper cites.
The sharpest cut: The impact of Manfred Padberg and his work
Martin Grötschel · 2004
Earlier work this paper cites.
Production planning by mixed integer programming
Yves Pochet and Laurence A Wolsey · 2006
Earlier work this paper cites.
Mixed integer programming: A historical perspective with xpress-mp
Robert Ashford · 2007
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
The numpy array: a structure for efficient numerical computation
Stefan Van Der Walt, S Chris Colbert, and Gael Varoquaux · 2011
Earlier work this paper cites.
Guiding combinatorial optimization with uct
Ashish Sabharwal, Horst Samulowitz, and Chandra Reddy · 2012
Earlier work this paper cites.
Mixed Integer Programming: Analyzing 12 Years of Progress , pages 449–481
Tobias Achterberg and Roland Wunderling · 2013
Earlier work this paper cites.
The Arcade Learning Environment: An evaluation platform for general agents
Marc G Bellemare, Yavar Naddaf, Joel Veness, and Michael Bowling · 2013
Earlier work this paper cites.
Learning to search in branch and bound algorithms
He He, Hal Daume III, and Jason M Eisner · 2014
Earlier work this paper cites.
The bsd 3-clause license
Open Source Initiative et al · 2015
Earlier work this paper cites.
Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba · 2016
Earlier work this paper cites.
Dash: Dynamic approach for switching heuristics
Giovanni Di Liberto, Serdar Kadioglu, Kevin Leo, and Yuri Malitsky · 2016
Earlier work this paper cites.
Learning to branch in mixed integer programming
Elias B. Khalil, Pierre Le Bodic, Le Song, George L. Nemhauser, and Bistra Dilkina · 2016
Earlier work this paper cites.
PySCIPOpt: Mathematical programming in python with the SCIP optimization suite
Stephen Maher, Matthias Miltenberger, João Pedro Pedroso, Daniel Rehfeldt, Robert Schwarz, and Felipe Serrano · 2016
Cited alongside, same era.
A machine learning-based approximation of strong branching
Alejandro M. Alvarez, Quentin Louveaux, and Louis Wehenkel · 2017
Cited alongside, same era.
Neural combinatorial optimization with reinforcement learning
Irwan Bello, Hieu Pham, Quoc V. Le, Mohammad Norouzi, and Samy Bengio · 2017
Cited alongside, same era.
Learning combinatorial optimization algorithms over graphs
Hanjun Dai, Elias B. Khalil, Yuyu Zhang, Bistra Dilkina, and Le Song · 2017
Cited alongside, same era.
Learning to run heuristics in tree search
Elias B. Khalil, Bistra Dilkina, George L. Nemhauser, Shabbir Ahmed, and Yufen Shao · 2017
Cited alongside, same era.
Learning to branch
Maria-Florina Balcan, Travis Dick, Tuomas Sandholm, and Ellen Vitercik · 2018
Grandmaster level in StarCraft II using multi-agent reinforcement learning
Oriol Vinyals, Igor Babuschkin, Wojciech M. Czarnecki, Michaël Mathieu, Andrew Dudzik, Junyoung Chung, David H. Choi, Richard Powell, Timo Ewalds, Petko Georgiev, Junhyuk Oh, Dan Horgan, Manuel Kroiss, Ivo Danihelka, Aja Huang, Laurent Sifre, Trevor Cai, John P. Agapiou, Max Jaderberg, Alexander Sasha Vezhnevets, Rémi Leblond, Tobias Pohlen, Valentin Dalibard, David Budden, Yury Sulsky, James Molloy, Tom L. Paine, Çaglar Gülçehre, Ziyu Wang, Tobias Pfaff, Yuhuai Wu, Roman Ring, Dani Yogatama, Dario Wünsch, Katrina McKinney, Oliver Smith, Tom Schaul, Timothy P. Lillicrap, Koray Kavukcuoglu, Demis Hassabis, Chris Apps, and David Silver · 2019
Later among the works it cites.
Machine learning for combinatorial optimization: a methodological tour d’horizon
Yoshua Bengio, Andrea Lodi, and Antoine Prouvost · 2020
Closest in time.
CPLEX Optimizer User Manual, 2020
IBM CPLEX · 2020
Closest in time.
The SCIP Optimization Suite 7.0
Gerald Gamrath, Daniel Anderson, Ksenia Bestuzheva, Wei-Kun Chen, Leon Eifler, Maxime Gasse, Patrick Gemander, Ambros Gleixner, Leona Gottwald, Katrin Halbig, Gregor Hendel, Christopher Hojny, Thorsten Koch, Pierre Le Bodic, Stephen J. Maher, Frederic Matter, Matthias Miltenberger, Erik Mühmer, Benjamin Müller, Marc E. Pfetsch, Franziska Schlösser, Felipe Serrano, Yuji Shinano, Christine Tawfik, Stefan Vigerske, Fabian Wegscheider, Dieter Weninger, and Jakob Witzig · 2020
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Learning a classification of mixed-integer quadratic programming problems
Pierre Bonami, Andrea Lodi, and Giulia Zarpellon · 2018
Cited alongside, same era.
Cuts, primal heuristics, and learning to branch for the time-dependent traveling salesman problem
Christoph Hansknecht, Imke Joormann, and Sebastian Stiller · 2018
Cited alongside, same era.
Adaptive algorithmic behavior for solving mixed integer programs using bandit algorithms
Gregor Hendel, Matthias Miltenberger, and Jakob Witzig · 2018
Cited alongside, same era.
A general reinforcement learning algorithm that masters chess, shogi, and go through self-play
David Silver, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Matthew Lai, Arthur Guez, Marc Lanctot, Laurent Sifre, Dharshan Kumaran, Thore Graepel, Timothy Lillicrap, Karen Simonyan, and Demis Hassabis · 2018
Cited alongside, same era.
Learning to search via retrospective imitation
Jialin Song, Ravi Lanka, Albert Zhao, Yisong Yue, and Masahiro Ono · 2018
Cited alongside, same era.
Optimal solution predictions for mixed integer programs
Jian-Ya Ding, Chao Zhang, Lei Shen, Shengyin Li, Bing Wang, Yinghui Xu, and Le Song · 2019
Cited alongside, same era.
Hybrid models for learning to branch
Prateek Gupta, Maxime Gasse, Elias B Khalil, M Pawan Kumar, Andrea Lodi, and Yoshua Bengio · 2020
Closest in time.
Gurobi Optimizer Reference Manual, 2020
Gurobi Optimization LLC · 2020
Closest in time.
Or-gym: A reinforcement learning library for operations research problems, 2020
Christian D. Hubbs, Hector D. Perez, Owais Sarwar, Nikolaos V. Sahinidis, Ignacio E. Grossmann, and John M. Wassick · 2020
Closest in time.
Conda Package Manager, 2020
Anaconda Inc · 2020
Closest in time.
Machine-learning-based column selection for column generation
Mouad Morabit, Guy Desaulniers, and Andrea Lodi · 2020
Closest in time.
Reinforcement learning for integer programming: Learning to cut
Yunhao Tang, Shipra Agrawal, and Yuri Faenza · 2020
Closest in time.
MIPLearn, 2020
Alinson S Xavier and Feng Qiu · 2020
Closest in time.
Learning efficient search approximation in mixed integer branch and bound
Kaan Yilmaz and Neil Yorke-Smith · 2020
Closest in time.
Parameterizing branch-and-bound search trees to learn branching policies
Giulia Zarpellon, Jason Jo, Andrea Lodi, and Yoshua Bengio · 2020
Closest in time.
OpenGraphGym: A parallel reinforcement learning framework for graph optimization problems
Weijian Zheng, Dali Wang, and Fengguang Song · 2020
Closest in time.