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On the Approximability of the Traveling Salesman Problem with Line Neighborhoods

Authors Antonios Antoniadis, Sándor Kisfaludi-Bak, Bundit Laekhanukit, Daniel Vaz

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ACM Subject Classification
  • Theory of computation → Computational geometry
  • Theory of computation → Approximation algorithms analysis
Keywords
  • Traveling Salesman with neighborhoods
  • Group Steiner Tree
  • Geometric approximation algorithms

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Abstract

We study the variant of the Euclidean Traveling Salesman problem where instead of a set of points, we are given a set of lines as input, and the goal is to find the shortest tour that visits each line. The best known upper and lower bounds for the problem in ℝ^d, with d ≥ 3, are NP-hardness and an O(log³ n)-approximation algorithm which is based on a reduction to the group Steiner tree problem. 
We show that TSP with lines in ℝ^d is APX-hard for any d ≥ 3. More generally, this implies that TSP with k-dimensional flats does not admit a PTAS for any 1 ≤ k ≤ d-2 unless P = NP, which gives a complete classification regarding the existence of polynomial time approximation schemes for these problems, as there are known PTASes for k = 0 (i.e., points) and k = d-1 (hyperplanes). We are able to give a stronger inapproximability factor for d = O(log n) by showing that TSP with lines does not admit a (2-ε)-approximation in d dimensions under the Unique Games Conjecture. On the positive side, we leverage recent results on restricted variants of the group Steiner tree problem in order to give an O(log² n)-approximation algorithm for the problem, albeit with a running time of n^{O(log log n)}.

Cite As Get BibTex

Antonios Antoniadis, Sándor Kisfaludi-Bak, Bundit Laekhanukit, and Daniel Vaz. On the Approximability of the Traveling Salesman Problem with Line Neighborhoods. In 18th Scandinavian Symposium and Workshops on Algorithm Theory (SWAT 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 227, pp. 10:1-10:21, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022) https://doi.org/10.4230/LIPIcs.SWAT.2022.10

Author Details

Antonios Antoniadis
  • University of Twente, Enschede, The Netherlands
Sándor Kisfaludi-Bak
  • Aalto University, Finland
Bundit Laekhanukit
  • Shanghai University of Finance and Economics, China
Daniel Vaz
  • Operations Research Group, Technische Universität München, Germany

Funding

  • Antoniadis, Antonios: Work done in part while the author was at Saarland University and Max Planck Institute for Informatics and supported by DFG grant AN 1262/1-1.
  • Vaz, Daniel: This work has been supported by the Alexander von Humboldt Foundation with funds from the German Federal Ministry of Education and Research (BMBF). Work done in part while the author was at Saarland University and Max Planck Institute for Informatics.

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