Hedging Uncertainty: Approximation Algorithms for Stochastic Optimization Problems

Abstract

We study two-stage, finite-scenario stochastic versions of several combinatorial optimization problems, and provide nearly tight approximation algorithms for them. Our problems range from the graph-theoretic (shortest path, vertex cover, facility location) to set-theoretic (set cover, bin packing), and contain representatives with different approximation… (More)
DOI: 10.1007/978-3-540-25960-2_8

Topics

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