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Joint dynamic probabilistic constraints with projected linear decision rules

Submission Status:published
  • We consider multistage stochastic linear optimization problems combining joint dynamic probabilistic constraints with hard constraints. We develop a method for projecting decision rules onto hard constraints of wait-and-see type. We establish the relation between the original (infinite dimensional) problem and approximating problems working with projections from different subclasses of decision policies. Considering the subclass of linear decision rules and a generalized linear model for the underlying stochastic process with noises that are Gaussian or truncated Gaussian, we show that the value and gradient of the objective and constraint functions of the approximating problems can be computed analytically.

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Metadaten
Author:Vincent Guigues, Rene Henrion
DOI:https://doi.org/10.1080/10556788.2016.1233972
Parent Title (English):Optimization Methods and Software
Document Type:Article
Language:English
Date of Publication (online):2016/10/07
Date of first Publication:2016/10/07
Release Date:2016/11/28
Volume:32
First Page:1006
Last Page:1032
Institutes:Weierstraß-Institut für Angewandte Analysis und Stochastik
Subprojects:B04