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Epi-convergent scenario generation method for stochastic problems via sparse grid

By Michael Chen and Sanjay Mehrotra


One central problem in solving stochastic programming problems is to generate moderate-sized scenario trees which represent well the risk faced by a decision maker. In this paper we propose an efficient scenario generation method based on sparse grid, and prove it is epi-convergent. Furthermore, we show numerically that the proposed method converges to the true optimal value fast in comparisonwith Monte Carlo and Quasi Monte Carlo methods

Topics: 510 Mathematik, ddc:510
Publisher: Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät II, Institut für Mathematik
Year: 2008
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