Stability and Scalarization in Vector Optimization Using Improvement Sets

Abstract

The aim of this paper is to study certain aspects of stability and scalarization of a nonconvex vector optimization problem through improvement sets. This paper attempts to investigate an open problem on stability posed by Chicco et al. The notion of stability is studied through Painleve---Kuratowski set-convergence, where we establish sufficiency conditions for the lower and upper set-convergences of optimal solution sets of a family of perturbed vector problems, both in the given space and its image space. The perturbations are performed both on the objective function and the feasible set. Further, by using a nonlinear scalarization function defined in terms of an improvement set, we establish lower and upper Painleve---Kuratowski set-convergences of sequences of approximate solution sets of certain scalarized problems. We then link these set-convergences with the set-convergences of optimal solution sets of the perturbed problems. Finally, we investigate the stability and scalarization of a linear vector optimization problem in finite dimensional spaces.