Characterizing strict efficiency for convex multiobjective programming problems

The article pertains to characterize strict local efficient solution (s.l.e.s.) of higher order for the multiobjective programming problem (MOP) with inequality constraints. To create the necessary framework, we partition the index set of objectives of MOP to give rise to subproblems. The s.l.e.s. of order m for MOP is related to the local efficient solution of a subproblem. This relationship inspires us to adopt the D.C. optimization approach, the convex subdifferential sum rule, and the notion of ε-subdifferential to derive the necessary and sufficient optimality conditions for s.l.e.s. of order m \geqq 1{m \geqq 1} for the convex MOP. Further, the saddle point criteria of higher order are also presented.