Characterizations of Approximate Duality and Saddle Point Theorems for Nonsmooth Robust Vector Optimization. (11th March 2020) - Sun, Xiangkai; Tang, Liping; Zeng, Jing
Abstract: This article deals with robust approximate Pareto efficient solutions for a class of nonsmooth vector optimization problems with data uncertainty. By using robust optimization and scalarization methodology, we first obtain some nonsmooth approximate optimality conditions for this uncertain optimization problem. Then, we introduce a Wolfe-type robust approximate dual problem for this uncertain optimization problem and obtain some nonsmooth robust approximate duality properties. Moreover, we investigate nonsmooth approximate saddle point theorems of an uncertain approximate vector Lagrangian function associated with this uncertain optimization problem.