necessary conditions in generalized semi-infinite optimization with nondifferentiable convex data

In this paper we focus on generalized semi-infinite optimization problem in which the index set of the inequality constraints depends on the decision vector and all emerging functions are assumed to be convex, not necessarily differentiable. we introduce three constraint qualifications which are based on the convex subdifferential, and derive some kuhn-tucker type necessary optimality conditions for the problem.