一类外平面图的有界染色

图G的k-有界染色是图G的一个最多有k个顶点染同一种颜色的顶点染色．图G的k-有界染色数χk(G)是指对图G进行k-有界染色所用的最少颜色数．讨论一类外平面图的k-有界染色，给出能在多项式时间内确定其k-有界染色数的一些充分条件．     

具非负曲率的完备非紧黎曼流形

将三维欧式空间旋转抛物面顶点的定义推广到一般的非负曲率完备非紧黎曼流形上,利用Perelman G证明Cheeger-Gromoll核心猜想的几何方法,讨论了具非负曲率的完备非紧黎曼流形M上的核心S的结构, 证明了如果由核心出发的法测地线均为射线,则或者S退化为一点,或者M=Rk×N,其中N是紧致的具非负曲率的黎曼流形.特别地,如果核心的维数仅比流形的维数低一维,可以证明其法测地线均为射线,从而有M=Rn-1×S.     

(k+1)-连通无K_(1,r)-图是Hamilton-连通的两个充分条件

一个图若不含与Ｋ１,ｒ（ｒ３）同构的导出子图,则称它为无Ｋ１,ｒ－图．本文将运用Ｔ－插点方法,通过对图的独立集的邻域交的研究,给出（ｋ＋１）－连通无Ｋ１,ｒ－图（ｒ４）是Ｈａｍｉｌｔｏｎ－连通的两个充分条件．     

图谱半径的一个不等式

设G是一个简单连通图,v是G的一个割点,G_1,G_2…G_p(p≥2)是G的v—分支,q是一个正整数(1≤q≤p)。令H_1=G_1∪G_2∪…∪G_q,H_2=G_(q+1)∪…∪G_p,ρ,ρ_1,ρ_2分别是G,H_1和H_2的谱半径。则有不等式     

图之极小顶点覆盖

图的一个顶点覆盖被称为极小的假如其任何真子集不再是该图的一顶点覆盖，本文证明了某些图之所有极小顶点覆盖的个数的明确表达式，其中包括路径，梯形图，及线性ｎ部图。     

具有三个单模的有限维遗传代数的Hochschild上同调群

计算代数的Hochschild上同调群是非常重要且复杂的,高维代数Hochschild上同调群维数的计算能否通过计算较低维代数的Hochschild上同调群的维数来实现是一个有趣的问题.本文利用有向图的顶点矩阵,通过计算二维代数的Hochschild上同调群的维数来计算具有三个单模的有限维遗传代数的Hochschild上同调群的维数.     

点可迁图的顶点划分

设G是k正则连通点可迁图。图G的一个边割S称为限制性边割，如果G－S不含孤立点，最小限制性边割所含的边数λ′称为限制性边连通度。已经证明λ′≤2k-2，等号成立时，称图G是极大限制性边连通的。本文证明了：如果G不是极大限制性边连通的，那么G的顶点集存在一个划分π＝（C1，…，Cm)，使得由Ch导出的子图同构于一个连通k-1正则点可迁图H,h=1,2，…，m，而且k≤|H|≤2k-3。     

Pn×Pm的邻点强可区别的全染色

讨论路的笛卡尔积的邻点可区别的全染色问题,给出路的笛卡尔积Pn×Pm的邻点强可区别的全色数为χast(Pn×Pm)=5 n=2,m≥2或m=n=36 min{n,m}≥3且m n≠6     

几种图的对策着色和对策色数

介绍了一种新的色对策和对策色数,比较了2种色对策的差异.对几种特殊的图形的色对策数进行了讨论,运用顶点标号方法,给出获胜策略.     

An Index mechanism for multi-scale view in spatial databases

There are numerous geometric objects stored in the spatial databases. An importance function in a spatial database is that users can browse the geometric objects as a map efficiently. Thus the spatial database should display the geometric objects users concern about swiftly onto the display window. This process includes two operations:retrieve data from database and then draw them onto screen. Accordingly, to improve the efficiency, we should try to reduce time of both retrieving object and displaying them. The former can be achieved with the aid of spatial index such as R-tree, the latter require to simplify the objects. Simplification means that objects are shown with sufficient but not with unnecessary detail which depend on the scale of browse. So the major problem is how to retrieve data at different detail level efficiently. This paper introduces the implementation of a multi-scale index in the spatial database SISP (Spatial Information Shared Platform) which is generalized from R-tree. The difference between the generalization and the R-tree lies on two facets: One is that every node and geometric object in the generalization is assigned with a importance value which denote the importance of them, and every vertex in the objects are assigned with a importance value,too. The importance value can be use to decide which data should be retrieve from disk in a query. The other difference is that geometric objects in the generalization are divided into one or more sub-blocks, and vertexes are total ordered by their importance value. With the help of the generalized R-tree, one can easily retrieve data at different detail levels.Some experiments are performed on real-life data to evaluate the performance of solutions that separately use normal spatial index and multi-scale spatial index. The results show that the solution using multi-scale index in SISP is satisfying.