Abstract
This paper considers conjugate duality in multiobjective optimization, in which minimality (efficiency, noninferiority, or Pareto optimality) is a natural solution concept. First, conjugate maps and subgradients are defined for vector-valued functions and point-to-set maps. Embedding the primal problem into a family of perturbed problems enables us to define a dual problem in terms of the conjugate map of the perturbed objective function. Every solution to the stable primal problem is associated with a solution to the dual problem, which is characterized as a subgradient of the perturbation map. This pair of solutions is also a saddle point of the Lagrangian.
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