# 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.
Keywords :
Type de document :
Pré-publication, Document de travail
2013

https://hal.archives-ouvertes.fr/hal-00848275
Contributeur : Vincent Leclère <>
Soumis le : jeudi 26 septembre 2013 - 14:28:43
Dernière modification le : lundi 21 mars 2016 - 11:29:43

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EpiConv.pdf
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### Identifiants

• HAL Id : hal-00848275, version 2

### Citation

Vincent Leclère. Epiconvergence of relaxed stochastic optimization problem. 2013. <hal-00848275v2>

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