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Confidence Level Solutions for Stochastic Programming

Collection
  • Cahiers de recherche; 2000.05
Publication date2000
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.

Citation (ISO format)
NESTOROV, Yurii, VIAL, Jean-Philippe. Confidence Level Solutions for Stochastic Programming. 2000
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Identifiers
  • PID : unige:5864
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Technical informations

Creation04/15/2010 12:20:49 PM
First validation04/15/2010 12:20:49 PM
Update time03/14/2023 3:26:53 PM
Status update03/14/2023 3:26:53 PM
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