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Branch and Bound (B&B) is the exact tree search method typically used to solve Mixed-Integer Linear Programming problems (MILPs).
Lifelong Learning with a Changing Action Set
Chandak, Y.; Theocharous, G.; Nota, C.; and Thomas, P. S. 2019b · 1906
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Learning in Gated Neural Networks
Makkuva, A. V.; Oh, S.; Kannan, S.; and Viswanath, P. 2019 · 1906
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An Automatic Method of Solving Discrete Programming Problems
Land, A. H.; and Doig, A. G. 1960 · 1960
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Experiments in mixed-integer programming
Benichou, M.; Gauthier, J.; Girodet, P.; and Hentges, G. 1971 · 1971
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Efficient Training of Artificial Neural Networks for Autonomous Navigation
Pomerleau, D. A. 1991 · 1991
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Finding Cuts in the TSP (A Preliminary Report)
Applegate, D.; Bixby, R.; Chvatal, V.; and Cook, B. 1995 · 1995
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Long short-term memory
Hochreiter, S.; and Schmidhuber, J. 1997 · 1997
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Integer programming
Wolsey, L. A. 1998 · 1998
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A Computational Study of Search Strategies for Mixed Integer Programming
Linderoth, J. T.; and Savelsbergh, M. W. P. 1999 · 1999
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Branching rules revisited
Achterberg, T.; Koch, T.; and Martin, A. 2005 · 2004
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Constraint Integer Programming
Achterberg, T. 2007 · 2007
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Mixed integer programming computation
Lodi, A. 2009 · 2008
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Visualizing High-Dimensional Data Using t-SNE
van der Maaten, L.; and Hinton, G. 2008 · 2008
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Hybrid Branching , 309–311
Achterberg, T.; and Berthold, T. 2009 · 2009
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MIPLIB 2010
Koch, T.; Achterberg, T.; Andersen, E.; Bastert, O.; Berthold, T.; Bixby, R. E.; Danna, E.; Gamrath, G.; Gleixner, A. M.; Heinz, S.; Lodi, A.; Mittelmann, H.; Ralphs, T.; Salvagnin, D.; Steffy, D. E.; and Wolter, K. 2011 · 2010
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Guiding Combinatorial Optimization with UCT
Sabharwal, A.; Samulowitz, H.; and Reddy, C. 2012 · 2012
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Mixed Integer Programming: Analyzing 12 Years of Progress , 449–481
Achterberg, T.; and Wunderling, R. 2013 · 2013
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Performance Variability in Mixed-Integer Programming , chapter 1, 1–12
Lodi, A.; and Tramontani, A. 2013a · 2013
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Performance Variability in Mixed-Integer Programming , chapter Chapter 1, 1–12
Lodi, A.; and Tramontani, A. 2013b · 2013
Cited alongside, same era.
Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
Chung, J.; Gulcehre, C.; Cho, K.; and Bengio, Y. 2014 · 2014
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Learning to Search in Branch and Bound Algorithms
He, H.; Daume III, H.; and Eisner, J. M. 2014 · 2014
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DASH: Dynamic Approach for Switching Heuristics
Di Liberto, G.; Kadioglu, S.; Leo, K.; and Malitsky, Y. 2016 · 2015
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Reinforcement Learning in Large Discrete Action Spaces
Dulac-Arnold, G.; Evans, R.; Sunehag, P.; and Coppin, B. 2015 · 2015
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An abstract model for branching and its application to mixed integer programming
Le Bodic, P.; and Nemhauser, G. 2017 · 2017
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On learning and branching: a survey
Lodi, A.; and Zarpellon, G. 2017 · 2017
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Learning to Branch
Balcan, M.-F.; Dick, T.; Sandholm, T.; and Vitercik, E. 2018 · 2018
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Machine Learning for Combinatorial Optimization: a Methodological Tour d’Horizon
Bengio, Y.; Lodi, A.; and Prouvost, A. 2018 · 2018
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The SCIP Optimization Suite 6.0
Gleixner, A.; Bastubbe, M.; Eifler, L.; Gally, T.; Gamrath, G.; Gottwald, R. L.; Hendel, G.; Hojny, C.; Koch, T.; Lübbecke, M.; Maher, S. J.; Miltenberger, M.; Müller, B.; Pfetsch, M.; Puchert, C.; Rehfeldt, D.; Schlösser, F.; Schubert, C.; Serrano, F.; Shinano, Y.; Viernickel, J. M.; Walter, M.; Wegscheider, F.; Witt, J. T.; and Witzig, J. 2018 · 2018
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Goodfellow, I.; Shlens, J.; and Szegedy, C. 2015 · 2015
Cited alongside, same era.
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S.; and Szegedy, C. 2015 · 2015
Cited alongside, same era.
Adam: A method for stochastic optimization
Kingma, D. P.; and Ba, J. 2015 · 2015
Cited alongside, same era.
A Machine Learning-Based Approximation of Strong Branching
Alvarez, M. A.; Louveaux, Q.; and Wehenkel, L. 2017 · 2016
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Learning to Branch in Mixed Integer Programming
Khalil, E. B.; Bodic, P. L.; Song, L.; Nemhauser, G.; and Dilkina, B. 2016 · 2016
Cited alongside, same era.
Feature Pyramid Networks for Object Detection
Lin, T.; Dollár, P.; Girshick, R. B.; He, K.; Hariharan, B.; and Belongie, S. J. 2016 · 2016
Cited alongside, same era.
PySCIPOpt: Mathematical Programming in Python with the SCIP Optimization Suite
Maher, S.; Miltenberger, M.; Pedroso, J. P.; Rehfeldt, D.; Schwarz, R.; and Serrano, F. 2016 · 2016
Cited alongside, same era.
Hansknecht, C.; Joormann, I.; and Stiller, S. 2018 · 2018
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ExGate: Externally Controlled Gating for Feature-based Attention in Artificial Neural Networks
Son, J.; and Mishra, A. K. 2018 · 2018
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Learning to Search via Retrospective Imitation
Song, J.; Lanka, R.; Zhao, A.; Bhatnagar, A.; Yue, Y.; and Ono, M. 2018 · 2018
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An updated mixed-integer programming library: MIPLIB 3
Bixby, R. E.; Ceria, S.; McZeal, C. M.; and Savelsbergh, M. W. 1998 · 2019
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Learning MILP Resolution Outcomes Before Reaching Time-Limit
Fischetti, M.; Lodi, A.; and Zarpellon, G. 2019 · 2019
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Exact Combinatorial Optimization with Graph Convolutional Neural Networks
Gasse, M.; Chetelat, D.; Ferroni, N.; Charlin, L.; and Lodi, A. 2019 · 2019
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MIPLIB 2017: Data-Driven Compilation of the 6th Mixed-Integer Programming Library
Gleixner, A.; Hendel, G.; Gamrath, G.; Achterberg, T.; Bastubbe, M.; Berthold, T.; Christophel, P. M.; Jarck, K.; Koch, T.; Linderoth, J.; Lübbecke, M.; Mittelmann, H. D.; Ozyurt, D.; Ralphs, T. K.; Salvagnin, D.; and Shinano, Y. 2019 · 2019
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Branch And Bound—Why Does It Work?
Lipton, R. J.; and Regan, R. W. 2012 · 2019
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PyTorch: An Imperative Style, High-Performance Deep Learning Library
Paszke, A.; Gross, S.; Massa, F.; Lerer, A.; Bradbury, J.; Chanan, G.; Killeen, T.; Lin, Z.; Gimelshein, N.; Antiga, L.; Desmaison, A.; Kopf, A.; Yang, E.; DeVito, Z.; Raison, M.; Tejani, A.; Chilamkurthy, S.; Steiner, B.; Fang, L.; Bai, J.; and Chintala, S. 2019 · 2019
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Code for the relpscost
relpscost. 2019 · 2019
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Mogrifier LSTM
Melis, G.; Kočiský, T.; and Blunsom, P. 2020 · 2020
Closest in time.
Wu, Y.; and He, K. 2018 · 2020
Closest in time.