矩阵奇异值分解问题重分析的摄动法

本文提出了一般实矩阵奇异值分解问题重分析的摄动法.这是一种简捷、高效的快速重分析方法,对于提高各种需要反复进行矩阵奇异值分解的迭代分析问题的计算效率具有较重要的实用价值.文中导出了奇异值和左、右奇异向量的直到二阶摄动量的渐近估计算式.文末指出了将这种振动分析方法直接推广到一般复矩阵情况的途径.     

线性流形上双反对称矩阵的最佳逼近

本文讨论了线性流形上用双反对称矩阵构造给定矩阵的最佳逼近问题,给出问题解的表达式,最后给出求最佳逼近解的数值方法与数值算例.     

A comparison between the complex symmetric based and classical computation of the singular value decomposition of normal matrices

An algorithm for computing the singular value decomposition of normal matrices using intermediate complex symmetric matrices is proposed. This algorithm, as most eigenvalue and singular value algorithms, consists of two steps. It is based on combining the unitarily equivalence of normal matrices to complex symmetric tridiagonal form with the symmetric singular value decomposition of complex symmetric matrices. Numerical experiments are included comparing several algorithms, with respect to speed and accuracy, for computing the symmetric singular value decomposition (also known as the Takagi factorization). Next we compare the novel approach with the classical Golub-Kahan method for computing the singular value decomposition of normal matrices: it is faster, consumes less memory, but on the other hand the results are significantly less accurate.     

On Parallel Fast Jacobi Algorithm for the Eigenproblem of Real Symmetric Matrices

Jacobi algorithm has been developed for the eigenproblem of real symmetric matrices, singular value decomposition of matrices and least squares of the overdetermined system on a parallel computer. In this paper, the parallel schemes and fast algorithm are discussed, and the error analysis and a new bound are presented.     

The structured total least‐squares approach for non‐linearly structured matrices

In this paper, an extension of the structured total least‐squares (STLS) approach for non‐linearly structured matrices is presented in the so‐called ‘Riemannian singular value decomposition’ (RiSVD) framework. It is shown that this type of STLS problem can be solved by solving a set of Riemannian SVD equations. For small perturbations the problem can be reformulated into finding the smallest singular value and the corresponding right singular vector of this Riemannian SVD. A heuristic algorithm is proposed. Some examples of Vandermonde‐type matrices are used to demonstrate the improved accuracy of the obtained parameter estimator when compared to other methods such as least squares (LS) or total least squares (TLS). Copyright © 2002 John Wiley & Sons, Ltd.     

To solving problems of algebra for two-parameter matrices. VII

The paper discusses the method of rank factorization for solving spectral problems for two-parameter polynomial matrices. New forms of rank factorization, which are computed using unimodular matrices only, are suggested. Applications of these factorizations to solving spectral problems for two-parameter polynomial matrices of both general and special forms are presented. In particular, matrices free of the singular spectrum are considered. Conditions sufficient for a matrix to be free of the singular spectrum and also conditions sufficient for a basis matrix of the null-space to have neither the finite regular nor the finite singular spectrum are provided. Bibliography: 3 titles.     

Hermitian自反矩阵反问题的最小二乘解

本文研究了Hermitian自反矩阵反问题的最小二乘解及其最佳逼近.利用矩阵的奇异值分解理论,获得了最小二乘解的表达式.同时对于最小二乘解的解集合,得到了最佳逼近解.     

矩阵方程AXB=C最小二乘D反对称解

首先将对称矩阵推广到D反对称矩阵,然后研究了方程AXB=C的D反对称最小二乘解,利用矩阵对的广义奇异分解、标准相关分解及子空间上的投影定理,得到了最小二乘解的通式．     

带ARMA有色噪声的广义系统信息融合Kalman平滑器

对于带自回归滑动平均(ARMA)有色观测噪声的多传感器广义离散随机线性系统,应用奇异值分解,提出了广义系统多传感器信息融合状态平滑问题。基于Kalman滤波方法,在线性最小方差信息融合准则下,给出了按矩阵加权融合降阶稳态广义Kalman平滑器。为了计算最优加权,提出了局部平滑误差协方差阵的计算公式。一个Monte Carlo仿真例子说明了所提方法的有效性。     

Lanczos method for large-scale quaternion singular value decomposition

In many color image processing and recognition applications, one of the most important targets is to compute the optimal low-rank approximations to color images, which can be reconstructed with a small number of dominant singular value decomposition (SVD) triplets of quaternion matrices. All existing methods are designed to compute all SVD triplets of quaternion matrices at first and then to select the necessary dominant ones for reconstruction. This way costs quite a lot of operational flops and CPU times to compute many superfluous SVD triplets. In this paper, we propose a Lanczos-based method of computing partial (several dominant) SVD triplets of the large-scale quaternion matrices. The partial bidiagonalization of large-scale quaternion matrices is derived by using the Lanczos iteration, and the reorthogonalization and thick-restart techniques are also utilized in the implementation. An algorithm is presented to compute the partial quaternion singular value decomposition. Numerical examples, including principal component analysis, color face recognition, video compression and color image completion, illustrate that the performance of the developed Lanczos-based method for low-rank quaternion approximation is better than that of the state-of-the-art methods.