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This paper surveys the recent attempts at leveraging machine learning to solve constrained optimization problems.
Maximization of a linear function of variables subject to linear inequalities
George B Dantzig · 1951
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Multiplier and gradient methods
Magnus R Hestenes · 1969
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A method for nonlinear constraints in minimization problems
Michael JD Powell · 1969
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Neural computation of decisions in optimization problems
John Hopfield and D Tank · 1985
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On the stability of the travelling salesman problem algorithm of hopfield and tank
GV Wilson and GS Pawley · 1988
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Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J Williams · 1992
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Convex optimization
Stephen Boyd, Stephen P Boyd, and Lieven Vandenberghe · 2004
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Numerical optimization
Jorge Nocedal and Stephen Wright · 2006
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Handbook of constraint programming
Francesca Rossi, Peter Van Beek, and Toby Walsh · 2006
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Learning restart strategies
Matteo Gagliolo and Jürgen Schmidhuber · 2007
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The graph neural network model
Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini · 2008
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Deep learning
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton · 2015
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Neural machine translation by jointly learning to align and translate, 2016
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio · 2016
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Learning to branch in mixed integer programming
Elias Khalil, Pierre Le Bodic, Le Song, George Nemhauser, and Bistra Dilkina · 2016
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Optnet: Differentiable optimization as a layer in neural networks
Brandon Amos and J Zico Kolter · 2017
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Neural combinatorial optimization with reinforcement learning
Irwan Bello, Hieu Pham, Quoc V. Le, Mohammad Norouzi, and Samy Bengio · 2017
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Learning to run heuristics in tree search
Elias B. Khalil, Bistra Dilkina, George L. Nemhauser, Shabbir Ahmed, and Yufen Shao · 2017
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On learning and branching: a survey
Andrea Lodi and Giulia Zarpellon · 2017
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Gap safe screening rules for sparsity enforcing penalties
Eugene Ndiaye, Olivier Fercoq, Alexandre Gramfort, and Joseph Salmon · 2017
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Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio · 2017
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Oriol Vinyals, Meire Fortunato, and Navdeep Jaitly · 2017
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Maria-Florina Balcan, Travis Dick, Tuomas Sandholm, and Ellen Vitercik · 2018
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Learning combinatorial optimization algorithms over graphs
Hanjun Dai, Elias B. Khalil, Yuyu Zhang, Bistra Dilkina, and Le Song · 2018
Differentiation of blackbox combinatorial solvers
Marin Vlastelica Pogančić, Anselm Paulus, Vit Musil, Georg Martius, and Michal Rolinek · 2019
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Satnet: Bridging deep learning and logical reasoning using a differentiable satisfiability solver
Po-Wei Wang, Priya Donti, Bryan Wilder, and Zico Kolter · 2019
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Melding the data-decisions pipeline: Decision-focused learning for combinatorial optimization
Bryan Wilder, Bistra Dilkina, and Milind Tambe · 2019
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Machine learning for combinatorial optimization: a methodological tour d’horizon
Yoshua Bengio, Andrea Lodi, and Antoine Prouvost · 2020
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Learning with differentiable perturbed optimizers
Quentin Berthet, Mathieu Blondel, Olivier Teboul, Marco Cuturi, Jean-Philippe Vert, and Francis Bach · 2020
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Neural networks and physical systems with emergent collective computational abilities
J.J. Hopfield · 2018
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Reinforcement learning for solving the vehicle routing problem
Mohammadreza Nazari, Afshin Oroojlooy, Lawrence V Snyder, and Martin Takáč · 2018
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Statistical learning for dc optimal power flow
Yeesian Ng, Sidhant Misra, Line A Roald, and Scott Backhaus · 2018
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Revised note on learning algorithms for quadratic assignment with graph neural networks, 2018
Alex Nowak, Soledad Villar, Afonso S. Bandeira, and Joan Bruna · 2018
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Reinforcement learning: An introduction
Richard S Sutton and Andrew G Barto · 2018
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Po-Wei Wang, Wei-Cheng Chang, and J. Zico Kolter · 2018
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Graph neural networks: A review of methods and applications
Jie Zhou, Ganqu Cui, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, Lifeng Wang, Changcheng Li, and Maosong Sun · 2018
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Fabrizio Detassis, Michele Lombardi, and Michela Milano · 2020
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Smart ”predict, then optimize”
Adam N. Elmachtoub and Paul Grigas · 2020
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Mipaal: Mixed integer program as a layer
Aaron Ferber, Bryan Wilder, Bistra Dilkina, and Milind Tambe · 2020
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Lagrangian Duality for Constrained Deep Learning
Ferdinando Fioretto, Pascal Van Hentenryck, Terrence W. K. Mak, Cuong Tran, Federico Baldo, and Michele Lombardi · 2020
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Predicting ac optimal power flows: Combining deep learning and lagrangian dual methods
Ferdinando Fioretto, Terrence W.K. Mak, and Pascal Van Hentenryck · 2020
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Hybrid models for learning to branch
Prateek Gupta, Maxime Gasse, Elias B. Khalil, M. Pawan Kumar, Andrea Lodi, and Yoshua Bengio · 2020
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Interior point solving for lp-based prediction+optimisation
Jayanta Mandi and Tias Guns · 2020
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Smart predict-and-optimize for hard combinatorial optimization problems
Jayanta Mandi, Peter J Stuckey, Tias Guns, et al · 2020
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Learning active constraints to efficiently solve bilevel problems
Eléa Prat and Spyros Chatzivasileiadis · 2020
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A general large neighborhood search framework for solving integer linear programs
Jialin Song, ravi lanka, Yisong Yue, and Bistra Dilkina · 2020
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Reinforcement learning for integer programming: Learning to cut
Yunhao Tang, Shipra Agrawal, and Yuri Faenza · 2020
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Differentially private and fair deep learning: A lagrangian dual approach
Cuong Tran, Ferdinando Fioretto, and Pascal Van Hentenryck · 2020
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Combining deep learning and optimization for security-constrained optimal power flow
Alexandre Velloso and Pascal Van Hentenryck · 2020
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