mlsanityData to 7 Oct 2026

Travel the Same Path: A Novel TSP Solving Strategy

arXiv 2210.05906 ↗First posted 12 Oct 2022cs.LG cs.AI math.OCcited by 0

On the map: Offline RL and imitation learning from data → Imitation learning methods

Abstract (arXiv)

In this paper, we provide a novel strategy for solving Traveling Salesman Problem, which is a famous combinatorial optimization problem studied intensely in the TCS community. In particular, we consider the imitation learning framework, which helps a deterministic algorithm making good choices whenever it needs to, resulting in a speed up while maintaining the exactness of the solution without suffering from the unpredictability and a potential large deviation. Furthermore, we demonstrate a strong generalization ability of a graph neural network trained under the imitation learning framework. Specifically, the model is capable of solving a large instance of TSP faster than the baseline while has only seen small TSP instances when training.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Imitation learning methods, one of 13 directions in Offline RL and imitation learning from data. Builds on 5 of its 33 references that are in the map. First posted on arXiv 12 Oct 2022.

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5 of 33 references.

References · 5

  1. 2013Spectral Networks and Locally Connected Networks on Graphscited by 1,097
  2. 2019Exact Combinatorial Optimization with Graph Convolutional Neural Networkscited by 313
  3. 2019An Efficient Graph Convolutional Network Technique for the Travelling Salesman Problemcited by 265
  4. 2021The Transformer Network for the Traveling Salesman Problemcited by 59
  5. 2020Ecole: A Gym-like Library for Machine Learning in Combinatorial Optimization Solverscited by 43

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  1. 2024IDEQ -- Improving Diffusion Models for the Traveling Salesman Problem (TSP) by Leveraging the Structure of the Solution Space
  2. 2026Leveraging Structural Constraints for Diffusion-based Neural TSP Solvers
  3. 2025An Efficient Diffusion-based Non-Autoregressive Solver for Traveling Salesman Problem
  4. 2024Self-Improvement for Neural Combinatorial Optimization: Sample without Replacement, but Improvement
  5. 2026A First Guess is Rarely the Final Answer: Learning to Search in the Traveling Salesperson Problem
  6. 2025A Unified Deep Reinforcement Learning Approach for Close Enough Traveling Salesman Problem
  7. 2025ViTSP: A Vision Language Models Guided Framework for Solving Large-Scale Traveling Salesman Problems
  8. 2026Offline Decision Transformers for Neural Combinatorial Optimization: Surpassing Heuristics on the Traveling Salesman Problem