Ruizhong Qiu, Zhiqing Sun, and Yiming Yang. Dimes: A differentiable meta solver for combinatorial optimization problems. arXiv preprint arXiv:2210.04123, 2022.
Zhiqing Sun and Yiming Yang. Difusco: Graph-based diffusion solvers for combinatorial optimization. arXiv preprint arXiv:2302.08224, 2023.
Chaitanya K Joshi, Thomas Laurent, and Xavier Bresson. An efficient graph convolutional network technique for the travelling salesman problem. arXiv preprint arXiv:1906.01227, 2019a.
Zhang-Hua Fu, Kai-Bin Qiu, and Hongyuan Zha. Generalize a small pre-trained model to arbitrarily large tsp instances. Proceedings of the AAAI Conference on Artificial Intelligence, 35(8):7474-7482, 2021.
Wouter Kool, Herke van Hoof, and Max Welling. Attention, learn to solve routing problems! In International Conference on Learning Representations, 2019. URL https://openreview.net/forum?id=ByxBFsRqYm.
Yimeng Min, Frederik Wenkel, Michael Perlmutter, and Guy Wolf. Can hybrid geometric scattering networks help solve the maximum clique problem? Advances in Neural Information Processing Systems, 35:22713-22724, 2022.
Georges A Croes. A method for solving traveling-salesman problems. Operations research, 6(6): 791-812, 1958.
Carla P Gomes, Bart Selman, Henry Kautz, et al. Boosting combinatorial search through randomization. AAAI/IAAI, 98:431-437, 1998.
John L Bresina. Heuristic-biased stochastic sampling. In AAAI/IAAI, Vol. 1, pages 271-278, 1996.
Carla P Gomes, Bart Selman, Nuno Crato, and Henry Kautz. Heavy-tailed phenomena in satisfiability and constraint satisfaction problems. Journal of automated reasoning, 24(1):67-100, 2000.
Michel Deudon, Pierre Cournot, Alexandre Lacoste, Yossiri Adulyasak, and Louis-Martin Rousseau. Learning heuristics for the tsp by policy gradient. In International Conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research, pages 170-181. Springer, 2018.
Keld Helsgaun. An extension of the lin-kernighan-helsgaun tsp solver for constrained traveling salesman and vehicle routing problems. Roskilde: Roskilde University, pages 24-50, 2017.
Chaitanya K Joshi, Quentin Cappart, Louis-Martin Rousseau, and Thomas Laurent. Learning the travelling salesperson problem requires rethinking generalization. Constraints, pages 1-29, 2022.
Thomas N Kipf and Max Welling. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907, 2016.
Zhuwen Li, Qifeng Chen, and Vladlen Koltun. Combinatorial optimization with graph convolutional networks and guided tree search. In Advances in Neural Information Processing Systems, pages 539-548, 2018.
Yoshua Bengio, Andrea Lodi, and Antoine Prouvost. Machine learning for combinatorial optimization: a methodological tour dhorizon. European Journal of Operational Research, 290(2): 405-421, 2021.
Chaitanya K Joshi, Thomas Laurent, and Xavier Bresson. On learning paradigms for the travelling salesman problem. arXiv preprint arXiv:1910.07210, 2019b.
Frederik Wenkel, Yimeng Min, Matthew Hirn, Michael Perlmutter, and Guy Wolf. Overcoming oversmoothness in graph convolutional networks via hybrid scattering networks. arXiv preprint arXiv:2201.08932, 2022.
Pablo Barceló, Egor V Kostylev, Mikael Monet, Jorge Pérez, Juan Reutter, and Juan-Pablo Silva. The logical expressiveness of graph neural networks. In 8th International Conference on Learning Representations (ICLR 2020), 2020.