What about wednesday? approximation algorithms for multistage stochastic optimization

Authors:
Anupam Gupta;Martin Pál;Ramamoorthi Ravi;Amitabh Sinha
Affiliations:
Dept. of Computer Science, Carnegie Mellon University, Pittsburgh, PA;DIMACS Center, Rutgers University, Piscataway, NJ;Tepper School of Business, Carnegie Mellon University, Pittsburgh, PA;Ross School of Business, University of Michigan, Ann Arbor, MI
Venue:
APPROX'05/RANDOM'05 Proceedings of the 8th international workshop on Approximation, Randomization and Combinatorial Optimization Problems, and Proceedings of the 9th international conference on Randamization and Computation: algorithms and techniques
Year:
2005

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Abstract

We study the problem of multi-stage stochastic optimization with recourse, and provide approximation algorithms using cost-sharing functions for such problems. Our algorithms use and extend the Boosted Sampling framework of [6]. We also show how the framework can be adapted to give approximation algorithms even when the inflation parameters are correlated with the scenarios.