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Learning TSP Requires Rethinking Generalization

Authors Chaitanya K. Joshi , Quentin Cappart, Louis-Martin Rousseau, Thomas Laurent

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Chaitanya K. Joshi
  • Institute for Infocomm Research, A*STAR, Singapore
Quentin Cappart
  • Ecole Polytechnique de Montréal, Canada
Louis-Martin Rousseau
  • Ecole Polytechnique de Montréal, Canada
Thomas Laurent
  • Loyola Marymount University, LA, USA

Acknowledgements

We would like to thank X. Bresson, V. Dwivedi, A. Ferber, E. Khalil, W. Kool, R. Levie, A. Prouvost, P. Veličković and the anonymous reviewers for helpful discussions.

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Chaitanya K. Joshi, Quentin Cappart, Louis-Martin Rousseau, and Thomas Laurent. Learning TSP Requires Rethinking Generalization. In 27th International Conference on Principles and Practice of Constraint Programming (CP 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 210, pp. 33:1-33:21, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021) https://doi.org/10.4230/LIPIcs.CP.2021.33

Abstract

End-to-end training of neural network solvers for combinatorial optimization problems such as the Travelling Salesman Problem is intractable and inefficient beyond a few hundreds of nodes. While state-of-the-art Machine Learning approaches perform closely to classical solvers when trained on trivially small sizes, they are unable to generalize the learnt policy to larger instances of practical scales. Towards leveraging transfer learning to solve large-scale TSPs, this paper identifies inductive biases, model architectures and learning algorithms that promote generalization to instances larger than those seen in training. Our controlled experiments provide the first principled investigation into such zero-shot generalization, revealing that extrapolating beyond training data requires rethinking the neural combinatorial optimization pipeline, from network layers and learning paradigms to evaluation protocols.

Subject Classification

ACM Subject Classification
  • Computing methodologies → Neural networks
Keywords
  • Combinatorial Optimization
  • Travelling Salesman Problem
  • Graph Neural Networks
  • Deep Learning

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