arXiv:2608. 00270v2 Announce Type: replace Abstract: Neural Combinatorial Optimization (NCO) techniques have emerged as a highly efficient alternative to traditional exact algorithms for solving routing problems such as the Traveling Salesman Problem (TSP).
By David Aguado, Daniel Fuertes, Carlos R. del-Blanco, Fernando Jaureguizar
arXiv:2601. 13465v4 Announce Type: replace Abstract: Graph neural networks are usually treated as auxiliaries for combinatorial optimization: they imitate algorithms, guide search, or supply scores to classical procedures.
By Yimeng Min, Carla P. Gomes
arXiv:2606. 19185v1 Announce Type: new Abstract: The Traveling Salesman Problem (TSP) is a cornerstone of combinatorial optimization and arises in many practical scenarios.
By Bolin Shen, Ziwei Huang, Zhiguang Cao, Yushun Dong
arXiv:2606. 22776v2 Announce Type: replace-cross Abstract: Non-autoregressive neural solvers amortize computation across traveling salesman problem (TSP) instances, but models trained on random Euclidean instances can degrade when the number or spatial distribution of nodes changes.
By Xiang Li
arXiv:2607. 09708v1 Announce Type: new Abstract: Solving large-scale instances of the Traveling Salesman Problem (TSP) exactly is computationally expensive.
By Tianfeng Chen, Xianyue Li
arXiv:2412. 13858v2 Announce Type: replace Abstract: We investigate diffusion models to solve the Traveling Salesman Problem.
By Mickael Basson, Philippe Preux