非凸分式优化问题的最优性充分条件和对偶

通过引入广义弧连通概念,在Rn空间中,研究极大极小非凸分式规划问题的最优性充分条件及其对偶问题.首先获得了极大极小非凸分式规划问题的最优性充分条件;然后建立分式规划问题的一个对偶模型并得到了弱对偶定理,强对偶定理和逆对偶定理.     

广义弧式连通凸锥优化问题的最优性条件及对偶问题

给出了弧式连通凸锥优化问题的强有效解和Benson真有效解的最优性条件,讨论了目标函数和约束函数均为广义弧式连通凸锥函数优化问题的近似有效解的最优性条件,给出了相应的近似Mond-Weir型对偶模型,给出了弱对偶和逆对偶定理.     

极小极大分式优化的一个对偶

在函数广义弧连通意义下,建立了极小极大分式优化问题一个对偶模型,并获得了弱对偶和强对偶结果。     

对称弧式连通凸多目标半无限规划的最优性

以弧式连通函数和对称梯度为基础,研究新函数在多目标半无限规划下的最优性理论.定义了一类新的弧式连通函数,对称弧式连通函数、对称拟弧式连通函数、对称弱拟弧式连通函数、对称伪弧式连通函数、对称严格伪弧式连通函数,讨论了这些函数在多目标半无限规划下的最优性.给出更加广义的弧式连通函数,将它们运用到多目标半无限规划.     

带约束条件集值优化问题近似Henig有效解集的连通性

研究了带约束条件集值优化问题近似Henig有效解集的连通性.在实局部凸Hausdorff空间中,讨论了可行域为弧连通紧的,目标函数为C-弧连通的条件下,带约束条件集值优化问题近似Henig有效解集的存在性和连通性.并给出了带约束条件集值优化问题近似Henig有效解集的连通性定理.     

依赖团数的有向图极大弧连通的充分条件

互连网络通常以有向图为模型,有向图的弧连通度是网络可靠性的一个重要参数.设D是一个有向图,δ(D)是最小度,弧连通度为λ(D),则λ(D)≤δ(D).当λ(D)=δ(D)时,称有向图D是极大弧连通的.本文给出了依赖团数的有向图极大弧连通的一些充分条件.     

引理与弧连通完全分配格

称一个完全分配格L满足Urysohn条件,如果对任一正规空间X及X的任意两个不交闭子集A,B都有连续映射fX→L,使得f［A］=0,f［B］=1,这里L赋予区间拓扑.本文证明了完全分配格L满足Urysohn条件当且仅当L弧连通,而L弧连通又等价于L有同构于单位区间I的极大链     

弧连通锥-凸数学规划的最优性条件

在弧连通锥-凸假设下讨论Hausdorff局部凸空间中的一类数学规划的最优性条件问题.首先,利用择一定理得到了锥约束标量优化问题的一个必要最优性条件.其次,利用凸集分离定理证明了无约束向量优化问题关于弱极小元的标量化定理和一个一致的充分必要条件.所得结果深化和丰富了最优化理论及其应用的内容.     

含有BF—I函数的多目标变分问题的混合对偶性

在有效解的意义下,对一类含有BF—I函数的多目标变分问题给出了混合型对偶的强对偶定理、弱对偶定理和严格逆对偶定理。     

依赖团数的有向图极大与超级边连通的度序列条件

互连网络通常以有向图为模型,有向图的弧连通度是网络可靠性的一个重要参数.给出了依赖团数的有向图极大和超级边连通的度序列条件.     

Optimality Conditions and Duality Involving Arcwise Connected and Generalized Arcwise Connected Functions

Some properties of arcwise connected functions in terms of their directional derivatives are investigated. These properties are then utilized to establish necessary and sufficient optimality conditions for scalar-valued nonlinear programming problems. Mond–Weir type duality results are also proved.     

Arcwise Connected Cone-Convex Functions and Mathematical Programming

The concept of arcwise connected cone-convex functions in topological vector spaces is introduced. Optimality conditions and duality theorems for a vector-valued nonlinear programming problem involving arcwise connected cone-convex functions are discussed.     

Optimality conditions and duality on approximate solutions in vector optimization with arcwise connectivity

In this paper a new class of generalized vector-valued arcwise connected functions, termed sub-arcwise connected functions, is introduced. The properties of sub-arcwise connected functions are derived. The approximate quasi efficient solutions of vector optimization problems are studied, and the necessary and sufficient optimality conditions are obtained under the assumption of arcwise connectivity. An approximate Mond-Weir type dual problem is formulated and the duality theorems are established.     

Complex Minimax Fractional Programming of Analytic Functions

We prove that a minmax fractional programming problem is equivalent to a minimax nonfractional parametric problem for a given parameter in complex space. Using a parametric approach, we establish the Kuhn-Tucker type necessary optimality conditions and prove the existence theorem of optimality for complex minimax fractional programming in the framework of generalized convexity. Subsequently, we apply the optimality conditions to formulate a one-parameter dual problem and prove weak duality, strong duality, and strict converse duality theorems involving generalized convex complex functions. This research was partly supported by NSC, Taiwan.     

Duality for minmax programming involving V-invex functions

Sufficient optimality conditions and duality results for a class of minmax programming problems are obtained under V-invexity type assumptions on objective and constraint functions. Applications of these results to certain fractional and generalized fractional programming problems are also presented     

On optimality and duality theorems of nonlinear disjunctive fractional minmax programs

This paper is concerned with the study of optimality conditions for disjunctive fractional minmax programming problems in which the decision set can be considered as a union of a family of convex sets. Dinkelbach’s global optimization approach for finding the global maximum of the fractional programming problem is discussed. Using the Lagrangian function definition for this type of problem, the Kuhn–Tucker saddle point and stationary-point problems are established. In addition, via the concepts of Mond–Weir type duality and Schaible type duality, a general dual problem is formulated and some weak, strong and converse duality theorems are proven.