Optimality conditions in optimization under uncertainty

Abstract

Most optimization problems involve uncertain data due to measurement errors, unknown future developments, and modeling approximations. In this paper, we consider scalar optimization problems under uncertainty with infinite scenario sets. We apply methods from vector optimization in general spaces, set-valued optimization, and scalarization techniques to derive necessary optimality conditions for solutions of robust optimization problems.