Robust Algorithms for TSP and Steiner Tree

Authors Arun Ganesh, Bruce M. Maggs, Debmalya Panigrahi

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Arun Ganesh
  • Department of Electrical Engineering and Computer Sciences, University of California at Berkeley, CA, USA
Bruce M. Maggs
  • Department of Computer Science, Duke University, Durham, NC, USA
  • Emerald Innovations, Cambridge, MA, USA
Debmalya Panigrahi
  • Department of Computer Science, Duke University, Durham, NC, USA

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Arun Ganesh, Bruce M. Maggs, and Debmalya Panigrahi. Robust Algorithms for TSP and Steiner Tree. In 47th International Colloquium on Automata, Languages, and Programming (ICALP 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 168, pp. 54:1-54:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)


Robust optimization is a widely studied area in operations research, where the algorithm takes as input a range of values and outputs a single solution that performs well for the entire range. Specifically, a robust algorithm aims to minimize regret, defined as the maximum difference between the solution’s cost and that of an optimal solution in hindsight once the input has been realized. For graph problems in P, such as shortest path and minimum spanning tree, robust polynomial-time algorithms that obtain a constant approximation on regret are known. In this paper, we study robust algorithms for minimizing regret in NP-hard graph optimization problems, and give constant approximations on regret for the classical traveling salesman and Steiner tree problems.

Subject Classification

ACM Subject Classification
  • Theory of computation → Routing and network design problems
  • Robust optimization
  • Steiner tree
  • traveling salesman problem


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