Epiconvergence of relaxed stochastic optimization problem

Abstract : In this paper we consider the relaxation of a dynamic stochastic optimization problem where we replace a stochastic constraint - for example an almost sure constraint - by a conditional expectation constraint. We show an epiconvergence result relying on the Kudo convergence of $\sigma-$algebra and continuity of the objective and constraint operators. We also present some classicals constraints in stochastic optimization and give some conditions insuring their continuity. We conclude with a decomposition algorithm that uses such a relaxation.
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https://hal.archives-ouvertes.fr/hal-00848275
Contributor : Vincent Leclère <>
Submitted on : Thursday, September 26, 2013 - 2:28:43 PM
Last modification on : Friday, December 1, 2017 - 1:19:55 AM
Long-term archiving on : Friday, April 7, 2017 - 3:29:54 AM

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Vincent Leclère. Epiconvergence of relaxed stochastic optimization problem. 2013. ⟨hal-00848275v2⟩

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