Epiconvergence of relaxed stochastic optimization problem - Archive ouverte HAL Access content directly
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.
Fichier principal
Vignette du fichier
epiconv_orl.pdf (167.03 Ko) Télécharger le fichier
Origin Files produced by the author(s)
Loading...

Dates and versions

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

Identifiers

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⟩
289 View
312 Download

Altmetric

Share

Gmail Mastodon Facebook X LinkedIn More