Date of Original Version
Abstract or Description
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 . We also show how the framework can be adapted to give approximation algorithms even when the inflation parameters are correlated with the scenarios
C. Chekuri et al. (Eds.): APPROX and RANDOM 2005, LNCS 3624, 86-98.