LAUSR

This paper devotes to the quasi $$\epsilon $$ϵ-solution (one sort of approximate solutions) for a robust convex optimization problem in the face of data uncertainty. Using robust optimization approach (worst-case approach), we establish approximate optimality theorem and approximate duality theorems in term of Wolfe type on quasi $$\epsilon $$ϵ-solution for the robust convex optimization problem. Moreover, some examples are given to illustrate the obtained results.