On Size and Hardness Generalization in Unsupervised Learning for the Travelling Salesman Problem
This work addresses generalization challenges in unsupervised learning for combinatorial optimization, specifically TSP, but is incremental as it builds on existing GNN and local search methods.
The study tackled the generalization capability of unsupervised learning for solving the Travelling Salesman Problem (TSP), finding that training with larger instance sizes and harder distributions improves model effectiveness and generalization.
We study the generalization capability of Unsupervised Learning in solving the Travelling Salesman Problem (TSP). We use a Graph Neural Network (GNN) trained with a surrogate loss function to generate an embedding for each node. We use these embeddings to construct a heat map that indicates the likelihood of each edge being part of the optimal route. We then apply local search to generate our final predictions. Our investigation explores how different training instance sizes, embedding dimensions, and distributions influence the outcomes of Unsupervised Learning methods. Our results show that training with larger instance sizes and increasing embedding dimensions can build a more effective representation, enhancing the model's ability to solve TSP. Furthermore, in evaluating generalization across different distributions, we first determine the hardness of various distributions and explore how different hardnesses affect the final results. Our findings suggest that models trained on harder instances exhibit better generalization capabilities, highlighting the importance of selecting appropriate training instances in solving TSP using Unsupervised Learning.