关于E-凸函数及E-凸规划几个错误结论的修正

本文研究Youness在1999年建立的有关E凸函数和E规划的结论.利用E凸函数和E凸规划的基本性质和优化分析技术,获得了有关E凸函数E凸规划的几个错误结论的修正..     

E-凸函数的若干特征

讨论了一类广义的凸集和凸函数:E-凸集和E-凸函数的若干性质,并给出E-凸函数的一个判别准则．     

中点凸性与广义凸函数相关集合的对称性问题

研究十二类广义凸函数相关集合的对称性问题及其对应函数的中点凸性问题.证明了其中的十个集合具有对称性,应用反例说明了余下的两个集合不必具有对称性.证明了六个集合对应的函数具有中点凸性,应用反例说明了余下六个集合对应的函数不必具有中点凸性.     

E-凸函数和E-拟凸函数的等价条件

在更弱的连续假设下研究集合A_(x,y)={λ∈[0,1]|f(λE(x)+(1-λ)E(y))≤λf(E(x))+(1-λ)f(E(y))}和集合A′_(x,y)={λ∈[0,1]|f(λE(x)+(1-λ)E(y))≤max{f(E(x)),f(E(y))}}的稠密性、闭性、(弱)近似凸性,得到E-凸函数和E-拟凸函数的等价条件.     

E-凸Fuzzy集和E-反凸Fuzzy集

研究了广义凸Fuzzy集和广义反凸Fuzzy集以及它们的性质。通过将凸Fuzzy集和E-凸集相结合,提出了一种新的广义凸Fuzzy集———E-凸Fuzzy集,使得凸Fuzzy集成为它的特例,并对E-凸Fuzzy集的性质进行了初步研究。然后,类似地,通过将反凸Fuzzy集和E-凸集相结合,提出了一种新的广义反凸Fuzzy集———E-反凸Fuzzy集,使得反凸Fuzzy集成为它的特例,并对E-反凸Fuzzy集的性质进行了初步研究。     

广义凸函数的特征性质

提出广义凸集、广义凸函数、中间点广义凸函数、端点广义凸函数四个定义,通过定义条件P1,研究条件P1所蕴含的等式关系,进而得到一个基础性定理一稠密性定理和一个相对条件较弱的推论,最后将结果应用于若干不同类型的广义凸函数类,尤其是s-凸函数、几何凸函数、rp-凸函数,得到它们所共有的一个特征性质,即满足稠密性定理．     

广义凸函数相关集合的稠密性问题

应用新方法,研究十二类广义凸函数相关集合的稠密性问题.证明了其中的八个集合在[0,1]中是稠密的.应用反例说明了其中的四个集合在[0,1]中不必稠密.     

次b凸函数和次b凸规划

研究一种称为次b 凸函数的广义凸函数, 并介绍了次b 凸集的概念. 分别在一般情形及可微情形下讨论了次b 凸函数的相关性质, 得到了次b 凸函数成为拟凸函数及伪凸函数的充分条件. 最后, 在次b 凸函数的条件下给出了无约束及带不等式约束规划的最优性条件.     

凸函数课堂教学设计

在最优化方法教学中,笔者结合自己的理解和体会对凸函数的课堂教学进行了设计,并举例说明函数的凹凸性在不等式证明中的应用.     

广义凸集的支撑函数

本文讨论了笔者在[1]中提出的伪凸集,拟凸集的支撑函数与障碍锥的性质,并通过这些性质得出了二个闭性准则。     

E-Convex Sets, E-Convex Functions, and E-Convex Programming

A class of sets and a class of functions called E-convex sets and E-convex functions are introduced by relaxing the definitions of convex sets and convex functions. This kind of generalized convexity is based on the effect of an operator E on the sets and domain of definition of the functions. The optimality results for E-convex programming problems are established.     

On E-Convex Sets, E-Convex Functions, and E-Convex Programming

Recently, E-convex sets and E-convex functions were introduced in Ref. 1. However, some results seem to be incorrect. In this note, some counterexamples are given.     

具有(F,α,ρ,d)—凸的分式规划问题的最优性条件和对偶性

给出了一类非线性分式规划问题的参数形式和非参数形式的最优性条件,在此基础上,构造出了一个参数对偶模型和一个非参数对偶模型,并分别证明了其相应的对偶定理,这些结果是建立在次线性函数和广义凸函数的基础上的.     

Optimality Conditions and Duality for a Class of Nonlinear Fractional Programming Problems

In this paper, we present sufficient optimality conditions and duality results for a class of nonlinear fractional programming problems. Our results are based on the properties of sublinear functionals and generalized convex functions.     

具有(F α,ρ,d)-V-凸的非光滑多目标分式规划的最优性条件和对偶性

本文考虑了一类非光滑多目标分式规划问题,该多目标分式规划问题中所出现的函数是局部Lipschitz的．对该类多目标分式规划问题,引入了(F,α,ρ,d)-V-凸函数的概念,证明了有效解的充分条件和必要条件,构造出了一种参数对偶模型和一种半参数对偶模型,并证明了相应的对偶定理．     

A New Self-Dual Embedding Method for Convex Programming

In this paper we introduce a conic optimization formulation to solve constrained convex programming, and propose a self-dual embedding model for solving the resulting conic optimization problem. The primal and dual cones in this formulation are characterized by the original constraint functions and their corresponding conjugate functions respectively. Hence they are completely symmetric. This allows for a standard primal-dual path following approach for solving the embedded problem. Moreover, there are two immediate logarithmic barrier functions for the primal and dual cones. We show that these two logarithmic barrier functions are conjugate to each other. The explicit form of the conjugate functions are in fact not required to be known in the algorithm. An advantage of the new approach is that there is no need to assume an initial feasible solution to start with. To guarantee the polynomiality of the path-following procedure, we may apply the self-concordant barrier theory of Nesterov and Nemirovski. For this purpose, as one application, we prove that the barrier functions constructed this way are indeed self-concordant when the original constraint functions are convex and quadratic. We pose as an open question to find general conditions under which the constructed barrier functions are self-concordant.     

带有极值函数的多目标双层规划问题的对偶

For a multiobjective bilevel programming problem(P) with an extremal-value function,its dual problem is constructed by using the Fenchel-Moreau conjugate of the functions involved.Under some convexity and monotonicity assumptions,the weak and strong duality assertions are obtained.