Algorithms and adaptivity gaps for stochastic k-TSP

Haotian Jiang, Jian Li, Daogao Liu, Sahil Singla

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations


Given a metric (V, d) and a root ∈ V , the classic k-TSP problem is to find a tour originating at the root of minimum length that visits at least k nodes in V . In this work, motivated by applications where the input to an optimization problem is uncertain, we study two stochastic versions of k-TSP. In Stoch-Reward k-TSP, originally defined by Ene-Nagarajan-Saket [13], each vertex v in the given metric (V, d) contains a stochastic reward Rv. The goal is to adaptively find a tour of minimum expected length that collects at least reward k; here “adaptively” means our next decision may depend on previous outcomes. Ene et al. give an O(log k)-approximation adaptive algorithm for this problem, and left open if there is an O(1)-approximation algorithm. We totally resolve their open question, and even give an O(1)-approximation non-adaptive algorithm for Stoch-Reward k-TSP. We also introduce and obtain similar results for the Stoch-Cost k-TSP problem. In this problem each vertex v has a stochastic cost Cv, and the goal is to visit and select at least k vertices to minimize the expected sum of tour length and cost of selected vertices. Besides being a natural stochastic generalization of k-TSP, this problem is also interesting because it generalizes the Price of Information framework [33] from deterministic probing costs to metric probing costs. Our techniques are based on two crucial ideas: “repetitions” and “critical scaling”. In general, replacing a random variable with its expectation leads to very poor results. We show that for our problems, if we truncate the random variables at an ideal threshold, then their expected values form a good surrogate. Here, we rely on running several repetitions of our algorithm with the same threshold, and then argue concentration using Freedman’s and Jogdeo-Samuels’ inequalities. Unfortunately, this ideal threshold depends on how far we are from achieving our target k, which a non-adaptive algorithm does not know. To overcome this barrier, we truncate the random variables at various different scales and identify a “critical” scale.

Original languageEnglish (US)
Title of host publication11th Innovations in Theoretical Computer Science Conference, ITCS 2020
EditorsThomas Vidick
PublisherSchloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing
ISBN (Electronic)9783959771344
StatePublished - Jan 2020
Event11th Innovations in Theoretical Computer Science Conference, ITCS 2020 - Seattle, United States
Duration: Jan 12 2020Jan 14 2020

Publication series

NameLeibniz International Proceedings in Informatics, LIPIcs
ISSN (Print)1868-8969


Conference11th Innovations in Theoretical Computer Science Conference, ITCS 2020
Country/TerritoryUnited States

All Science Journal Classification (ASJC) codes

  • Software


  • Approximation algorithms
  • Stochastic optimization
  • Travelling salesman problem


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