On approximate solutions for robust convex semidefinite optimization problems

Abstract

In this paper, we consider approximate solutions ( $$\epsilon $$ -solutions) for a convex semidefinite programming problem in the face of data uncertainty. Using robust optimization approach (worst-case approach), we prove an approximate optimality theorem and approximate duality theorems for $$\epsilon $$ -solutions in robust convex semidefinite programming problem under the robust characteristic cone constraint qualification. Moreover, an example is given to illustrate the obtained results.