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Journal Articles Operations Research Letters Year : 2019

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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Dates and versions

hal-00848275 , version 1 (26-07-2013)
hal-00848275 , version 2 (26-09-2013)
hal-00848275 , version 3 (02-11-2020)

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Cite

Vincent Leclère. Epiconvergence of relaxed stochastic optimization problem. Operations Research Letters, 2019, 47 (6), pp.553-559. ⟨10.1016/j.orl.2019.09.014⟩. ⟨hal-00848275v3⟩
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