僑生先修部
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Item Sufficiency Criteria And Duality For Complex Nondifferentiable Fractional Programming Involving Pseudoinvex Function(國立僑生大學先修班, 1998-07-??) 劉正傳; 林世昌; J. C. Liu; S. C. LinUnder different forms of invexity conditions, sufficient conditions and five duality models are presented for the nondifferentiable fractional programming problem containing square roots of positive semidefinite Hermitian quadratic forms.Item On Mixed Duality in Multiobjective Fractional Programming Involving Nonsmooth Pseudoinvex Functions(國立僑生大學先修班, 2005-12-??) 劉正傳; J.C.LiuIn this paper, we formulate one mixed type dual model to unify Wolfe type dual and Mond-Weir type dual for multiobjective fractional programming problem with nonsmooth pseudoinvex functions. We also establish weak, strong and strict converse duality theorems.Item Duality For Multiobjective Fractional Programming Involving Nonsmooth (F,p)-convex Functions(國立僑生大學先修班, 1996-07-??) 劉正傳; J. C. LiuWolfe type dual for multiobjective fractional programming problem is introduced and certain duality results have been derived in the framework of (F, p)-convex functions.Item Optimality and Duality For Generalized Fractional Programming Involving Nonsmooth Pseudoinvex Functions(國立僑生大學先修班, 1995-07-??) 劉正傳; J. C. LiuUsing a parametric approach,we establish necessary and sufficient conditions and derive duality theorems for a class of nonsmooth generalized minimax fractional programming problems containing pseudo-invex functions.Item Duality For NonditTerentiable Multiobjective Programming Without A Constraint Qualification(國立僑生大學先修班, 1993-07-??) 劉正傳; J. C. LiuNecessary and sufficient conditions without constraint qualification for pareto optimality of multiobjective programming are derived. This article suggests the establishment of a Wolfe-type duality theorem for nondifferentiable, convex, multiobjective minimization problems. The vector Lagrangian and the generalized saddle point for pareto optimality are studied. Key works. Multiobjective programming, pareto optimality, cone of directions, vector Lagrangian, Pareto saddle point.