Convexificators for nonconvex multiobjective optimization problems with uncertain data: robust optimality and duality
Peer reviewed, Journal article
Published version
Permanent lenke
https://hdl.handle.net/11250/3140188Utgivelsesdato
2023Metadata
Vis full innførselSamlinger
- Institutt for matematiske fag [2527]
- Publikasjoner fra CRIStin - NTNU [38679]
Sammendrag
In this paper, we investigate robust optimality conditions and duality for a class of nonconvex multiobjective optimization problems with uncertain data in the worst case by the upper semi-regular convexificator. The Fermat principle for a locally Lipschitz function is presented in terms of the upper semi-regular convexificator. We establish robust necessary optimality conditions of the Fritz-John type and KKT type for the uncertain nonconvex multiobjective optimization problems. In addition, robust sufficient optimality conditions as well as saddle point conditions are derived under the generalized ∂ˆ∗-pseudoquasiconvexity and generalized convexity, respectively. The robust duality relations between the original problem and its mixed robust dual problem are obtained under a generalized pseudoconvexity assumption.