图的最小顶点覆盖问题的面上DNA解法

1994年，Adleman提出一种解决NP完全问题的新方法-DNA计算．之后又出现了许多关于DNA计算的改进操作并增加了其可靠性，其中面上操作是一种很有效的方法．本文利用DNA计算的固态处理(面上计算)解决了图论中又-NP完全问题一图的最小顶点覆盖问题．构造了含有6个顶点10条边的图的顶点集子集对应的数据池之后，进行了一系列的合成、杂交、清洗、变性等生物操作，得到所有覆盖对应的DNA序列，然后通过编址过程得到所要求的最小覆盖．     

基于二次误差度量的大型网格模型简化算法

针对传统网格模型简化算法无法处理尺寸大于内存容量的网格模型的问题,提出一种改进的基于二次误差度量的大型网格简化算法.在经典二次误差度量(quadric error metric,QEM)算法的基础上,改进算法引入顶点法向量夹角与边长作为权值,以及基于八叉树的模型划分策略.实验结果表明,该算法能够完成大型网格模型的简化,并且在简化过程中很好地保持了原模型的细节特征.     

网络流量的有效测量方法分析

把网络流量的有效测量问题抽象为求给定图G=(V,E)的最小弱顶点覆盖集的问题.给出了一个求最小弱顶点覆盖集的近似算法,并证明了该算法具有比界2(lnd+1),其中d是图G中顶点的最大度.指出了该算法的时间复杂性为O(|V|²).     

一种求解平面图的最小顶点覆盖算法

最小顶点覆盖问题是图论中经典的组合优化问题,在实际生活中有着广泛的应用价值。根据最小顶点覆盖与最大独立集在图论中事实上是属于等价问题这一特性,从最大独立集的角度出发,根据最大独立集的特性,设计了一种求解简单平面图的最大独立集算法,从而求出最小顶点覆盖。通过实验结果的比对验证算法的正确性和有效性。     

一种带控制节点的最小生成树聚类方法

综合考虑对象间相对距离和高等级对象对低等级对象的集聚效应这两种聚类影响因素，提出了一种带控制节点的最小生成树聚类方法。该方法用聚类对象间距离为权构建一棵最小生成树，将树中高等级节点作为分割最小树时选取被打断边的控制因素，使本次分割而成的两子树都包含控制节点，且被打断的边是在此条件下的最长边，最终使每棵子树包含且仅包含一个控制节点。检验自构建数据和地震数据的聚类结果证明，该方法在某些情况下能够较好地揭示数据分布的真实规律。     

OpenGL3．0新技术及其应用研究

随着计算机图形学的快速发展,OpenGL不断改变着人们对计算机图形显示的认识。OpenGL是一个优秀的三维图形硬件的软件接口,同时也是一个跨平台的、开放性的三维图形和模型库。从OpenGL发展历程以及各个阶段版本功能及特点,详细阐述了OpenGL3．0的新功能和新特性。从着色语言,顶点数组对象和条件渲染等方面介绍了OpenGL3．0的几个新技术,最后给出了一个以VC＋＋6．0为平台基于OpenGL3．0图像显示的例子。     

基于混合子分方法的曲面网格顶点与法向插值

顶点位置和法向插值是参数曲面造型的重要内容,文中基于混合子分方法生成三次B样条控制网格,使得相应的三次B样条曲面插值初始网格中指定的顶点,并通过引入插值模板的概念,把法向的插值转化为对模板的旋转变换,使得曲面在不改变2插值顶点的情况下插值法向,最后得到一张C^2连续的插值指定顶点和法向的曲面,与传统的逐片Bezier或Coons曲面片构造方法相比,此方法更为简洁且具有更高的连续阶,而且易于推广到高阶B样条和任意拓扑情形,具有较强的实用性。     

Finding Minimum Vertex Covering in Stochastic Graphs: A Learning Automata Approach

Structural and behavioral parameters of many real networks such as social networks are unpredictable, uncertain, and have time-varying parameters, and for these reasons, deterministic graphs for modeling such networks are too restrictive to solve most of the real-network problems. It seems that stochastic graphs, in which weights associated to the vertices are random variables, might be better graph models for real-world networks. Once we use a stochastic graph as the model for a network, every feature of the graph such as path, spanning tree, clique, dominating set, and cover set should be treated as a stochastic feature. For example, choosing a stochastic graph as a graph model of an online social network and defining community structure in terms of clique, the concept of a stochastic clique may be used to study community structures’ properties or define spreading of influence according to the coverage of influential users; the concept of stochastic vertex covering may be used to study spread of influence. In this article, minimum vertex covering in stochastic graphs is first defined, and then four learning, automata-based algorithms are proposed for solving a minimum vertex-covering problem in stochastic graphs where the probability distribution functions of the weights associated with the vertices of the graph are unknown. It is shown that through a proper choice of the parameters of the proposed algorithms, one can make the probability of finding minimum vertex cover in a stochastic graph as close to unity as possible. Experimental results on synthetic stochastic graphs reveal that at a certain confidence level the proposed algorithms significantly outperform the standard sampling method in terms of the number of samples needed to be taken from the vertices of the stochastic graph.     

LOD技术在地下水有限元后处理中的应用

地下水有限元后处理阶段的数据量较大,这对模型重现、网络快速传输和计算结果的实时可视化造成困难。为此,分析地下水有限元后处理中面临的主要问题和LOD技术,指出顶点元素删除法是一种适应地下水有限元后处理的有效数据模型简化方法,设计顶点删除和恢复过程中的主要数据结构,并应用DT方法对“空洞”进行局部三角剖分。实例证明该方法在地下水有限元后处理中应用的有效性。     

A 2-approximation NC algorithm for connected vertex cover and tree cover

The connected vertex cover problem is a variant of the vertex cover problem, in which a vertex cover is additional required to induce a connected subgraph in a given connected graph. The problem is known to be NP-hard and to be at least as hard to approximate as the vertex cover problem is. While several 2-approximation NC algorithms are known for vertex cover, whether unweighted or weighted, no parallel algorithm with guaranteed approximation is known for connected vertex cover. Moreover, converting the existing sequential 2-approximation algorithms for connected vertex cover to parallel ones results in RNC algorithms of rather high complexity at best.In this paper we present a 2-approximation NC (and RNC) algorithm for connected vertex cover (and tree cover). The NC algorithm runs in O(log²n) time using O(Δ²(m+n)/logn) processors on an EREW-PRAM, while the RNC algorithm runs in O(logn) expected time using O(m+n) processors on a CRCW-PRAM, when a given graph has n vertices and m edges with maximum vertex degree of Δ.