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Relevant combinatorial optimization problems (COPs) are often NP-hard.
On the Optimization of a Synaptic Learning Rule
Bengio, S., Bengio, Y., Cloutier, J., and Gecsei, J · 1992
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Simple statistical gradient-following algorithms for connectionist reinforcement learning
Williams, R. J · 1992
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Evolution and design of distributed learning rules
Runarsson, T. and Jonsson, M · 2000
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Ant colony optimization
Dorigo, M., Birattari, M., and Stutzle, T · 2006
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Adam: A Method for Stochastic Optimization
Kingma, D. P. and Ba, J · 2015
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Learning to learn by gradient descent by gradient descent
Andrychowicz, M., Denil, M., Gómez, S., Hoffman, M. W., Pfau, D., Schaul, T., Shillingford, B., and de Freitas, N · 2016
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Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Finn, C., Abbeel, P., and Levine, S · 2017
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An extension of the Lin-Kernighan-Helsgaun TSP solver for constrained traveling salesman and vehicle routing problems
Helsgaun, K · 2017
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Semi-supervised classification with graph convolutional networks
Kipf, T. N. and Welling, M · 2017
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Finding near-optimal independent sets at scale
Lamm, S., Sanders, P., Schulz, C., Strash, D., and Werneck, R. F · 2017
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Evolution Strategies as a Scalable Alternative to Reinforcement Learning, September 2017
Salimans, T., Ho, J., Chen, X., Sidor, S., and Sutskever, I · 2017
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Relational inductive biases, deep learning, and graph networks, October 2018
Battaglia, P. W., Hamrick, J. B., Bapst, V., Sanchez-Gonzalez, A., Zambaldi, V., Malinowski, M., Tacchetti, A., Raposo, D., Santoro, A., Faulkner, R., Gulcehre, C., Song, F., Ballard, A., Gilmer, J., Dahl, G., Vaswani, A., Allen, K., Nash, C., Langston, V., Dyer, C., Heess, N., Wierstra, D., Kohli, P., Botvinick, M., Vinyals, O., Li, Y., and Pascanu, R · 2018
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JAX: Composable transformations of Python+NumPy programs, 2018
Bradbury, J., Frostig, R., Hawkins, P., Johnson, M. J., Leary, C., Maclaurin, D., Necula, G., Paszke, A., VanderPlas, J., Wanderman-Milne, S., and Zhang, Q · 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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An efficient graph convolutional network technique for the travelling salesman problem
Joshi, C. K., Laurent, T., and Bresson, X · 2019
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Attention, learn to solve routing problems!
Kool, W., van Hoof, H., and Welling, M · 2019
Cited alongside, same era.
Understanding and correcting pathologies in the training of learned optimizers, June 2019
Metz, L., Maheswaranathan, N., Nixon, J., Freeman, C. D., and Sohl-Dickstein, J · 2019
Cited alongside, same era.
Learning What to Defer for Maximum Independent Sets
Ahn, S., Seo, Y., and Shin, J · 2020
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Neural large neighborhood search for the capacitated vehicle routing problem
Hottung, A. and Tierney, K · 2020
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POMO: Policy optimization with multiple optima for reinforcement learning
Kwon, Y.-D., Choo, J., Kim, B., Yoon, I., Gwon, Y., and Min, S · 2020
Cited alongside, same era.
Tasks, stability, architecture, and compute: Training more effective learned optimizers, and using them to train themselves, September 2020
Sym-NCO: Leveraging symmetricity for neural combinatorial optimization
Kim, M., Park, J., and Park, J · 2022
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Deep Policy Dynamic Programming for Vehicle Routing Problems
Kool, W., van Hoof, H., Gromicho, J. A. S., and Welling, M · 2022
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VeLO: Training Versatile Learned Optimizers by Scaling Up, November 2022
Metz, L., Harrison, J., Freeman, C. D., Merchant, A., Beyer, L., Bradbury, J., Agrawal, N., Poole, B., Mordatch, I., Roberts, A., and Sohl-Dickstein, J · 2022
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DIMES: A Differentiable Meta Solver for Combinatorial Optimization Problems
Qiu, R., Sun, Z., and Yang, Y · 2022
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BQ-NCO: Bisimulation Quotienting for Efficient Neural Combinatorial Optimization
Drakulic, D., Michel, S., Mai, F., Sors, A., and Andreoli, J.-M · 2023
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Metz, L., Maheswaranathan, N., Freeman, C. D., Poole, B., and Sohl-Dickstein, J · 2020
Cited alongside, same era.
Generalize a small pre-trained model to arbitrarily large TSP instances
Fu, Z.-H., Qiu, K.-B., and Zha, H · 2021
Cited alongside, same era.
Learning TSP requires rethinking generalization
Joshi, C. K., Cappart, Q., Rousseau, L.-M., and Laurent, T · 2021
Cited alongside, same era.
Learning to delegate for large-scale vehicle routing
Li, S., Yan, Z., and Wu, C · 2021
Cited alongside, same era.
Unbiased Gradient Estimation in Unrolled Computation Graphs with Persistent Evolution Strategies, December 2021
Vicol, P., Metz, L., and Sohl-Dickstein, J · 2021
Cited alongside, same era.
NeuroLKH: Combining Deep Learning Model with Lin-Kernighan-Helsgaun Heuristic for Solving the Traveling Salesman Problem
Xin, L., Song, W., Cao, Z., and Zhang, J · 2021
Cited alongside, same era.
What’s Wrong with Deep Learning in Tree Search for Combinatorial Optimization
Böther, M., Kißig, O., Taraz, M., Cohen, S., Seidel, K., and Friedrich, T · 2022
Cited alongside, same era.
Grinsztajn, N., Furelos-Blanco, D., Surana, S., Bonnet, C., and Barrett, T. D · 2023
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Pointerformer: Deep reinforced multi-pointer transformer for the traveling salesman problem
Jin, Y., Ding, Y., Pan, X., He, K., Zhao, L., Qin, T., Song, L., and Bian, J · 2023
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From Distribution Learning in Training to Gradient Search in Testing for Combinatorial Optimization
Li, Y., Guo, J., Wang, R., and Yan, J · 2023
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Neural Combinatorial Optimization with Heavy Decoder: Toward Large Scale Generalization
Luo, F., Lin, X., Liu, F., Zhang, Q., and Wang, Z · 2023
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Unsupervised Learning for Solving the Travelling Salesman Problem
Min, Y., Bai, Y., and Gomes, C. P · 2023
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Variational Annealing on Graphs for Combinatorial Optimization
Sanokowski, S., Berghammer, W. F., Hochreiter, S., and Lehner, S · 2023
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DIFUSCO: Graph-based Diffusion Solvers for Combinatorial Optimization
Sun, Z. and Yang, Y · 2023
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Low-Variance Gradient Estimation in Unrolled Computation Graphs with ES-Single
Vicol, P · 2023
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DeepACO: Neural-enhanced Ant Systems for Combinatorial Optimization
Ye, H., Wang, J., Cao, Z., Liang, H., and Li, Y · 2023
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