关于Haar 酉元的一个注记

设M 是一个Ⅱ₁ 型因子, τ 是M 的正规的、忠实的迹态, U ∈ M 是一个Haar 酉元, p ∈ M是一个投影, τ (p) = (1/n) (n > 3, n ∈ Z), p 和U 自由. 我们用初等方法证明了若pUp = wh 是pUp 的极分解, 则w 也是一个Haar 酉元且w 和h 是自由的. 我们还给出了pUp 的矩的刻画.     

一个矩阵分解算法的推广及应用

对样本相关系数矩阵等行和分解算法作了简化和推广,使算法不仅可以应用在基于正态总体非独立样本的假设检验问题,也可以有效地运用在最优化算法中牛顿法等与二次函数极小化有关的问题上.     

矩阵奇异值分解及其在高维数据处理中的应用

矩阵奇异值分解能够实现对高维数据的局部特征提取及维数约减,在智能信息处理和模式识别研究领域具有十分重要的应用价值.首先分析了高维数据处理所面临的困境,并对常用的降维算法进行简单的归纳总结;然后阐述了矩阵奇异值分解的基本原理及其在维数约减和数据压缩中的物理意义;接着通过分析两种建立在奇异值分解基础上的PCA与LSA降维算法的数学导出过程,进一步给出了两者的等价性证明;最后总结了矩阵奇异值分解的优缺点,并且预测了高维数据处理技术未来的发展趋势.     

Locally connected models for Julia sets

Let P be a polynomial with a connected Julia set J. We use continuum theory to show that it admits a finest monotone map φ onto a locally connected continuumJP_∼, i.e. a monotone map φ:J→JP_∼ such that for any other monotone map ψ:J→J^′ there exists a monotone map h with ψ=h°φ. Then we extend φ onto the complex plane C (keeping the same notation) and show that φ monotonically semiconjugates PC_| to a topological polynomialg:C→C. If P does not have Siegel or Cremer periodic points this gives an alternative proof of Kiwi's fundamental results on locally connected models of dynamics on the Julia sets, but the results hold for all polynomials with connected Julia sets. We also give a characterization and a useful sufficient condition for the map φ not to collapse all of J into a point.     

APPLICATION OF TWO OPERATORS TRANSFORM FROM CLASS A OPERATOR TO THE CLASS OF HYPONORMAL OPERATOR

In this article, we give an operator transform T (*) from class A operator to the class of hyponormal operators. It is different from the operator transform T defined by M. Ch and T. Yamazaki. Then, we show that σ(T ) = σ( T (*)) and σa(T )\{0} = σa( T (*))\{0}, in case T belongs to class A. Next, we obtain some relations between T and T (9).     

N(2,2,0)代数的稳定化子与同余分解

为了深入研究N(2,2,0)代数的代数结构,首先利用代数的推理方法进一步研究了N(2,2,0)代数的稳定化子,改进了以前学者的部分结果,然后利用稳定化子建立了一种同余关系,给出了N(2,2,0)代数的同余分解,证明了其商代数仍是N(2,2,0)代数,并获得了自然同态下一类逆像的代数结构和性质.     

An alternating projected gradient algorithm for nonnegative matrix factorization

Due to the extensive applications of nonnegative matrix factorizations (NMFs) of nonnegative matrices, such as in image processing, text mining, spectral data analysis, speech processing, etc., algorithms for NMF have been studied for years. In this paper, we propose a new algorithm for NMF, which is based on an alternating projected gradient (APG) approach. In particular, no zero entries appear in denominators in our algorithm which implies no breakdown occurs, and even if some zero entries appear in numerators new updates can always be improved in our algorithm. It is shown that the effect of our algorithm is better than that of Lee and Seung’s algorithm when we do numerical experiments on two known facial databases and one iris database.     

Prediction error sampling procedure based on dominant Schur decomposition. Application to state estimation in high dimensional oceanic model

Iterative procedure is described to generate patterns of dominant Schur vectors of the system dynamics. Their roles in estimating the filter gain is study. These patterns are produced by several integrations of the model from a set of perturbations. This approach is motivated by a number of interesting results on stability of the filter whose gain is approximated in a subspace of dominant Schur vectors. A simple method for the filter design is presented which is aimed at overcoming the most serious drawback of advanced filtering algorithms for high dimensional systems related to very high computational cost in evaluation of the filter gain.The resulting filter will be compared with the existing ones, showing its relevance from a practical point of view. In order to demonstrate its efficiency, the new filter is tested on various experiments. These experiments include the much studied problem of estimating the solution of the Lorenz system as well as that of assimilating sea surface height observations in a high dimensional oceanic model. It is shown that significant increases in efficiency can be obtained by using this filter and that the proposed filter is very promising for solving realistic assimilation problems in meteorology and oceanography.     

Parallel characteristic finite element method for time‐dependent convection–diffusion problem

Based on the overlapping‐domain decomposition and parallel subspace correction method, a new parallel algorithm is established for solving time‐dependent convection–diffusion problem with characteristic finite element scheme. The algorithm is fully parallel. We analyze the convergence of this algorithm, and study the dependence of the convergent rate on the spacial mesh size, time increment, iteration times and sub‐domains overlapping degree. Both theoretical analysis and numerical results suggest that only one or two iterations are needed to reach to optimal accuracy at each time step. Copyright © 2010 John Wiley & Sons, Ltd.