Confidence Level Solutions for Stochastic Programming

Y. Nesterov and J.-P. Vial

Working Papers from Ecole des Hautes Etudes Commerciales, Universite de Geneve-

Abstract: We propose an alternative apporach to stochastic programming based on Monte-Carlo sampling and stochastic gradient optimization. The procedure is by essence probabilistic and the computed solution is a random variable. The associated objectiev value is doubly random, since it depends two outcomes: the event in the stochastic program and the randomized algorithm. We propose a solution concept in which the propability that the randomized algorithm produces a solution with an expected objective value departing from the optimal one by more than is small enough. We derive complexity bounds for this process. We show that by repeating the basic process on independent sample, one can significantly sharpen the complexity bounds.

Keywords: STOCHASTIC PROGRAMMING; MATHEMATICAL ANALYSIS (search for similar items in EconPapers)
JEL-codes: C61 (search for similar items in EconPapers)
Pages: 15 pages
Date: 2000
References: Add references at CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:fth:ehecge:2000.05

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