具有等式与不等式约束条件拟可微优化的Lagrange乘子型最优性条件

讨论了不等式约束优化问题中拟微分形式下Ｆｒｉｔｚ Ｊｏｈｎ必要条件与 Ｃｌａｒｋｅ广义梯度形式下Ｆｒｉｔｚ Ｊｏｈｎ必要条件的关系．在较弱条件下给出了具有等式与不等式约束条件的两个Ｌａｇｒａｎｇｅ乘子形式的最优性必要条件,在这两个条件中等式约束函数的拟微分和Ｃｌａｒｋｅ广义梯度分别被使用。     

具有混合约束条件的拟可微函数的Fritz John条件

对于含有等式与不等式约束条件的拟可微函数（在Ｄｅｍｙａｎｏｖ和Ｒｕｂｉｎｏｖ意义下）优化问题，本文给出了Ｆｒｉｔｚ Ｊｏｈｎ形式最优性条件，改进了已有的结果。     

广义拟可微函数的微分

本文给出一种广义拟可微函数类,它是Demyanov与Rlubinov(1980)意义下拟可微函数的推广,通过凸集类对的空间的某些理论,建立了这类广义拟可微函数的微分学理论,包括加法运算、数乘运算、乘法运算、除法运算、极大值运算,极小值运算以及复合运算的微分公式和中值定理。这些结果为广义拟可微类函数优化研究提供了基本工具．     

不可微多目标规划的最优性条件

众所周知,Clarke、Ioffe和Aubin对非光滑分析做出了开创性的奠基工作,然而当应用它们解决有关实际问题时,在不少情况下还不很理想,特别是,他们所定义的向量函数广义梯度都比较难计算,这常造成相应不可微多目标规划算法在具体实现时的困难。     

不可微凸规划问题上Lagrange乘子的存在性与值函数的次可微性

本文讨论了一类非光滑凸规划问题，给出了Ｌａｇｒａｎｇｅ乘子的存在性与值函数的次可微性的关系和乘子存在的充分条件。     

一类复合不可微规划的二阶最优性条件

本文给出了一类复合不可微规划的二阶最优性条件,拓广了R·Fletcher的结论.     

不可微多目标优化

本文首先说明了什么是不呆微多目标优化问题，然后概括性地介绍了多目标优化研究的主要内容，在此基础上，对不可微多目标优化的主要结果和内容加以综述。     

一类拟可微函数的极小化算法

本文给出一类拟可微函数的极小化问题min f(x)=f₀(x)-maxf_i(x),x∈Rⁿ的算法,其中f₀是凸函数,f_i是连续可微函数,I是一个有限的指标集.算法的核心是对次微分作外接多面体近似.该算法属于下降算法.有关算法的理论作了详细的论述.     

一类不可微规划的二阶最优性条件

本文给出了一类复合不可微规划的二阶最优性条件，拓广了Ｒ·Ｆｌｅｔｃｈｅｒ的结论。     

Demyanov Difference of Two Sets and Optimality Conditions of Lagrange Multiplier Type for Constrained Quasidifferentiable Optimization

In the first part of this paper, the Demyanov difference of two sets is considered. An expression for the Demyanov difference of two sets, which are the convex hulls of a finite number of points, is presented. In the second part, first-order necessary optimality conditions of the Lagrange multiplier type, for quasidifferentiable optimization with equality and inequality constraints, are given by means of the Demyanov difference of subdifferential and negative superdifferential.     

On First Order Optimality Conditions for Vector Optimization

We develop first order optimality conditions for constrained vector optimization. The partial orders for the objective and the constraints are induced by closed and convex cones with nonempty interior. After presenting some well known existence results for these problems, based on a scalarization approach, we establish necessity of the optimality conditions under a Slater-like constraint qualification, and then sufficiency for the K-convex case. We present two alternative sets of optimality conditions, with the same properties in connection with necessity and sufficiency, but which are different with respect to the dimension of the spaces to which the dual multipliers belong. We introduce a duality scheme, with a point-to-set dual objective, for which strong duality holds. Some examples and open problems for future research are also presented,     

First and Second-Order Optimality Conditions for Convex Composite Multiobjective Optimization

Multiobjective optimization is a useful mathematical model in order to investigate real-world problems with conflicting objectives, arising from economics, engineering, and human decision making. In this paper, a convex composite multiobjective optimization problem, subject to a closed convex constraint set, is studied. New first-order optimality conditions for a weakly efficient solution of the convex composite multiobjective optimization problem are established via scalarization. These conditions are then extended to derive second-order optimality conditions.     

Optimality Conditions for Nonconvex Constrained Optimization Problems

Abstract

We present optimality conditions for a class of nonsmooth and nonconvex constrained optimization problems. To achieve this aim, various well-known constraint qualifications are extended based on the concept of tangential subdifferential and the relations between them are investigated. Moreover, local and global necessary and sufficient optimality conditions are derived in the absence of convexity of the feasible set. In addition to the theoretical results, several examples are provided to illustrate the advantage of our outcomes.     

First Order Optimality Conditions for Generalized Semi-Infinite Programming Problems

We study first-order optimality conditions for the class of generalized semi-infinite programming problems (GSIPs). We extend various well-known constraint qualifications for finite programming problems to GSIPs and analyze the extent to which a corresponding Karush-Kuhn-Tucker (KKT) condition depends on these extensions. It is shown that in general the KKT condition for GSIPs takes a weaker form unless a certain constraint qualification is satisfied. In the completely convex case where the objective of the lower-level problem is concave and the constraint functions are quasiconvex, we show that the KKT condition takes a sharper form. The authors thank the anonymous referees for careful reading of the paper and helpful suggestions. The research of the first author was partially supported by NSERC.     

集值优化问题ε-严有效性的最优性条件

本文是文[1]工作的继续,对ε-严有效性开展进一步的研究.对于集值优化问题(SVP),在有关映射为锥-类凸的假设条件下,得到了ε-(真)严有效点(解)的ε-Lagrange乘子、ε-真严鞍点和ε-Lagrange型对偶等结果.     

非光滑半无限多目标优化问题的最优性充分条件

研究了一个非光滑半无限多目标优化问题(简记为SIMOP),并讨论了它的最优性条件.首先, 通过对目标函数和约束函数的某种组合赋予Clarke F-凸性假设, 获得了SIMOP(弱)有效解的最优性充分条件.接下来, 用Chankong-Haimes方法建立了此SIMOP的一个标量问题并得到了这个标量问题的最优性充分条件.     

多目标优化问题Proximal真有效解的最优性条件

在广义凸性假设下,给出了集合proximal真有效点的线性标量化,并在此基础上证明了它与Benson真有效点和Borwein真有效点的等价性.将这些结果应用到多目标优化问题上,得到proximal真有效解的最优性条件.最后,利用proximal次微分,得到了proximal真有效解的模糊型最优性条件.     

Optimality Conditions for the Difference of Convex Set-Valued Mappings

Using the concept of subdifferential of cone-convex set valued mappings recently introduced by Baier and Jahn J. Optimiz. Theory Appl. 100 (1999), 233–240, we give necessary optimality conditions for nonconvex multiobjective optimization problems. An example illustrating the usefulness of our results is also given. Mathematics Subject classification: Primary 90C29, 90C26; Secondary 49K99.