e-Pareto Optimality for Nondifferentiable Multiobjective Programming via Penalty Function

Necessary and sufficient conditions without a constraint qualification for e-Pareto optimality of multiobjective programming are derived. The necessary Kuhn]Tucker condition suggests the establishment of a Wolf-type duality theorem for nondifferentiable, convex, multiobjective minimization problems. The generalized e-saddle point for Pareto optimality of the vector Lagrangian is studied.