关于多重网格并行计算中拟边界Jacobi成份的影响

1．引言分布式存储并行计算环境中,高效率的获取一般通过区域分解或数据分割实现大粒度并行[4]．因此,对于有效求解偏微分方程的多重网格算法[1,6],并行计算均采用网格划分进行任务分配[5],实现大粒度并行．其中,松弛算子并行度是影响算法并行效率的关键因素．Gauss－SeidellGS）点或块松弛、ILU松弛和适合于方程组的分布式GS松弛（DGS）[1,6]为多重网格算法的有效松弛算子,但本质上都是串行的．尽管采用红黑（RB）序可增强并行度,但每次松弛在每层网格上仍需两次数据交换门（即所有子网格相互交换拟边界数据）．为了减少通…     

关于分块松弛算子中拟边界Jacobi成份的影响

分布式存贮并行计算环境中，高效率的获取一般通过区域分解或数据分割实现大粒度并行~[4]。因此，对于有效求解偏微分方程的多重网格算法~([1]、[7]),并行计算均采用网格划分进行任务分配~[6]以实现大料度并行。通讯的主体存在于松弛算子，其并行度是影响算法并行效率高低的关键因素。     

Toeplitz矩阵填充的 $\ell$-步修正增广拉格朗日乘子算法[英文]

基于 Toeplitz矩阵填充(TMC)的修正增广拉格朗日乘子(MALM)算法, 本文给出此算法的一种加速策略, 提出Toeplitz矩阵填充的 $\ell$-步修正增广拉格朗日乘子算法. 该方法通过削减原 MALM算法中每一步迭代的频繁数据传输, 提高算法的运行效率. 同时也证明了新算法的收敛性. 最后以数值实验表明 $\ell$-步修正增广拉格朗日乘子算法比原 MALM算法更有效.     

分布式L_(1/2)正则化

研究数据集被分割并存储于不同处理器时的特征提取和变量选择问题,其中处理器通过某种网络结构相互连接.提出分布式L_(1/2)正则化方法,基于ADMM算法给出分布式L_(1/2)正则化算法,证明了算法的收敛性.算法通过相邻处理器之间完成信息交互,其变量选择结果与数据集不分割时利用L_(1/2)正则化相同.实验表明,所提出的新算法有效、实用,适合于分布式存储数据处理.     

Toeplitz矩阵填充的?-步修正增广拉格朗日乘子算法（英文）

基于Toeplitz矩阵填充(TMC)的修正增广拉格朗日乘子(MALM)算法,本文给出此算法的一种加速策略,提出Toeplitz矩阵填充的?-步修正增广拉格朗日乘子算法.该方法通过削减原MALM算法中每一步迭代的频繁数据传输,提高算法的运行效率.同时也证明了新算法的收敛性.最后以数值实验表明?-步修正增广拉格朗日乘子算法比原MALM算法更有效.     

一种基于网格模型的无限传感器网络K区域覆盖算法

目前无限传感器网络K覆盖问题的解决机制大都有颇为苛刻的假设条件,如要求节点具有很强的能量、节点的感知区域能被精确定义等.提出了一个基于布尔感知理论的分布式网格模型及相应的K区域覆盖算法,能够很好地处理感知区域形状不规则及大小发生变化的K覆盖问题,算法时间复杂度小,适用范围广泛.仿真实验证明了算法的有效性.     

非线性抛物问题的二重网格混合元算法

针对一类非线性抛物方程的混合元形式,本文提出了二重网格算法.该算法是在网格大小为H的粗网格上求解—个非线性系统,再在网格大小为h的细网格上进行两次线性计算.算法第二步和第三步的误差分别为O(△_t~2 h~(k 1) H~(2K 2)),O(△_t~2 h~(k 1) h~(-d/2)H~(4k 4)),其中k为逼近空间的多项式的次数,d为空间维数.该估计对H的选取起了很大的作用.对于粗网格上的非线性计算,本文给出了L~p(2≤p<∞)模误差估计.     

一种用于分布式同步的令牌算法

本文提出了一种称为令牌算法的分布式同步算法.按此算法每达成一次互斥协议仅需发送信件N封,此处N为网络中结点的个数,而采用其它的几种分布式同步算法时需发送2N或更多的信件.本文还讨论了分布式同步算法的好环标准.按本文提出的标准,令牌算法是最佳的分布式同步算法.     

于改进UGF算法的线性拓扑WSN系统可靠性评估

提出了面向感知数据融合的通用发生函数(UGF)改进算法,并使用该算法对线性拓扑结构的无线传感网络(WSN)可靠性进行了评估。首先对PEGASIS协议下WSN的线性拓扑结构及数据传输过程进行抽象,建立了双向连续k/n:F系统模型。然后根据WSN感知数据传输及融合方式,在改进算法中重新定义了传感节点的UGF表达式和组合算子。最后对双向连续k/n:F模型进行单向化分解,根据得到的单向模型可靠性推导出双向模型的可靠性表达式。通过具体实例对提出的改进算法进行了验证,计算结果显示改进的算法可有效解决传感网络线性拓扑结构可靠性评估问题。     

并行间断有限元算法求解Navier-Stokes方程

间断Galerkin有限元方法非常适合在非结构网格上高精度求解Navier-Stokes方程,然而其十分耗费计算资源.为了提高计算效率,提出了高效的MIMD并行算法.采用隐式时间离散GMRES+LU SGS格式,结合多重网格方法,当地时间步长加速算法收敛.为了保证各处理器间负载平衡,采用区域分解二级图方法划分网格,实现内存合理分配,数据只在相邻处理器间传递.数值模拟了RAE2822翼型和M6黏性绕流,加速比基本呈线性变化且接近理想值.结果表明了该算法能有效减少计算时间、合理分配内存,具有较高的加速比和并行效率,适合于MIMD粗粒度科学计算.     

黄河万家寨水利枢纽水库冰情定点连续自动监测系统的设计与应用

基于空气、冰与水的物理特性差异实现对冰水情连续自动检测的原理,作者设计、研制了适应高寒地区水电大坝冰情定点连续监测的冰水情自动检测传感器与信号采集系统,通过在黄河万家寨水利枢纽水库现场对冰情近4个月的冬季连续试验,获取了水库冰层内部系统的冰水情数据,采集数据客观反映了水库库区监测点冰层生消变化的规律,为确保引黄入晋工程的安全运行,也为高寒地区河道与水电大坝的冰情预警提供了一种可行的检测方法与设备.     

An efficient rough feature selection algorithm with a multi-granulation view

Feature selection is a challenging problem in many areas such as pattern recognition, machine learning and data mining. Rough set theory, as a valid soft computing tool to analyze various types of data, has been widely applied to select helpful features (also called attribute reduction). In rough set theory, many feature selection algorithms have been developed in the literatures, however, they are very time-consuming when data sets are in a large scale. To overcome this limitation, we propose in this paper an efficient rough feature selection algorithm for large-scale data sets, which is stimulated from multi-granulation. A sub-table of a data set can be considered as a small granularity. Given a large-scale data set, the algorithm first selects different small granularities and then estimate on each small granularity the reduct of the original data set. Fusing all of the estimates on small granularities together, the algorithm can get an approximate reduct. Because of that the total time spent on computing reducts for sub-tables is much less than that for the original large-scale one, the algorithm yields in a much less amount of time a feature subset (the approximate reduct). According to several decision performance measures, experimental results show that the proposed algorithm is feasible and efficient for large-scale data sets.     

一种基于Bloom Filter的频繁模式挖掘算法

挖掘最大频繁项集是多种数据挖掘应用中的关键问题.针对频繁模式挖掘的可扩展性问题,基于Bloom Filter的相关理论,提出了一种"挖掘最频繁的K个元素"的Mining Top-K算法.该算法基于推广的Bloom Filter的数据结构,能够较为准确地筛选出数据流中出现最频繁的K个元素,并估算这K个元素的出现频数.实验结果表明:该方法在具有低空间复杂度特性的同时又不会失去准确性.     

基于Shadowed Sets的连续属性离散化

基于Shadowed Sets理论研究了粗糙集连续属性离散化问题,提出一种新的基于Shadowed Sets 理论的候选断点集提取算法.该算法根据实例在单属性上的分布,对数据样本进行分类,采用Shadowed Sets计算出各类的上下近似,最终提取出候选断点集.使用多组UCI数据对此算法的性能进行检验,同时还与其它候选断点集提取算法做了对比实验.实验结果表明,此算法能有效地减少数据集候选断点的数目,提高离散化算法运行速度和识别率.     

An EM Algorithm for Estimating Equations

This article presents an algorithm for accommodating missing data in situations where a natural set of estimating equations exists for the complete data setting. The complete data estimating equations can correspond to the score functions from a standard, partial, or quasi-likelihood, or they can be generalized estimating equations (GEEs). In analogy to the EM, which is a special case, the method is called the ES algorithm, because it iterates between an E-Step wherein functions of the complete data are replaced by their expected values, and an S-Step where these expected values are substituted into the complete-data estimating equation, which is then solved. Convergence properties of the algorithm are established by appealing to general theory for iterative solutions to nonlinear equations. In particular, the ES algorithm (and indeed the EM) are shown to correspond to examples of nonlinear Gauss-Seidel algorithms. An added advantage of the approach is that it yields a computationally simple method for estimating the variance of the resulting parameter estimates.     

A self-sorting in-place fast Fourier transform algorithm suitable for vector and parallel processing

Summary. We propose a new algorithm for fast Fourier transforms. This algorithm features uniformly long vector lengths and stride one data access. Thus it is well adapted to modern vector computers like the Fujitsu VP2200 having several floating point pipelines per CPU and very fast stride one data access. It also has favorable properties for distributed memory computers as all communication is gathered together in one step. The algorithm has been implemented on the Fujitsu VP2200 using the basic subroutines for fast Fourier transforms discussed elsewhere. We develop the theory of index digit permutations to some extent. With this theory we can derive the splitting formulas for almost all mixed-radix FFT algorithms known so far. This framework enables us to prove these algorithms but also to derive our new algorithm. The development and systematic use of this framework is new and allows us to simplify the proofs which are now reduced to the application of matrix recursions. Received October 29, 1992 / Revised version received October 21, 1993     

数据流的非平稳性度量——纪念李国平院士吴新谋教授诞辰100周年

该文融合遍历论、粗粒化方法和信息论的观点研究数据流的非平稳性度量问题. 引入了数据流的非平稳性度量的概念, 给出了数据流非平稳性度量的有效的近似算法. 数据流的非平稳性度量为该文融合遍历论、粗粒化方法和信息论的观点研究数据流的非平稳性度量问题. 引入了数据流的非平稳性度量的概念, 给出了数据流非平稳性度量的有效的近似算法. 数据流的非平稳性度量为该文融合遍历论、粗粒化方法和信息论的观点研究数据流的非平稳性度量问题. 引入了数据流的非平稳性度量的概念, 给出了数据流非平稳性度量的有效的近似算法. 数据流的非平稳性度量为$0$和$1$之间的实数,平稳性较好的数据流的非平稳性度量较小. 作者将数据流的非平稳性度量应用到模型选择问题中,提出残差序列非平稳性度量最小化的模型选择标准. 作者用数值试验检验了该文提出的数据流非平稳性度量的近似算法, 并检验了其作为模型选择标准的能力.数值试验的结果表明, 非平稳性度量是衡量数据流非平稳程度的一个合理指标, 可以很好地区分趋势平稳数据和差分平稳数据, 区分独立同分布序列、白噪声序列和鞅差序列.     

在线Logistic回归

针对连续数据流分类问题,基于在线学习理论,提出一种在线logistic回归算法.研究带有正则项的在线logistic回归,提出了在线logistic-l2回归模型,并给出了理论界估计.最终实验结果表明,随着在线迭代次数的增加,提出的模型与算法能够达到离线预测的分类结果.本文工作为处理海量流数据分类问题提供了一种新的有效方法.     

Randomized LU decomposition

Randomized algorithms play a central role in low rank approximations of large matrices. In this paper, the scheme of the randomized SVD is extended to a randomized LU algorithm. Several error bounds are introduced, that are based on recent results from random matrix theory related to subgaussian matrices. The bounds also improve the existing bounds of already known randomized SVD algorithm. The algorithm is fully parallelized and thus can utilize efficiently GPUs without any CPU–GPU data transfer. Numerical examples, which illustrate the performance of the algorithm and compare it to other decomposition methods, are presented.     

Discretization algorithm for fractional order integral by Haar wavelet approximation

A discretization algorithm is proposed by Haar wavelet approximation theory for the fractional order integral. In this paper, the integration time is divided into two parts, one presents the effect of the past sampled data, calculated by the iterative method, and the other presents the effect of the recent sampled data at a fixed time interval, calculated by the Haar wavelet. This method can reduce the amount of the stored data effectively and be applied to the design of discrete-time fractional order PID controllers. Finally, several numerical examples and simulation results are given to illustrate the validity of this discretization algorithm.