弱拟凸集的一些性质及其应用

在文[1]中,我们提出了赋范线性空间中伪凸、弱拟凸等广义凸集的概念,并探究了其逼近性质.本文将给出[1]中所提出的广义凸集中最弱的一种集——弱拟凸集的最佳逼近特征、强唯一性及弱拟凸集的强分离定理.并把所获的结果应用到 L_p(T,m)空间中去,得到了 L_1(T,m)空间中最佳逼近的特征和唯一性及 L_p(T,m)(1相似文献   

Banach空间中线性算子的(集值)度量广义逆及其齐性单值选择

为研究Banach空间中不适定线性算子方程的最佳逼近解,Nashed在文[1]中引入了Banach空问中线性算子T的(集值)度量广义逆T的概念,并提出“求解线性算子的(集值)度量广义逆的具有良好性质的单值选择是值得研究”的公开问题.本文首先证明了Banach空间中线性算子的度量广义逆是具有闭凸值的集值映射,给出了该度量广义逆的等价表达式,并利用Banach空间的再赋范方法,给出其有界齐性的单值选择,部分地解决了Nashed所提出的公开问题.     

强E-凸性下赋范空间为内积空间的刻画

首先在赋范线性空间中引入了一类广义强凸集值映射,称之为强E-凸集值映射.其次利用Radstr?m消去律研究了强E-凸集值映射的一些基本性质.最后,给出了强E-凸集值映射形式下赋范线性空间为内积空间的刻画条件.     

内积空间中的h-强凸集值映射

本文首先在赋范线性空间中引入一类广义强凸集值映射,称之为h-强凸集值映射.其次利用R?dstr?m消去律研究了h-强凸集值映射的一些基本性质.最后,给出h-强凸集值映射形式下赋范线性空间为内积空间的刻画条件.     

赋$\beta$-范空间中的最佳逼近问题

本文讨论赋$\beta$-范空间中的最佳逼近问题.以[1]引进的共轭锥为工具,借助[2]中关于$\beta$-次半范的Hahn-Banach延拓定理,第二节给出赋$\beta$-范空间的闭子空间中最佳逼近元的特征,第三节得到赋$\beta$-范空间中任何凸子集或子空间均为半Chebyshev集的充要条件是空间本身严格凸,文章最后证明了严格凸的赋$\beta$-范空间中任何有限维子空间都是Chebyshev集.     

关于弱拟凸集与广义凸集逼近的几点注记

本文证明了赋范线性空间中闭弱拟凸集必为凸集,并指出郭元明的”弱拟凸集的一些性质及其应用”、“广义凸集的联合逼近特性”两文中的主要结论实质上是已知的结果.     

线性拓扑空间中太阳集的若干逼近性质

1958年Efimov-steckin引入“太阳集”(sun)概念.Brosowski、Amir-Deutsch,Brosowski-Deutsch广泛研究了它的性质.太阳集是凸集的弱化.在赋范空间中的逼近理论有重要作用.因为在逼近问题上,这种集合往往可以取代凸集的作用.这个词的意思是:所谓G是个太阳集,乃是对于G以外任一元素x G.若g_0∈G是对x的一切G中元的一个佳逼元(如果存在),则对于从g_0引通过x的射线的一切元素说,g_0也是其一个佳逼元.在赋范空间中,这里所研究佳逼性,就是范数||g-x||当g遍历G而到达最小值的意思.现在扩充概念到线性拓扑空间X中.设给定了一个实值泛函数φ(x),规定为绝齐性、次加性的.即     

关于Fuzzy赋范空间的若干问题（Ⅱ）

在1984年,吴从炘、方锦暄和A.K.Katsaras分别提出了两种Fuzzy赋范空间的定义。这些概念既是赋范空间概念的自然推广,又是特殊的Fuzzy拓扑线性空间。在文[3～5]中,不仅考察了这两种定义之间的关系,还讨论了Fuzzy赋范空间的性质以及其上广义Fuzzy线性算子的连续性等。在本文中,我们将继文[4]给出Fuzzy赋范空间中子集有界性、稠密性的刻划条件并利用这些条件给出Fuzzy范数是诱出的充要条件。此外,作为诱出Fuzzy范数的推广,我们给出了两类Fuzzy范数的特征刻划。     

联合最佳逼近的特征和唯一性

§1.引言和预备工作设E是赋范线性空间,G、F是E的子集,F有界,若存在g_0∈G,满足则称g_0是F在G中的联合最佳逼近元,其全体记为Z_0(F)。近年来有不少文章研究了联合最佳逼近的存在性、特征及唯一性等问题(如见[1]—[6])。本文引入了严格太阳集的概念,并指出它是凸集的弱化,然后考虑G是严格太阳集时,建立了联合最佳逼近的特征和唯一性。另外还给出了G是任意集时,联合最佳逼近的特征。     

几乎满的ε-等距算子的等距逼近问题

我们从减弱文Vestfrid[1]中定理3中空间一致凸条件和加强ε-等距算子条件着手去研究Banach空间中几乎满的ε-等距算子的等距逼近问题．另外，我们结合完备的β-范（0〈β〈1）空间的性质得到一些相关结论．     

近似凸集的某些性质

主要研究了两类近似凸集的关系和性质.首先,举例说明两类近似凸集没有相互包含关系.其次,在近似凸集(nearly convex)条件下,证明了在一定条件下函数上图是近似凸集与凸集的等价关系.同时,考虑了近似凸函数与函数上图是近似凸集的等价刻画、近似凸函数与函数水平集是近似凸集的必要性,并用例子说明近似凸函数与函数水平集是近似凸集的充分性不成立.最后,基于近似凸函数和拟凸函数的概念,给出了近似拟凸函数的概念并研究了近似拟凸函数与水平集是近似凸集的等价刻画.     

Convex extensions of the convex set valued maps

In this article the existence of the convex extension of convex set valued map is considered. Conditions are obtained, based on the notion of the derivative of set valued maps, which guarantee the existence of convex extension. The conditions are given, when the convex set valued map has no convex extension. The convex set valued map is specified, which is the maximal convex extension of the given convex set valued map and includes all other convex extensions. The connection between Lipschitz continuity and existence of convex extension of the given convex set valued map is studied.     

积分凸性及其应用

该文在Banach空间中通过向量值函数的Bochner积分引进集合与泛函的积分凸性以及集合的积分端点等概念. 文章主要证明有限维凸集、开凸集和闭凸集均是积分凸集，下半连续凸泛函与开凸集上的上半连续凸泛函均是积分凸的, 非空紧集具有积分端点, 对紧凸集来说其积分端点集与端点集一致, 最后给出积分凸性在最优化理论方面的两个应用.     

平面有限点集中的最大面积六边形

若平面上的有限点集构成凸多边形的顶点集,则称此有限点集处于凸位置令P表示平面上处于凸位置的有限点集,研究了P的子集所确定的凸六边形的面积与CH(P)面积比值的最大值问题.     

Condition numbers and error bounds in convex programming

After a brief survey on condition numbers for linear systems of equalities, we analyse error bounds for convex functions and convex sets. The canonical representation of a convex set is defined. Other representations of a convex set by a convex function are compared with the canonical representation. Then, condition numbers are introduced for convex sets and their convex representations.     

Homotopy classification of projective convex sets

A subset of projective space is called convex if its intersection with every line is connected. The complement of a projective convex set is again convex. We prove that for any projective convex set there exists a pair of complementary projective subspaces, one contained in the convex set and the other in its complement. This yields their classification up to homotopy.     

Optimality conditions in convex optimization revisited

The phrase convex optimization refers to the minimization of a convex function over a convex set. However the feasible convex set need not be always described by convex inequalities. In this article we consider a convex feasible set which is described by inequality constraints that are locally Lipschitz and not necessarily convex or differentiable. We show that if the Slater constraint qualification and a simple non-degeneracy condition is satisfied then the Karush–Kuhn–Tucker type optimality condition is both necessary and sufficient.     

A geometric inequality on mixed volumes

We develop some geometric inequality for a kind of generalized convex set. The integral of (n – 2)-th mean curvature of the generalized convex set, the mixed volume of the convex hull of the set, and a reference convex set are involved in the inequality.Partially supported by grants from Kosef and BSRI-95-1419.     

Motzkin decomposition of closed convex sets

Theodore Motzkin proved, in 1936, that any polyhedral convex set can be expressed as the (Minkowski) sum of a polytope and a polyhedral convex cone. This paper provides five characterizations of the larger class of closed convex sets in finite dimensional Euclidean spaces which are the sum of a compact convex set with a closed convex cone. These characterizations involve different types of representations of closed convex sets as the support functions, dual cones and linear systems whose relationships are also analyzed in the paper. The obtaining of information about a given closed convex set F and the parametric linear optimization problem with feasible set F from each of its different representations, including the Motzkin decomposition, is also discussed.     

Strong Restricted-Orientation Convexity

Strong restricted-orientation convexity is a generalization of standard convexity. We explore the properties of strongly convex sets in multidimensional Euclidean space and identify major properties of standard convex sets that also hold for strong convexity. We characterize strongly convex flats and halfspaces, and establish the strong convexity of the affine hull of a strongly convex set. We then show that, for every point in the boundary of a strongly convex set, there is a supporting strongly convex hyperplane through it. Finally, we show that a closed set with nonempty interior is strongly convex if and only if it is the intersection of strongly convex halfspaces; we state a condition under which this result extends to sets with empty interior.