Date of Original Version



Conference Proceeding

Abstract or Description

This paper studies an extension of the k-median problem where we are given a metric space (V, d) and not just one but m client sets {Si ⊆ V}im=1, and the goal is to open k facilities F to minimize: maxi∈[m] {Σj∈Si d(j, F)}, i.e., the worst-case cost over all the client sets. This is a "min-max" or "robust" version of the k-median problem; however, note that in contrast to previous papers on robust/stochastic problems, we have only one stage of decision-making---where should we place the facilities? We present an O(log n + log m) approximation for robust k-median: The algorithm is combinatorial and very simple, and is based on reweighting/Lagrangean-relaxation ideas. In fact, we give a general framework for (minimization) facility location problems where there is a bound on the number of open facilities. For robust and stochastic versions of such location problems, we show that if the problem satisfies a certain "projection" property, essentially the same algorithm gives a logarithmic approximation ratio in both versions. We use our framework to give the first approximation algorithms for robust/stochastic versions of k-tree, capacitated k-median, and fault-tolerant k-median.



Published In

Proceedings of the Nineteenth Annual ACM-SIAM Symposium on Discrete Algorithms , 1164-1173.