Generalized quasiconvex mappings and vector optimization

Quasiconvexity for mappings is generalized in such a way that this notion gives the sufficiency of necessary optimality conditions such as multiplier rules. One can show that it is the weakest type of generalized convexity notions in the sense that this generalized quasiconvexity holds if certain multiplier rules are sufficient for optimality. It also yields the equivalence of local and global minima. The theory is applied to a mufti-objective programming problem and a vector approximation problem.