Sensitivity analysis of Wasserstein distributionally robust optimization problems

We consider sensitivity of a generic stochastic optimization problem to model uncertainty. We take a non-parametric approach and capture model uncertainty using Wasserstein balls around the postulated model. We provide explicit formulae for the first-order correction to both the value function and t...

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Detalles Bibliográficos
Autores principales: Bartl, Daniel, Drapeau, Samuel, Obłój, Jan, Wiesel, Johannes
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Royal Society 2021
Materias:
Research Articles
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8670962/
https://www.ncbi.nlm.nih.gov/pubmed/35153602
http://dx.doi.org/10.1098/rspa.2021.0176

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8670962/
https://www.ncbi.nlm.nih.gov/pubmed/35153602
http://dx.doi.org/10.1098/rspa.2021.0176

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