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Large Neighborhood Search (LNS) is a combinatorial optimization heuristic that starts with an assignment of values for the variables to be optimized, and iteratively improves it by searching a large neighborhood around the current assignment.
A reinforcement learning approach to job-shop scheduling
Zhang, W. and Dietterich, T. G · 1995
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Using prediction to improve combinatorial optimization search
Boyan, J. A. and Moore, A. W · 1997
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Using constraint programming and local search methods to solve vehicle routing problems
Shaw, P · 1998
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Learning instance-independent value functions to enhance local search
Moll, R., Barto, A. G., Perkins, T. J., and Sutton, R. S · 1999
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Local branching
Fischetti, M. and Lodi, A · 2003
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Local Branching: A Tutorial
Lodi, A · 2003
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Propagation guided large neighborhood search
Perron, L., Shaw, P., and Furnon, V · 2004
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Exploring relaxation induced neighborhoods to improve mip solutions
Danna, E., Rothberg, E., and Pape, C. L · 2005
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The feasibility pump
Fischetti, M., Glover, F., and Lodi, A · 2005
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A new model for learning in graph domains
Gori, M., Monfardini, G., and Scarselli, F · 2005
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Miplib 2003
Achterberg, T., Koch, T., and Martin, A · 2006
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Primal heuristics for mixed integer programs
Berthold, T · 2006
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Rens - relaxation enforced neighborhood search
Berthold, T · 2007
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Dins, a mip improvement heuristic
Ghosh, S · 2007
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An evolutionary algorithm for polishing mixed integer programming solutions
Rothberg, E · 2007
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The graph neural network model
Scarselli, F., Gori, M., Tsoi, A. C., Hagenbuchner, M., and Monfardini, G · 2008
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A gender-based genetic algorithm for the automatic configuration of algorithms
Ansótegui, C., Sellmann, M., and Tierney, K · 2009
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Paramils: An automatic algorithm configuration framework
Hutter, F., Hoos, H. H., Leyton-Brown, K., and Stützle, T · 2009
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Large neighborhood search
Pisinger, D. and Ropke, S · 2010
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Sequential model-based optimization for general algorithm configuration
Hutter, F., Hoos, H. H., and Leyton-Brown, K · 2011
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Large neighborhood search beyond mip
Berthold, T., Heinz, S., Pfetsch, M., and Vigerske, S · 2012
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Solving mixed integer programs using neural networks, 2020
Nair, V., Bartunov, S., Gimeno, F., von Glehn, I., Lichocki, P., Lobov, I., O’Donoghue, B., Sonnerat, N., Tjandraatmadja, C., Wang, P., Addanki, R., Hapuarachchi, T., Keck, T., Keeling, J., Kohli, P., Ktena, I., Li, Y., Vinyals, O., and Zwols, Y · 2012
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Online learning in mdps with side information
Abbasi-Yadkori, Y. and Neu, G · 2014
Cited alongside, same era.
Learning to search in branch and bound algorithms
He, H., Daume III, H., and Eisner, J. M · 2014
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Algorithm runtime prediction: Methods & evaluation
Hutter, F., Xu, L., Hoos, H. H., and Leyton-Brown, K · 2014
Cited alongside, same era.
Model-based genetic algorithms for algorithm configuration
Ansótegui, C., Malitsky, Y., Samulowitz, H., Sellmann, M., and Tierney, K · 2015
Cited alongside, same era.
Large neighborhood search with constraint programming for a vehicle routing problem with synchronization constraints
Hojabri, H., Gendreau, M., Potvin, J.-Y., and Rousseau, L.-M · 2018
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On mixed integer programming formulations for the unit commitment problem
Knueven, B., Ostrowski, J., and Watson, J.-P · 2018
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Combinatorial optimization with graph convolutional networks and guided tree search
Li, Z., Chen, Q., and Koltun, V · 2018
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Reinforcement learning for solving the vehicle routing problem
Nazari, M., Oroojlooy, A., Snyder, L., and Takac, M · 2018
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Learning to solve circuit-SAT: An unsupervised differentiable approach
Amizadeh, S., Matusevych, S., and Weimer, M · 2019
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Exact combinatorial optimization with graph convolutional neural networks
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Contextual markov decision processes
Hallak, A., Di Castro, D., and Mannor, S · 2015
Cited alongside, same era.
Pointer networks
Vinyals, O., Fortunato, M., and Jaitly, N · 2015
Cited alongside, same era.
A machine learning-based approximation of strong branching
Alvarez, A., Louveaux, Q., and Wehenkel, L · 2016
Cited alongside, same era.
Neural combinatorial optimization with reinforcement learning
Bello, I., Pham, H., Le, Q. V., Norouzi, M., and Bengio, S · 2016
Cited alongside, same era.
Deep Learning
Goodfellow, I., Bengio, Y., and Courville, A · 2016
Cited alongside, same era.
Learning to branch in mixed integer programming
Khalil, E., Le Bodic, P., Song, L., Nemhauser, G., and Dilkina, B · 2016
Cited alongside, same era.
Semi-supervised classification with graph convolutional networks
Kipf, T. N. and Welling, M · 2016
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Gasse, M., Chételat, D., Ferroni, N., Charlin, L., and Lodi, A · 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
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Neural large neighborhood search for the capacitated vehicle routing problem
Hottung, A. and Tierney, K · 2019
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Attention, learn to solve routing problems!
Kool, W., van Hoof, H., and Welling, M · 2019
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Learning a SAT solver from single-bit supervision
Selsam, D., Lamm, M., Bünz, B., Liang, P., de Moura, L., and Dill, D. L · 2019
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Evaluating robustness of neural networks with mixed integer programming
Tjeng, V., Xiao, K. Y., and Tedrake, R · 2019
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Learning local search heuristics for boolean satisfiability
Yolcu, E. and Poczos, B · 2019
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Neural large neighborhood search
Addanki, R., Nair, V., and Alizadeh, M · 2020
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Accelerating primal solution findings for mixed integer programs based on solution prediction
Ding, J., Zhang, C., Shen, L., Li, S., Wang, B., Xu, Y., and Song, L · 2020
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The SCIP optimization suite 7.0
Gamrath, G., Anderson, D., Bestuzheva, K., Chen, W.-K., Eifler, L., Gasse, M., Gemander, P., Gleixner, A., Gottwald, L., Halbig, K., et al · 2020
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Hybrid models for learning to branch
Gupta, P., Gasse, M., Khalil, E., Mudigonda, P., Lodi, A., and Bengio, Y · 2020
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A general large neighborhood search framework for solving integer programs
Song, J., Lanka, R., Yue, Y., and Dilkina, B · 2020
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Learning to solve large-scale security-constrained unit commitment problems
Xavier, A. S., Qiu, F., and Ahmed, S · 2020
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Parameterizing branch-and-bound search trees to learn branching policies
Zarpellon, G., Jo, J., Lodi, A., and Bengio, Y · 2020
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Course on solving algorithms for discrete optimization, lecture 3.4.7 large neighbourhood search, 2021
Lee, J. and Stuckey, P · 2021
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