Robust duality and saddle point characterizations for nonconvex multiobjective optimization with data uncertainty
Journal article
Submitted version
Permanent lenke
https://hdl.handle.net/11250/3142087Utgivelsesdato
2023Metadata
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- Institutt for matematiske fag [2468]
- Publikasjoner fra CRIStin - NTNU [38127]
Originalversjon
Journal of Nonlinear and Convex Analysis. 2023, 24 (2), 447-461.Sammendrag
This paper is devoted to investigate the robust duality and saddle point characterizations of nonconvex multiobjective optimization with data uncertainty in both the objective and constraints. Based on the robust necessary optimality conditions, we introduce a mixed type robust dual model of the uncertain multiobjective optimization problem, which covers the Wolfe type dual model and Mond-Weir type dual model as special cases. The weak robust duality, strong robust duality and converse robust duality between the robust dual model and the robust counterpart of original problem are established under some suitable conditions. Moreover, we also obtain the robust saddle point type su_cient and necessary optimality conditions for the uncertain multiobjective optimization problem under the generalized convexity assumptions.