集值优化问题超有效解的Lagrange最优性条件

在局部凸空间中考虑约束集值优化问题（VP）在超有效解意义下的Lagrange最优性条件．在近似锥-次类凸假设下，利用择一性定理得到了（VP）取得强有效解的必要条件，利用超有效解集的性质及超有效解的定义给出了（VP）取得超有效解的充分条件，最后给出了一种与（VP）等价的无约束规划．     

向量值最优化问题的最优性条件与对偶性

本文我们首先给出一类向量值优化问题（VP）的正切锥真有效解的定义，在锥方向导数的假设下，讨论了一类单目标问题 的最优性必要条件；然后利用正切锥方向导数定义一类正切锥F－凸函数类，并给出了（VP）正切锥真有效解的充分性条件，最后我们亦讨论了（VP）在正切锥真有效解意义下的对偶性质。     

拓扑向量空间中非光滑向量极值问题的最优性条件与对偶

本文提出了向量值函数的锥D-s凸，锥D-s拟凸，s右导数及锥D-s伪凸等新概念，探讨了锥D-s凸函数的有关性质，建立了带约束非光滑向量极值问题(VP)的最优性必要条件与涉及锥D-s凸(拟凸，伪凸)函数的约束极值问题(VP)的最优性充分条件，给出了原问题(VP)与其Mond-Weir型对偶问题的弱对偶与强对偶结论，揭示了(VP)的局部锥D-(弱)有效解与整体锥D-(弱)有效解，(VP)的锥D-弱有效解与锥D-有效解的关系，所得结果拓广了凸规划及部分广义凸规划的有关结论．     

集值优化问题超有效解的Kuhn-Tucker最优性条件

在Hausdorff局部凸拓扑线性空间中考虑约束向量集值优化问题(VP)的超有效性．在近似锥-次类凸假设下,利用择一性定理得到了Kuhn-Tucker型最优性必要条件,利用标量化定理得到了Kuhn-Tucker型最优性充分条件．最后给出了一种与(VP)等价的无约束优化．     

广义凸拓扑线性空间集值优化的ε-强有效解

在Hausdorff局部凸拓扑线性空间中考虑约束集值优化问题(VP)的ε-强有效性.在内部锥类凸假设下,利用凸集分离定理,分别建立了关于ε-强有效解的标量化定理和ε-Lagrange乘子定理.     

向量优化问题的几个收敛性结果

在文本中，获得了集合的弱有效元与真有效元的几个收敛性结果。然后，讨论了集值映射向量优化问题（VP）和它的近似问题（VP）n，在较强的假设条件下，获得了（VP）n的真有效解的几个收敛性结果。     

关于正α齐次算子方程的一个定理及其应用

本文在较宽松的条件下，研究了正α齐次算子方程（α＞１）解的存在唯一性并给出了应用．     

集值映射多目标规划的K-T最优性条件

讨论集值映射多目标规划(VP)的最优性条件问题.首先,在没有锥凹的假设下,利用集值映射的相依导数,得到了(VP)的锥--超有效解要满足的必要条件和充分条件.其次,在锥凹假设和比推广了的Slater规格更弱的条件下,给出了(VP)关于锥--超有效解的K--T型最优性必要条件和充分条件.     

多目标规划ak—较多有效解类的若干性质

在（１）中,作者提出多目标规划的较多有效解和较多最优解概念,并研究了它们的基本性质,文（３）则讨论ｋ－较多最优解的若干性质。文（４）利用较多序类进一步引进多目标规划问题的ａｋ－较多有效解,并证明了这类解的最优性必要条件。本文再给出多目标规划问题的ａｋ－较多最优解的概念,并讨论了多目标规划ａｋ－较多有效解和ａｋ－较多最优解的若干重要性质。     

反应扩散方程解的渐近性态

本文使用锥映象不动点指数的计算方法，讨论一类反应扩散方程正静态解的存在性，并给出方程的静态解渐适性态．然后，利用上，下解的方法讨论相应周期系统周期解的存在性及其渐近性态．     

近似锥-次类凸集值优化的严有效性

在Hausdorff局部凸拓扑线性空间中考虑约束集值优化问题(VP)的严有效性.在近似锥-次类凸假设下,利用凸集分离定理,分别得到了Kuhn-Tucker型和Lagrange型最优性条件,建立了与(VP)等价的两种形式的无约束优化.     

拟不变凸集值优化的Kuhn-Tucker条件与Wolfe对偶

研究了拟不变凸集值优化最优性的Kuhn-Tucker条件及Wolfe型对偶问题．首先引进了alpha-阶G-拟不变凸集和alpha-阶S-拟不变凸集值函数的概念,由此研究了alpha-阶G-拟不变凸集所对应的伴随切锥及alpha-阶伴随导数的性质;最后,借助alpha-阶伴随切导数刻画了alpha-阶S-拟不变凸集值优化最优性的Kuhn-Tucker条件和Wolfe型对偶．     

Some kind of Pareto stationarity for multiobjective problems with equilibrium constraints

ABSTRACT

In this paper, we derive some optimality and stationarity conditions for a multiobjective problem with equilibrium constraints (MOPEC). In particular, under a generalized Guignard constraint qualification, we show that any locally Pareto optimal solution of MOPEC must satisfy the strong Pareto Kuhn-Tucker optimality conditions. We also prove that the generalized Guignard constraint qualification is the weakest constraint qualification for the strong Pareto Kuhn-Tucker optimality. Furthermore, under certain convexity or generalized convexity assumptions, we show that the strong Pareto Kuhn-Tucker optimality conditions are also sufficient for several popular locally Pareto-type optimality conditions for MOPEC.     

Second-order sufficient optimality conditions for local and global nonlinear programming

This paper presents a new approach to the sufficient conditions of nonlinear programming. Main result is a sufficient condition for the global optimality of a Kuhn-Tucker point. This condition can be verified constructively, using a novel convexity test based on interval analysis, and is guaranteed to prove global optimality of strong local minimizers for sufficiently narrow bounds. Hence it is expected to be a useful tool within branch and bound algorithms for global optimization.     

Optimality conditions and constraint qualifications in Banach space

In this paper, necessary optimality conditions for nonlinear programs in Banach spaces and constraint qualifications for their applicability are considered. A new optimality condition is introduced, and a constraint qualification ensuring the validity of this condition is given. When the domain space is a reflexive space, it is shown that the qualification is the weakest possible. If a certain convexity assumption is made, then this optimality condition is shown to reduce to the well-known extension of the Kuhn-Tucker conditions to Banach spaces. In this case, the constraint qualification is weaker than those previously given.This work was supported in part by the Office of Naval Research, Contract Number N00014-67-A-0321-0003 (NRO 47-095).     

Differentiable programming in Banach spaces

For an abstract differentiate mathematical programming problem defined in a Banach space we obtain generalized Kuhn-Tucker necessary conditions for optimality. We obtain these conditions by replacing the usual closed cone hypothesis by a closed range condition on a suitable linear operator. The latter condition is automatically satisfied in finite dimensions.