1) partial metric space

偏度量空间
2) metric space

度量空间
1.
Iterative processes for generalized asymptotically non-expansive mapping in convex metric space;
度量空间中广义渐进非扩张映射Ishikawa迭代的收敛性问题
2.
Discussion on the sets both open and close in the metric space;

度量空间中既开又闭的集合探讨
3.
Chain recurrent points and ω-limiting points in metric space;

度量空间中的链回归点与ω-极限点
3) metric spaces

度量空间
1.
A rectifible property of sets in metric spaces;

关于度量空间中泛流的注记(英文)
2.
In this paper,internal characterizations onthe compact-covering cs-pi images of metric spaces and the compact-covering cs images of locally separable metric spacesare obtained.
给出了度量空间的紧覆盖cs-π映象和局部可分度量空间的紧覆盖cs映象的内在刻画。
3.
Internal Characterizations of CS?mapping images( Compact ? covering CS ? mapping images ) of metric spaces.
分别建立了度量空间在CS 映射和紧复盖CS 映射下的象空间的特征 。
4) spatial measure

空间度量
1.
This paper gives a definition of spatial measure on spatial data cube for multi-source data on "Digital City",and describes basic concept of aggregation on spatial measure,and explains aggregation process of point spatial measure,line spatial measure,area spatial measure by legend,and states basic aggregation principle of spatial measure,and expresses foundatio.
叙述了面向“数字城市”多源数据的空间数据立方体空间度量的基本定义;描述了空间度量的聚集概念,并结合具体的图例阐述了点状、线状、面状空间度量的聚集过程;解释了空间数据立方体维上钻、维下翻、维层次上钻、维层次下翻的空间度量聚集操作基本原理。
5) partially ordered vector space

偏序向量空间
6) convex metric space

凸度量空间
1.
New Ishikawa iteration approximation with errors for asymptotically quasi-nonexpansive mappings in convex metric space;
凸度量空间中渐近拟非扩张映象新的带误差的Ishikawa迭代逼近
2.
Convergence of Ishikawa type iterative sequence of asymptoticallyquasi-nonexpansive mappings in convex metric space;
凸度量空间中渐近拟非扩张映象的Ishikawa型迭代序列的收敛性
3.
A fixed point existence theorem and a convergence theorem in convex metric spaces;

完备凸度量空间中不动点定理与收敛定理
补充资料:度量空间
度量空间 metric space 具有度量的抽象空间,设X是一个集合,若有定义在X×X上的非负实值函数d,满足①d(x,y)≥0,d(x,y)=0 ![]() n维欧几里得空间(Rn,d):Rn={(x1,…,xn)|xi∈R,i=1,2,…,n },d(x,y)= ![]() 希尔 伯特空 间(l2;d):l2={(x1,x2,…,xn…) ![]() 函数空间(ρ[0,1],d):C[0,1]={f:f为[0,1]上的实值连续函数},对任意f,g∈C[0,1],d(f,g)=max{|f(x)-g(x)|}。 x∈[0,1] 对度量空间(X,d)可引进拓扑结构,即以包含开球B(x,r)={y∈X|d( x,y)<r }的集为邻域定义拓扑,称为d所诱导的拓扑。 |
说明:补充资料仅用于学习参考,请勿用于其它任何用途。
参考词条