DROPS

Document

DOI: 10.4230/LIPIcs.CP.2021.33

Learning TSP Requires Rethinking Generalization

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

Published in: LIPIcs, Volume 210, 27th International Conference on Principles and Practice of Constraint Programming (CP 2021)

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.

Cite as

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)

Copy BibTex To Clipboard

@InProceedings{joshi_et_al:LIPIcs.CP.2021.33,
  author =	{Joshi, Chaitanya K. and Cappart, Quentin and Rousseau, Louis-Martin and Laurent, Thomas},
  title =	{{Learning TSP Requires Rethinking Generalization}},
  booktitle =	{27th International Conference on Principles and Practice of Constraint Programming (CP 2021)},
  pages =	{33:1--33:21},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-211-2},
  ISSN =	{1868-8969},
  year =	{2021},
  volume =	{210},
  editor =	{Michel, Laurent D.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2021.33},
  URN =		{urn:nbn:de:0030-drops-153248},
  doi =		{10.4230/LIPIcs.CP.2021.33},
  annote =	{Keywords: Combinatorial Optimization, Travelling Salesman Problem, Graph Neural Networks, Deep Learning}
}

Search Results

Documents authored by Laurent, Thomas

Learning TSP Requires Rethinking Generalization

Abstract

Cite as

Thanks for your feedback!

Could not send message