License: Creative Commons Attribution 3.0 Unported license (CC BY 3.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.FSTTCS.2017.27
URN: urn:nbn:de:0030-drops-83910
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2018/8391/
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Ene, Alina ; Nagarajan, Viswanath ; Saket, Rishi

Approximation Algorithms for Stochastic k-TSP

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Abstract

This paper studies the stochastic variant of the classical k-TSP problem where rewards at the vertices are independent random variables which are instantiated upon the tour's visit. The objective is to minimize the expected length of a tour that collects reward at least k. The solution is a policy describing the tour which may (adaptive) or may not (non-adaptive) depend on the observed rewards.
Our work presents an adaptive O(log k)-approximation algorithm for Stochastic k-TSP, along with a non-adaptive O(log^2 k)-approximation algorithm which also upper bounds the adaptivity gap by O(log^2 k). We also show that the adaptivity gap of Stochastic k-TSP is at least e, even in the special case of stochastic knapsack cover.

BibTeX - Entry

@InProceedings{ene_et_al:LIPIcs:2018:8391,
  author =	{Alina Ene and Viswanath Nagarajan and Rishi Saket},
  title =	{{Approximation Algorithms for Stochastic k-TSP}},
  booktitle =	{37th IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science (FSTTCS 2017)},
  pages =	{27:27--27:14},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-055-2},
  ISSN =	{1868-8969},
  year =	{2018},
  volume =	{93},
  editor =	{Satya Lokam and R. Ramanujam},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{http://drops.dagstuhl.de/opus/volltexte/2018/8391},
  URN =		{urn:nbn:de:0030-drops-83910},
  doi =		{10.4230/LIPIcs.FSTTCS.2017.27},
  annote =	{Keywords: Stochastic TSP, algorithms, approximation, adaptivity gap}
}

Keywords: Stochastic TSP, algorithms, approximation, adaptivity gap
Collection: 37th IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science (FSTTCS 2017)
Issue Date: 2018
Date of publication: 12.02.2018

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