License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.CP.2021.33
URN: urn:nbn:de:0030-drops-153248
URL: https://drops.dagstuhl.de/opus/volltexte/2021/15324/
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Joshi, Chaitanya K. ; Cappart, Quentin ; Rousseau, Louis-Martin ; Laurent, Thomas

Learning TSP Requires Rethinking Generalization

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LIPIcs-CP-2021-33.pdf (4 MB)


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.

BibTeX - Entry

@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/opus/volltexte/2021/15324},
  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}
}

Keywords: Combinatorial Optimization, Travelling Salesman Problem, Graph Neural Networks, Deep Learning
Collection: 27th International Conference on Principles and Practice of Constraint Programming (CP 2021)
Issue Date: 2021
Date of publication: 15.10.2021
Supplementary Material: Software (Source Code and Dataset): https://github.com/chaitjo/learning-tsp archived at: https://archive.softwareheritage.org/swh:1:dir:49220f1be1ae634e106f41948968734ac1569dbd


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