A new characterization of (weak) Pareto optimality for differentiable vector optimization problems with GG-invex functions

In this paper, the so-called ηη-approximation approach is used to characterize solvability (in Pareto sense) of a nonlinear multiobjective programming problem with GG-invex functions with respect to the same function ηη. In this method, an equivalent ηη-approximated vector optimization problem is constructed by a modification of both the objective and the constraint functions in the original multiobjective programming problem at the given feasible point. Moreover, in order to find a (weak) Pareto optimal solution in the original multiobjective problem, it is sufficient to solve its associated ηη-approximated vector optimization problem equivalent to the original multiobjective programming problem in the sense discussed in this paper.