Clifford分析中拟Cauchy型积分的H(o)lder连续性

以拟Cauchy型积分公式及超正则函数的Plemelj公式为基础,进一步研究了拟Cauchy型积分的Holder连续性:即对两点都在边界上;一点在边界上,另一点在区域内(区域外);两点都在区域内(两点都在区域外)这三种情形分别进行了研究.     

一种改进的随机Hough变换检测圆的方法

针对随机Hough变换检测圆准确率低的问题,提出了一种改进的随机Hough变换算法.算法利用原标准随机Hough变换的检测结果,确定出合适的阈值,将检测结果中平均误差超过此阈值的圆视为错检圆,并将这些圆上的点取出,重新进行随机Hough变换,直到正确检测出所有圆.模拟数据和真实图像的数值实验都表明改进算法的检测准确率明显提高.     

单纯形上二次型函数的最值搜索算法

由于单纯形域上的二次型函数往往是多峰函数,当函数形式较为复杂时难以求得全局最值.构造了两类适用于单纯形上二次型函数优化的算法,分别是单纯形上的Newton-Raphson算法与随机搜索算法.经过实例验证,这两种算法都是有效的.     

基于PCA-K-means和PCA-SOM神经网络的葡萄酒分类

针对葡萄酒物理和化学数据成分冗余,提出了两种葡萄酒分类的算法,分别是主成分分析K均值和主成分分析自组织神经网络算法.这两种算法对葡萄酒的物理化学成分进行了主成分分析,提取了主要的影响因素,将输入维数降低,再利用K均值和自组织神经网络算法分别对葡萄酒进行分类和比较.实验结果表明,PCA-K-means和PCA-SOM都具有较高的准确率,都有一定的使用价值和可操作性,并且PCA-K-means算法优于其它的算法.     

可数族弱Bregman相对非扩展映像的收敛性分析

在自反Banach空间中,引入可数族弱Bregman相对非扩张映像概念,构造了两种迭代算法求解可数族弱Bregman相对非扩张映像的公共不动点.在适当条件下,证明了两种迭代算法产生的序列的强收敛性.     

求解欠定线性方程组稀疏解的算法

针对欠定线性方程组稀疏解的求解问题,文中提出两个改进的迭代重加权最小范数解算法(IRMNS)及一个光滑的0函数算法.其中,第一个算法基于 q(q∈(0,1])范数提出的,当q较小的时候,算法可以增强恢复稀疏解的能力;第二个算法是直接由0范数最小化问题提出的,它可以看做是第一个算法在q =0时的拓展;第三个算法是通过用一个光滑函数来近似0范数从而将原问题进行转化求解的.数值例子表明这三种算法都是快速有效的.     

基于DEA和SFA的我国商业银行效率研究

本文利用板块数据,分别采用非参数前沿法中的DEA法和参数前沿法中的SFA法对我国十四家商业银行1997-2001期间的综合效率进行了测度,在此基础上对两种方法测度出的银行效率值排序进行了相关分析和一致性检验,结果表明两种方法测度出的银行效率在数值上有显著差异,但在是效率排序上具有很好的一致性。     

求解时间分布阶扩散方程的两个高阶有限差分格式

基于复化Simpson公式和复化两点Gauss-Legendre公式，构造了两个求解时间分布阶扩散方程的高阶有限差分格式.不同于以往文献中提出的时间一阶或二阶格式，这两种格式在时间方向都具有三阶精度，而在分布阶和空间方向可达到四阶精度.数值结果表明，两种算法都是稳定且收敛的，从而是有效的.两种格式的收敛速率也通过数值实验进行了验证，并且通过和文献中的算法对比可以得出其更为高效,     

一种新的中文文本分类特征提取的研究

特征提取是中文文本分类中的关键,传统的互信息算法没有考虑特征存在负值时,互信息量对分类的影响,因此削弱了这些特征在分类中的作用.首先提出一种改进的互信息算法,对特征和类的互信息量取绝对值的方法来克服这个缺点.然后实现了基于传统互信息KNN算法和改进互信息FV-KNN算法的两种中文文本自动分类系统.最后实验结果表明改进互信息FV-KNN算法在分类准确率、分类招回率和分类速度上都有较大的提高.     

一种守恒型间断跟踪法在一维单守恒律方程上的程序实现

茅在近几年发展了一种守恒型的间断跟踪法（见[6],[7]），该跟踪法是以解的守恒性质作为跟踪的机制而不是传统的跟踪法利用Rankine-Hugoniot条件。本文的目的是对该算法在一维单守恒律的情况进行程序实现，做成一个对任意初值问题都适应的强健的算法，可处理任意的间断相互作用。在文章的第三节给出了一个数值算例，并与用ENO格式（见[8]所算得的结果进行比较。     

Some performance tests of convex hull algorithms

The two-dimensional convex hull algorithms of Graham, Jarvis, Eddy, and Akl and Toussaint are tested on four different planar point distributions. Some modifications are discussed for both the Graham and Jarvis algorithms. Timings taken of FORTRAN implementations indicate that the Eddy and Akl-Toussaint algorithms are superior on uniform distributions of points in the plane. The Graham algorithm outperforms the others on those distributions where most of the points are on or near the boundary of the hull.     

Pseudo-conjugate directions for the solution of the nonlinear unconstrained optimization problem on a parallel computer

Parallel algorithms and test case results for the solution of the unconstrained optimization problem are presented. The algorithms involve the use of pseudo-conjugate directions (directions which tend to become conjugate as the solution is approached). It is shown that the algorithms are both fast and robust. Although all the algorithms of this paper involve the parallel execution of linear search procedures, a critical differentiation can be made among them, depending on whether the linear searches are performed along the same direction (parallel unidirectional algorithms) or different directions (parallel multidirectional algorithms).     

Properties of some convolution algorithms for the thermal analysis of semiconductor devices

This paper deals with three kinds of convolution algorithms designed for the thermal analysis of semiconductor devices and electronic circuits with the use of the lumped thermal models. Such kind of models and algorithms is especially convenient to analyse the electronic circuits consisting of a high number of thermally sensitive devices. The fundamental features, such as: stability, convergence and accuracy of these algorithms, are considered and investigated in the paper. In the investigations the exponential test function describing the dissipated power is taken into account. It was analytically proved that the considered algorithms are stable and convergent. The analytical formulas describing the values of the local and total cut off error of the algorithms are proposed. The theoretical considerations are accompanied by some calculation results illustrating the influence of the values of thermal parameter models and the size of the analysis step on the accuracy of calculations carried out with the considered algorithms.     

Dynamic programming algorithms for the optimal cutting of equal rectangles

This paper presents dynamic programming algorithms for generating optimal guillotine-cutting patterns of equal rectangles. The algorithms are applicable for solving the unconstrained problem where the blank demand is unconstrained, the constrained problem where the demand is exact, the unconstrained problem with blade length constraint, and the constrained problem with blade length constraint. The algorithms are able to generate the simplest optimal patterns to simplify the cutting process. When the sheet length is longer than the blade length of the guillotine shear used, the dynamic programming algorithm is applied to generate optimal layouts on segments of lengths no longer than the blade length, and the knapsack algorithm is employed to find the optimal layout of the segments on the sheet. The computational results indicate that the algorithms presented are more efficient than the branch-and-bound algorithms, which are the best algorithms so far that can guarantee the simplest patterns.     

Subgradient algorithms on Riemannian manifolds of lower bounded curvatures

The subgradient algorithms for convex optimizations on Riemannian Manifolds of sectional curvatures bounded from below are studied; and the convergence results of the algorithms (employing diminishing step sizes and dynamic step sizes) are established. Some numerical experiments are provided to illustrate the convergence performances of the algorithms.     

Random Variable Generation Using Concavity Properties of Transformed Densities

Abstract

Algorithms are developed for constructing random variable generators for families of densities. The generators depend on the concavity structure of a transformation of the density. The resulting algorithms are rejection algorithms and the methods of this article are concerned with constructing good rejection algorithms for general densities.     

A cell exclusion algorithm for determining all the solutions of a nonlinear system of equations

A new group of methods named cell exclusion algorithms (CEAs) is developed for finding all the solutions of a nonlinear system of equations. These types of algorithms, different in principle from those of homotopy, interval and cell-mapping-dynamical-analysis approaches, are based on cellular discretization and the use of a certain simple necessity test of the solutions. The main advantages of the algorithms are their simplicity, reliability, and general applicability. Having all features of interval techniques (but without using interval arithmetic) and with complexity O(log(1/)), the algorithms improve significantly on both the interval algorithms and the cell mapping techniques. Theoretical analysis and numerical simulations both demonstrate that CEAs are very efficient.     

现代优化计算方法在蛋白质结构预测中的应用

现代优化计算方法在蛋白质结构预测中占有重要地位.简要地介绍了模拟退火算法,遗传算法,人工神经网络和图论算法在蛋白质结构预测中的应用.对国内外近年来应用这些算法,特别是在蛋白质构象搜索问题中,解决蛋白质结构预测的研究作了回顾,并分析、比较了这几种算法的效果和特点.     

A comparison of sequential Delaunay triangulation algorithms

This paper presents an experimental comparison of a number of different algorithms for computing the Delaunay triangulation. The algorithms examined are: Dwyer's divide and conquer algorithm, Fortune's sweepline algorithm, several versions of the incremental algorithm (including one by Ohya, Iri and Murota, a new bucketing-based algorithm described in this paper, and Devillers's version of a Delaunay-tree based algorithm that appears in LEDA), an algorithm that incrementally adds a correct Delaunay triangle adjacent to a current triangle in a manner similar to gift wrapping algorithms for convex hulls, and Barber's convex hull based algorithm.

Most of the algorithms examined are designed for good performance on uniformly distributed sites. However, we also test implementations of these algorithms on a number of non-uniform distributions. The experiments go beyond measuring total running time, which tends to be machine-dependent. We also analyze the major high-level primitives that algorithms use and do an experimental analysis of how often implementations of these algorithms perform each operation.     

一类锥模型非单调信赖域算法及收敛性分析

本文对无约束优化问题提出了一类基于锥模型的非单调信赖域算法.二次模型非单调信赖域算法是新算法的特例.在适当的条件下,证明了算法的全局收敛性及Q-二次收敛性.