可重构造的网孔机器上的k-选择

对于一个 ｍ ×ｎ（ｍ ≤ｋ）的列有序矩阵,文中在 ｎ × ｎ 可重构造的网孔机器上提出了一个并行 ｋ选择算法,其时间复杂度为 Ｏ（ｌｏｇ２ｍ ＋ ｌｏｇｍ ｌｏｇ２ ｎ＋ ｌｏｇ３ ｎ）,而对于一般的ｌ元集,文中在相同的模型下提出了一个时间复杂度为 Ｏ ｌｏｇ２ ｌｎ ＋ ｌｏｇ ｌｎ ｌｏｇ２ ｎ＋ ｌｏｇ３ｎ＋ ｌｎ ｌｏｇ ｌｎ 的并行 ｋ选择算法．当时 ｌ≥ Ｏ（ｎｌｏｇ３ｎ／ｌｏｇ ｌｏｇｎ,该时间复杂度为 Ｏ ｌｎ ｌｏｇ ｌｎ ．特别地,当ｌ＝ Ｏ（ｎ１＋ ε）（ε＞ ０ 为常数）,则时间复杂度为 Ｏ ｌｎ ｌｏｇｎ ．此时达到的加速比为 ｎ／ｌｏｇｎ．     

最长公共子充列问题的改进快速算法

现在几个最常用的解决最长公共子序列（ＬＣＳ）问题的算法的时间复杂度分别是Ｏ（ｐｎ）,Ｏ（ｎ（ｍｐ））。这里Ｍ、ｎ为两个待比较字符串的长度,Ｐ是最长公共子串的长度。给出一种时间复杂度为Ｏ（ｐ（ｍｐ））,空间复杂度为Ｏ（ｍ＋ｎ）的算法。与以前的算法相比,不管在Ｐ〈〈ｍ的情况下,还是在Ｐ接近Ｍ时,这种算法都有更快的速度。     

高维空间最近点对问题的ε网算法

高维空间最近点对问题的ε网算法王晓东，傅清祥（福州大学）εＮＥＴＡＬＧＯＲＩＴＨＭＦＯＲＴＨＥＯＮ－ＬＩＮＥＣＬＯＳＥＳＴＰＡＩＲＰＲＯＢＬＥＭＳ￥ＷａｎｇＸｉａｏｄｏｎｇ；ＦｕＱｉｎｇｘｉａｎｇ（ＦｕｚｈｏｕＵｎｉｖｅｒｓｉｔｙ）Ａｂｓｔｒａｃｔ：...     

线性互补问题内点算法

线性互补问题内点算法徐成贤（西安交通大学）ＩＮＴＥＲＩＯＲＰＯＩＮＴＡＬＧＯＲＩＴＨＭＳＦＯＲＬＩＮＥＡＲＣＯＭＰＬＥＭＥＮＴＡＲＩＴＹＰＲＯＢＬＥＭＳ￥ＸｕＣｈｅｎｇ－ｘｉａｎ（ＤｅｐａｒｔｍｅｎｔｏｆＭａｔｈｅｍａｔｉｃｓ，Ｘｉ＇ａｎＪｉａｏｔｏ...     

一类扩展的Steiner树优化问题及其应用

本文提出了一个计算机网络通信和分布式系统中的一类扩展的Ｓｔｅｉｎｅｒ树问题．对此问题设计了两个求其最优解的算法．这两个算法的时间复杂性分别是Ｏ（３（ｋ－１）·ｎ＋２（ｋ－１）·ｎ２）和Ｏ（２（ｎ－ｋ）·ｎ２）．其中，ｋ是一棵Ｓｔｅｉｎｅｒ树需支撑的给定顶点的个数．     

几何算法求解货郎担问题

本文提出求解货郎担问题的一种几何算法。它的时间复杂性为：Ｏ（ｎ＾３／ｍ）次比较，Ｏ（ｎ＾２）次乘法，其中ｎ，ｍ分别是点集的点数和凸包顶点数。     

块角型约束线性规划问题的内点分解算法

块角型约束线性规划问题的内点分解算法吴力（中国科学院计算数学与科学工程计算研究所）ＡＤＥＣＯＭＰＯＳＩＴＩＯＮＡＬＧＯＲＩＴＨＭＦＯＲＬＩＮＥＡＲＰＲＯＧＲＡＭＭＩＮＧＰＲＯＢＬＥＭＳＷＩＴＨＢＬＯＣＫＡＮＧＵＬＡＲＣＯＮＳＴＲＡＩＮＴＳ￥ＷｕＬｉ（...     

货郎担问题最优并行启发式算法

本文给出了满足三角不等式的货郎担问题的并行启发式算法，在ＳＩＭＤ ＣＲＥＶ ＰＲＡＭ并行机上该算法使用Ｏ（ｎ＾３／ｌｏｇ＾２ｎ）台处理器需Ｏ熄ｌｏｇ＾２ｎ）时间，这里ｎ是给定城市的个数，因而该并行算法是最优的。     

应用嵌套排序的并行CHOLESKY分解算法

应用嵌套排序的并行ＣＨＯＬＥＳＫＹ分解算法迟学斌（中国科学院计算中心）ＰＡＲＡＬＬＥＬＣＨＯＬＥＳＫＹＤＥＣＯＭＰＯＳＩＴＩＯＮＵＳＩＮＧＮＥＳＴＥＤＤＩＳＳＥＣＴＩＯＮＴＥＣＨＮＯＬＯＧＹ￥ＣｈｉＸｕｅｂｉｎ（ＣｏｍｐｕｔｉｎｇＣｅｎｔｅｒ，Ａｃａ...     

一个选择算法及其并行化

文中用合并选择的思想及堆上的最佳算法，给出了求解选择问题的一个新算法及其相应的并行化。将串行合并选择算法的复杂度ｎＬｏｇｋ＋Ｏ（ｎ）降低到（ｎＬｏｇｋ）／２＋（ｎＬｏｇＬｏｇｋ）／２＋Ｏ（ｎ），并保持了原并行算法的结构，在ＳＩＭＤ树型机器的并行计算模型上，并行运行     

基于凸剖分的多边形窗口线裁剪算法

以不增加新点的方式将多边形剖分为一些凸多边形,并基于这些多边形的边建立二叉树进行管理.裁剪计算时,根据二叉树快速地找到与被裁剪线有相交的凸多边形,然后运用高效的凸多边形裁剪算法进行线裁剪.该方法能自适应地降低裁剪计算的复杂度,使其在O(logn)和O(n)之间变化,并在大多数情况下小于O(n),其中n是多边形边数.虽然该方法需要进行预处理,但在许多应用(如多边形窗口对多边形的裁剪)中,其总执行时间(包括预处理时间和裁剪时间)比已有的不需要预处理的裁剪算法少很多.     

An optimal online algorithm for halfplane intersection

The intersection of N halfplanes is a basic problem in computational geometry and computer graphics,The optimal offline algorithm for this problem runs in time O(N log N).In this paper,an optimal online algorithm which runs also in time O(N log N) for this problem is presented.The main idea of the algorithm is to give a new definition for the left side of a given line,to assign the order for the opoints of a convex polygon.and then to use binary search method in an ordered vertex set.The data structure used in the algorithm is no more complex than array.     

An Efficient Adaptive Algorithm for Constructing the Convex Differences Tree of a Simple Polygon

The convex differences tree (CDT) representation of a simple polygon is useful in computer graphics, computer vision, computer aided design and robotics. The root of the tree contains the convex hull of the polygon and there is a child node recursively representing every connectivity component of the set difference between the convex hull and the polygon. We give an O(n log K + K log² n) time algorithm for constructing the CDT, where n is the number of polygon vertices and K is the number of nodes in the CDT. The algorithm is adaptive to a complexity measure defined on its output while still being worst case efficient. For simply shaped polygons, where K is a constant, the algorithm is linear. In the worst case K = O(n) and the complexity is O(n log² n). We also give an O(n log n) algorithm which is an application of the recently introduced compact interval tree data structure.     

An algorithm for point cluster generalization based on the Voronoi diagram

This paper presents an algorithm for point cluster generalization. Four types of information, i.e. statistical, thematic, topological, and metric information are considered, and measures are selected to describe corresponding types of information quantitatively in the algorithm, i.e. the number of points for statistical information, the importance value for thematic information, the Voronoi neighbors for topological information, and the distribution range and relative local density for metric information. Based on these measures, an algorithm for point cluster generalization is developed. Firstly, point clusters are triangulated and a border polygon of the point clusters is obtained. By the border polygon, some pseudo points are added to the original point clusters to form a new point set and a range polygon that encloses all original points is constructed. Secondly, the Voronoi polygons of the new point set are computed in order to obtain the so-called relative local density of each point. Further, the selection probability of each point is computed using its relative local density and importance value, and then mark those will-be-deleted points as ‘deleted’ according to their selection probabilities and Voronoi neighboring relations. Thirdly, if the number of retained points does not satisfy that computed by the Radical Law, physically delete the points marked as ‘deleted’ forming a new point set, and the second step is repeated; else physically deleted pseudo points and the points marked as ‘deleted’, and the generalized point clusters are achieved. Owing to the use of the Voronoi diagram the algorithm is parameter free and fully automatic. As our experiments show, it can be used in the generalization of point features arranged in clusters such as thematic dot maps and control points on cartographic maps.     

货郎担问题的几何解法

本文提出货郎担问题的一种新的求解方法，即几何解法．它的时间复杂性为：求距离运算次数为Ｏ（ｎｍ），比较次数为Ｏ（ｍａｘ（ｎｍ，ｎｌｏｇｎ）），求夹角次数为Ｏ（n²/m），其中ｎ为点集中点的数目，ｍ为点集的凸包顶点数．     

平面点匹配的一点校准算法

点模式匹配是一项重要的视觉课题。对于一个平面点集,由平移和旋转并伴有一定噪声作用产生另一点集,提出一个基于一点校准的点模式快速匹配算法,并推广到带有属性点的匹配问题中。基于一点校准的点模式匹配算法,其计算复杂性为O(mn),其中m,n分别是两个点集所含点的个数,比基于两点距离近似相等的校准匹配算法,其计算复杂性为O(m2nl)(其中l为第二个点集中与第一个点集中任两个点的距离近似相等的平均个数),极大地减少了计算量。     

确定两个任意简单多边形交、并、差的算法

提出了把多边形的边分为奇偶边的新思想，根据输入多边形A，B之间边的拓扑关系，划分A，B边为内边、外边、重叠边3种，揭示A，B与它们的交、并、差之间边的本质联系，进而描述了确定任意两个简单多边形交、并、差算法．算法的时间复杂度为O((n m k)log(n m k))，其中n，m分别是A，B的顶点数，k是两多边形的交点数．算法建立在数学理论基础之上，很好地处理了布尔运算的奇异情形，比如重叠边，边与边相交于边的顶点等情形．本算法易于编程实现。     

Efficient geometric algorithms on the EREW PRAM

We present a technique that can be used to obtain efficient parallel geometric algorithms in the EREW PRAM computational model. This technique enables us to solve optimally a number of geometric problems in O(log n) time using O(n/log n) EREW PRAM processors, where n is the input size of a problem. These problems include: computing the convex hull of a set of points in the plane that are given sorted, computing the convex hull of a simple polygon, computing the common intersection of half-planes whose slopes are given sorted, finding the kernel of a simple polygon, triangulating a set of points in the plane that are given sorted, triangulating monotone polygons and star-shaped polygons, and computing the all dominating neighbors of a sequence of values. PRAM algorithms for these problems were previously known to be optimal (i.e., in O(log n) time and using O(n/log n) processors) only on the CREW PRAM, which is a stronger model than the EREW PRAM     

基于有序简单多边形的平面点集凸包快速求取算法

凸包问题是计算几何的基本问题之一，在许多领域均有应用。传统平面点集凸包算法和简单多边形凸包算法平行发展，互不相干。本文将改进的简单多边形凸包算法应用于平面点集凸包问题中，提出了新的点集凸包算法。该算法首先淘汰掉明显不位于凸包上的点，然后对剩余点集排序，再将点集按照一定顺序串联成有序简单多边形，最后利用前瞻回溯方法搜索多边形凸包，从而得到点集的凸包。本文算法不仅达到了Ｏ的理论时间复杂度下限，而且算法     

平面点集凸壳的快速算法

提出一种计算平面点集凸壳的快速算法。利用极值点划分出四个矩形,它们包含了所有凸壳顶点,通过对矩形中的点进行扫描,排除明显不是凸壳顶点的点,剩余的点构成一个简单多边形。再利用极点顺序法判断多边形顶点的凹凸性并删除所出现的凹顶点,最终得到一个凸多边形即为点集的凸壳。整个算法简洁明了,避免了乘法运算（除最坏情况外）,从而节省计算时间。