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:2412. 13858v2 Announce Type: replace Abstract: We investigate diffusion models to solve the Traveling Salesman Problem.
By Mickael Basson, Philippe Preux
arXiv:2606. 09343v1 Announce Type: new Abstract: Neural combinatorial optimization has recently achieved strong results on the Euclidean Traveling Salesman Problem (TSP) using generative models such as diffusion and consistency models.
By Micka\"el Basson (CRIStAL, Scool), Philippe Preux (CRIStAL, Scool)
arXiv:2607. 19072v1 Announce Type: new Abstract: This paper introduces a self-supervised pretraining framework for graph combinatorial optimization specifically designed to address the nature of routing problems like the Traveling Salesman Problem.
By David Aguado, Daniel Fuertes, Carlos R. del-Blanco, Fernando Jaureguizar
arXiv:2607. 23854v1 Announce Type: new Abstract: Humans often find good solutions to combinatorial optimization problems that are computationally hard even for advanced computer algorithms.
By Haijiang Yan, Jian-Qiao Zhu, Liqiang Huang, Ming Meng