Robust optimization is proving to be a fruitful tool to study problems with uncertain data. In this paper we deal with the minmax aproach to robust multiobjective optimization. We survey the main features of this problem with particular reference to results concerning linear scalarization and sensitivity of optimal values with respect to changes in the uncertainty set. Furthermore we prove results concerning sensitivity of optimal solutions with respect to changes in the uncertainty set. Finally we apply the presented results to mean-variance portfolio optimization.

Sensitivity to uncertainty and scalarization in robust multiobjective optimization: an overview with application to mean-variance portfolio optimization

Rocca, M
2022-01-01

Abstract

Robust optimization is proving to be a fruitful tool to study problems with uncertain data. In this paper we deal with the minmax aproach to robust multiobjective optimization. We survey the main features of this problem with particular reference to results concerning linear scalarization and sensitivity of optimal values with respect to changes in the uncertainty set. Furthermore we prove results concerning sensitivity of optimal solutions with respect to changes in the uncertainty set. Finally we apply the presented results to mean-variance portfolio optimization.
2022
2022
Multiobjective optimization; Robust optimization; Portfolio optimization
Rocca, M
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11383/2141471
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