Fetching the paper…
Reading the bibliography…
We propose UTSP, an unsupervised learning (UL) framework for solving the Travelling Salesman Problem (TSP).
An efficient graph convolutional network technique for the travelling salesman problem
Chaitanya K Joshi, Thomas Laurent, and Xavier Bresson · 1906
Earlier work this paper cites.
The Collected Mathematical Papers of James Joseph Sylvester… , volume 3
James Joseph Sylvester · 1909
Earlier work this paper cites.
On learning paradigms for the travelling salesman problem
Chaitanya K Joshi, Thomas Laurent, and Xavier Bresson · 1910
Earlier work this paper cites.
A method for solving traveling-salesman problems
Georges A Croes · 1958
Earlier work this paper cites.
Heuristic-biased stochastic sampling
John L Bresina · 1996
Earlier work this paper cites.
Boosting combinatorial search through randomization
Carla P Gomes, Bart Selman, Henry Kautz, et al · 1998
Earlier work this paper cites.
An effective implementation of the lin–kernighan traveling salesman heuristic
Keld Helsgaun · 2000
Earlier work this paper cites.
Heavy-tailed phenomena in satisfiability and constraint satisfaction problems
Carla P Gomes, Bart Selman, Nuno Crato, and Henry Kautz · 2000
Earlier work this paper cites.
Concorde tsp solver, 2006
David Applegate, Ribert Bixby, Vasek Chvatal, and William Cook · 2006
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
Cited alongside, same era.
Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2016
Cited alongside, same era.
An extension of the lin-kernighan-helsgaun tsp solver for constrained traveling salesman and vehicle routing problems
Keld Helsgaun · 2017
Cited alongside, same era.
Learning heuristics for the tsp by policy gradient
Michel Deudon, Pierre Cournut, Alexandre Lacoste, Yossiri Adulyasak, and Louis-Martin Rousseau · 2018
Cited alongside, same era.
Combinatorial optimization with graph convolutional networks and guided tree search
Zhuwen Li, Qifeng Chen, and Vladlen Koltun · 2018
Cited alongside, same era.
The logical expressiveness of graph neural networks
Pablo Barceló, Egor V Kostylev, Mikael Monet, Jorge Pérez, Juan Reutter, and Juan-Pablo Silva · 2020
Later among the works it cites.
Generalize a small pre-trained model to arbitrarily large tsp instances
Zhang-Hua Fu, Kai-Bin Qiu, and Hongyuan Zha · 2021
Later among the works it cites.
Machine learning for combinatorial optimization: a methodological tour d’horizon
Yoshua Bengio, Andrea Lodi, and Antoine Prouvost · 2021
Later among the works it cites.
Dimes: A differentiable meta solver for combinatorial optimization problems
Ruizhong Qiu, Zhiqing Sun, and Yiming Yang · 2022
Later among the works it cites.
Can hybrid geometric scattering networks help solve the maximum clique problem?
Yimeng Min, Frederik Wenkel, Michael Perlmutter, and Guy Wolf · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Attention, learn to solve routing problems!
Wouter Kool, Herke van Hoof, and Max Welling · 2019
Cited alongside, same era.
Revisiting graph neural networks: All we have is low-pass filters
Hoang Nt and Takanori Maehara · 2019
Cited alongside, same era.
Pomo: Policy optimization with multiple optima for reinforcement learning
Yeong-Dae Kwon, Jinho Choo, Byoungjip Kim, Iljoo Yoon, Youngjune Gwon, and Seungjai Min · 2020
Cited alongside, same era.
Learning the travelling salesperson problem requires rethinking generalization
Chaitanya K Joshi, Quentin Cappart, Louis-Martin Rousseau, and Thomas Laurent · 2022
Later among the works it cites.
Overcoming oversmoothness in graph convolutional networks via hybrid scattering networks
Frederik Wenkel, Yimeng Min, Matthew Hirn, Michael Perlmutter, and Guy Wolf · 2022
Later among the works it cites.
Difusco: Graph-based diffusion solvers for combinatorial optimization
Zhiqing Sun and Yiming Yang · 2023
Closest in time.