Pay today for a rainy day: improved approximation algorithms for demand-robust min-cut and shortest path problems

  • Authors:

  • Daniel Golovin;Vineet Goyal;R. Ravi

  • Affiliations:

  • Computer Science Department, Carnegie Mellon University, Pittsburgh, PA;Tepper School of Business, Carnegie Mellon University, Pittsburgh, PA;Tepper School of Business, Carnegie Mellon University, Pittsburgh, PA

  • Venue:
  • STACS'06 Proceedings of the 23rd Annual conference on Theoretical Aspects of Computer Science
  • Year:
  • 2006

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Abstract

Demand-robust versions of common optimization problems were recently introduced by Dhamdhere et al. [4] motivated by the worst-case considerations of two-stage stochastic optimization models. We study the demand robust min-cut and shortest path problems, and exploit the nature of the robust objective to give improved approximation factors. Specifically, we give a $(1 + \sqrt{2})$ approximation for robust min-cut and a 7.1 approximation for robust shortest path. Previously, the best approximation factors were O(log n) for robust min-cut and 16 for robust shortest paths, both due to Dhamdhere et al.[4]. Our main technique can be summarized as follows: We investigate each of the second stage scenarios individually, checking if it can be independently serviced in the second stage within an acceptable cost (namely, a guess of the optimal second stage costs). For the costly scenarios that cannot be serviced in this way (“rainy days”), we show that they can be fully taken care of in a near-optimal first stage solution (i.e., by ”paying today”). We also consider “hitting-set” extensions of the robust min-cut and shortest path problems and show that our techniques can be combined with algorithms for Steiner multicut and group Steiner tree problems to give similar approximation guarantees for the hitting-set versions of robust min-cut and shortest path problems respectively.