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Learning-Augmented Online TSP on Rings, Trees, Flowers and (Almost) Everywhere Else

Authors Evripidis Bampis , Bruno Escoffier , Themis Gouleakis , Niklas Hahn , Kostas Lakis , Golnoosh Shahkarami , Michalis Xefteris

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Evripidis Bampis
  • Sorbonne Université, CNRS, LIP6, F-75005 Paris, France
Bruno Escoffier
  • Sorbonne Université, CNRS, LIP6, F-75005 Paris, France
  • Institut Universitaire de France, Paris, France
Themis Gouleakis
  • National University of Singapore, Singapore
Niklas Hahn
  • Sorbonne Université, CNRS, LIP6, F-75005 Paris, France
Kostas Lakis
  • ETH Zürich, Switzerland
Golnoosh Shahkarami
  • Max-Planck-Institut für Informatik, Universität des Saarlandes, Saarbrücken, Germany
Michalis Xefteris
  • Sorbonne Université, CNRS, LIP6, F-75005 Paris, France

Acknowledgements

This work was partially funded by the grant ANR-19-CE48-0016 from the French National Research Agency (ANR). Kostas Lakis gratefully acknowledges financial support from the Foundation for Education and European Culture, Athens, Greece. Themis Gouleakis was supported by the National Research Foundation Fellowship for AI (Award NRF-NRFFAI-0002), an Amazon Research Award, and a Google South & Southeast Asia Research Award.

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Evripidis Bampis, Bruno Escoffier, Themis Gouleakis, Niklas Hahn, Kostas Lakis, Golnoosh Shahkarami, and Michalis Xefteris. Learning-Augmented Online TSP on Rings, Trees, Flowers and (Almost) Everywhere Else. In 31st Annual European Symposium on Algorithms (ESA 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 274, pp. 12:1-12:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
https://doi.org/10.4230/LIPIcs.ESA.2023.12

Abstract

We study the Online Traveling Salesperson Problem (OLTSP) with predictions. In OLTSP, a sequence of initially unknown requests arrive over time at points (locations) of a metric space. The goal is, starting from a particular point of the metric space (the origin), to serve all these requests while minimizing the total time spent. The server moves with unit speed or is "waiting" (zero speed) at some location. We consider two variants: in the open variant, the goal is achieved when the last request is served. In the closed one, the server additionally has to return to the origin. We adopt a prediction model, introduced for OLTSP on the line [Gouleakis et al., 2023], in which the predictions correspond to the locations of the requests and extend it to more general metric spaces. We first propose an oracle-based algorithmic framework, inspired by previous work [Bampis et al., 2023]. This framework allows us to design online algorithms for general metric spaces that provide competitive ratio guarantees which, given perfect predictions, beat the best possible classical guarantee (consistency). Moreover, they degrade gracefully along with the increase in error (smoothness), but always within a constant factor of the best known competitive ratio in the classical case (robustness). Having reduced the problem to designing suitable efficient oracles, we describe how to achieve this for general metric spaces as well as specific metric spaces (rings, trees and flowers), the resulting algorithms being tractable in the latter case. The consistency guarantees of our algorithms are tight in almost all cases, and their smoothness guarantees only suffer a linear dependency on the error, which we show is necessary. Finally, we provide robustness guarantees improving previous results.

Subject Classification

ACM Subject Classification
  • Theory of computation → Design and analysis of algorithms
  • Theory of computation → Online algorithms
  • Theory of computation → Parameterized complexity and exact algorithms
Keywords
  • TSP
  • Online algorithms
  • Learning-augmented algorithms
  • Algorithms with predictions
  • Competitive analysis

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