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Cutting plane selection is a subroutine used in all modern mixed-integer linear programming solvers with the goal of selecting a subset of generated cuts that induce optimal solver performance.
Cutting planes in integer and mixed integer programming
Hugues Marchand, Alexander Martin, Robert Weismantel, and Laurence Wolsey · 2002
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Constraint integer programming
Tobias Achterberg · 2007
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Embedding { \{ 0, 1 / 2 } 1/2\} -cuts in a branch-and-cut framework: A computational study
Giuseppe Andreello, Alberto Caprara, and Matteo Fischetti · 2007
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Implementing cutting plane management and selection techniques
Franz Wesselmann and U Stuhl · 2012
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Mixed integer programming: Analyzing 12 years of progress
Tobias Achterberg and Roland Wunderling · 2013
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Pyscipopt: Mathematical programming in python with the scip optimization suite
Stephen Maher, Matthias Miltenberger, Joao Pedro Pedroso, Daniel Rehfeldt, Robert Schwarz, and Felipe Serrano · 2016
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Deep learning
Ian Goodfellow, Yoshua Bengio, and Aaron Courville · 2016
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Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton · 2016
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Reinforcement learning: An introduction
Richard S Sutton and Andrew G Barto · 2018
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Learning to branch
Maria-Florina Balcan, Travis Dick, Tuomas Sandholm, and Ellen Vitercik · 2018
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Theoretical challenges towards cutting-plane selection
Santanu S Dey and Marco Molinaro · 2018
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Exact combinatorial optimization with graph convolutional neural networks
Maxime Gasse, Didier Chételat, Nicola Ferroni, Laurent Charlin, and Andrea Lodi · 2019
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Scoring positive semidefinite cutting planes for quadratic optimization via trained neural networks
Radu Baltean-Lugojan, Pierre Bonami, Ruth Misener, and Andrea Tramontani · 2019
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
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Fast graph representation learning with PyTorch Geometric
Matthias Fey and Jan E. Lenssen · 2019
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An image-based approach to detecting structural similarity among mixed integer programs
Zachary Steever, Chase Murray, Junsong Yuan, Mark Karwan, and Marco Lübbecke · 2020
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Presolve reductions in mixed integer programming
Tobias Achterberg, Robert E Bixby, Zonghao Gu, Edward Rothberg, and Dieter Weninger · 2020
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The scip optimization suite 8.0, 2021
Ksenia Bestuzheva, Mathieu Besançon, Wei-Kun Chen, Antonia Chmiela, Tim Donkiewicz, Jasper van Doornmalen, Leon Eifler, Oliver Gaul, Gerald Gamrath, Ambros Gleixner, Leona Gottwald, Christoph Graczyk, Katrin Halbig, Alexander Hoen, Christopher Hojny, Rolf van der Hulst, Thorsten Koch, Marco Lübbecke, Stephen J. Maher, Frederic Matter, Erik Mühmer, Benjamin Müller, Marc E. Pfetsch, Daniel Rehfeldt, Steffan Schlein, Franziska Schlösser, Felipe Serrano, Yuji Shinano, Boro Sofranac, Mark Turner, Stefan Vigerske, Fabian Wegscheider, Philipp Wellner, Dieter Weninger, and Jakob Witzig · 2021
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Miplib 2017: data-driven compilation of the 6th mixed-integer programming library
Ambros Gleixner, Gregor Hendel, Gerald Gamrath, Tobias Achterberg, Michael Bastubbe, Timo Berthold, Philipp Christophel, Kati Jarck, Thorsten Koch, Jeff Linderoth, et al · 2021
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Solving mixed integer programs using neural networks
Vinod Nair, Sergey Bartunov, Felix Gimeno, Ingrid von Glehn, Pawel Lichocki, Ivan Lobov, Brendan O’Donoghue, Nicolas Sonnerat, Christian Tjandraatmadja, Pengming Wang, et al · 2020
Cited alongside, same era.
Reinforcement learning for integer programming: Learning to cut
Yunhao Tang, Shipra Agrawal, and Yuri Faenza · 2020
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Mathematica, Version 12.2
Wolfram Research, Inc · 2020
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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
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Accelerating primal solution findings for mixed integer programs based on solution prediction
Jian-Ya Ding, Chao Zhang, Lei Shen, Shengyin Li, Bing Wang, Yinghui Xu, and Le Song · 2020
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Maria-Florina Balcan, Siddharth Prasad, Tuomas Sandholm, and Ellen Vitercik · 2021
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Learning to select cuts for efficient mixed-integer programming
Zeren Huang, Kerong Wang, Furui Liu, Hui-ling Zhen, Weinan Zhang, Mingxuan Yuan, Jianye Hao, Yong Yu, and Jun Wang · 2021
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Combinatorial optimization and reasoning with graph neural networks
Quentin Cappart, Didier Chételat, Elias Khalil, Andrea Lodi, Christopher Morris, and Petar Veličković · 2021
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Gurobi Optimizer Reference Manual, 2021
Gurobi Optimization, LLC · 2021
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A gentle introduction to graph neural networks
Benjamin Sanchez-Lengeling, Emily Reif, Adam Pearce, and Alexander B Wiltschko · 2021
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Smac3: A versatile bayesian optimization package for hyperparameter optimization
Marius Lindauer, Katharina Eggensperger, Matthias Feurer, André Biedenkapp, Difan Deng, Carolin Benjamins, Tim Ruhkopf, René Sass, and Frank Hutter · 2022
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