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Pittsburgh, Pennsylvania, USA
Oct. 23, 2005 to Oct. 25, 2005
ISBN: 0-7695-2468-0
pp: 367-378
Kedar Dhamdhere , Google Inc.
Vineet Goyal , Carnegie Mellon University
R. Ravi , Carnegie Mellon University
Mohit Singh , Carnegie Mellon University
ABSTRACT
<p>Robust optimization has traditionally focused on uncertainty in data and costs in optimization problems to formulate models whose solutions will be optimal in the worstcase among the various uncertain scenarios in the model. While these approaches may be thought of defining data- or cost-robust problems, we formulate a new "demand-robust" model motivated by recent work on two-stage stochastic optimization problems. We propose this in the framework of general covering problems and prove a general structural lemma about special types of first-stage solutions for such problems: there exists a first-stage solution that is a minimal feasible solution for the union of the demands for some subset of the scenarios and its objective function value is no more than twice the optimal. We then provide approximation algorithms for a variety of standard discrete covering problems in this setting, including minimum cut, minimum multi-cut, shortest paths, Steiner trees, vertex cover and un-capacitated facility location. While many of our results draw from rounding approaches recently developed for stochastic programming problems, we also show new applications of old metric rounding techniques for cut problems in this demand-robust setting.</p>
INDEX TERMS
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CITATION
Kedar Dhamdhere, Vineet Goyal, R. Ravi, Mohit Singh, "How to Pay, Come What May: Approximation Algorithms for Demand-Robust Covering Problems", FOCS, 2005, 2013 IEEE 54th Annual Symposium on Foundations of Computer Science, 2013 IEEE 54th Annual Symposium on Foundations of Computer Science 2005, pp. 367-378, doi:10.1109/SFCS.2005.42
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