广义行(列)酉对称矩阵的Moore-penrose逆

通过对母矩阵进行奇异值分解的方法得到广义行(列)酉对称矩阵的奇异值分解进一步得到其Moore-penrose逆;用谱分解方法得到母矩阵的Moore-penrose逆,进一步得到广义行(列)酉对称矩阵的Moore-penrose逆.     

O-对称矩阵的奇异值分解及其算法

本文研究了具有轴对称结构矩阵的奇异值分解,找出了这类矩阵奇异值分解与其子阵奇异值分解之间的定量关系.利用这些定量关系给出这类矩阵奇异值分解和Moore-Penrose逆的算法,据此可极大地节省求该类矩阵奇异值分解和Moore-Penrose逆时的计算量和存储量.     

行(列)反对称矩阵的满秩分解和广义逆

本文研究了行(列)转置矩阵与行(列)反对称矩阵的性质.利用分块矩阵理论获得了许多新的结果,给出了行(列)反对称矩阵的满秩分解、秩分解和广义逆的公式及快速算法.它们可极大地减少行(列)反对称矩阵的满秩分解、秩分解和广义逆的计算量与存储量,并且不会丧失数值精度.     

矩阵的满秩正交分解

旨在给出矩阵一种新分解(满秩正交分解).分解简单易求,且与矩阵的奇异值分解有类似的性质和应用.     

酉对称矩阵的极分解

为了简化大型行(列)酉对称矩阵的极分解,研究了酉对称矩阵的性质,获得了一些新的结果,给出了酉对称矩阵的极分解和广义逆的公式,它们可极大地减少行(列)酉对称矩阵的极分解的计算量与存储量,并且不会丧失数值精度.同时对酉对称矩阵的极分解作了扰动分析.     

Toeplitz型矩阵的逆矩阵的快速三角分解算法

针对有关“型”矩阵的三角分解问题 ,提出了一种 Toeplitz型矩阵的逆矩阵的快速三角分解算法 .首先假设给定 n阶非奇异矩阵 A,利用一组线性方程组的解 ,得到 A- 1的一个递推关系式 ,进而利用该关系式得到 A- 1的一种三角分解表达式 ,然后从 Toeplitz型矩阵的特殊结构出发 ,利用上述定理的结论 ,给出了Toeplitz型矩阵的逆矩阵的一种快速三角分解算法 ,算法所需运算量为 O( mn2 ) .最后 ,数值计算表明该算法的可靠性 .     

Broyden修正算法

对于求解非线性方程组F (x) =0的Broyden秩1方法的计算格式提出一种修正算法,尝试利用矩阵的奇异值分解求解迭代方程组,并且配合使用加速技巧,从而大大提高了算法的安全性和收敛速度.数值算例表明了新算法的有效性.     

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.     

矩阵的Γ逆

利用矩阵的广义奇异值分解,给出了复数域上矩阵的Γ逆存在的充要条件及其表达式,并讨论了Γ逆的唯-性.     

用随机奇异值分解算法求解矩阵恢复问题

本文研究了大型低秩矩阵恢复问题.利用随机奇异值分解（RSVD）算法,对稀疏矩阵做奇异值分解.该算法与Lanczos方法相比,在误差精度一致的同时运算时间大大降低,且该算法对相对低秩矩阵也有效.     

Robust tensor completion using transformed tensor singular value decomposition

In this article, we study robust tensor completion by using transformed tensor singular value decomposition (SVD), which employs unitary transform matrices instead of discrete Fourier transform matrix that is used in the traditional tensor SVD. The main motivation is that a lower tubal rank tensor can be obtained by using other unitary transform matrices than that by using discrete Fourier transform matrix. This would be more effective for robust tensor completion. Experimental results for hyperspectral, video and face datasets have shown that the recovery performance for the robust tensor completion problem by using transformed tensor SVD is better in peak signal‐to‐noise ratio than that by using Fourier transform and other robust tensor completion methods.     

Simultaneous singular value decomposition

We consider the following problem: Given a set of m×n real (or complex) matrices A₁,…,A_N, find an m×m orthogonal (or unitary) matrix P and an n×n orthogonal (or unitary) matrix Q such that P^*A₁Q,…,P^*A_NQ are in a common block-diagonal form with possibly rectangular diagonal blocks. We call this the simultaneous singular value decomposition (simultaneous SVD). The name is motivated by the fact that the special case with N=1, where a single matrix is given, reduces to the ordinary SVD. With the aid of the theory of *-algebra and bimodule it is shown that a finest simultaneous SVD is uniquely determined. An algorithm is proposed for finding the finest simultaneous SVD on the basis of recent algorithms of Murota-Kanno-Kojima-Kojima and Maehara-Murota for simultaneous block-diagonalization of square matrices under orthogonal (or unitary) similarity.     

矩阵方程ATXB+BTXTA=D的极小范数最小二乘解

1引言本文用Rm×n表示所有m×n实矩阵全体,ORn×n,ASRn×n分别表示n×n实正交矩阵类与反对称矩阵类.‖·‖F表示矩阵的Frobenius范数,A+为矩阵A的Moore-Penrose广义逆,A*B与A(?)B分别表示矩阵4与B的Hadamard乘积及Kronecker乘积,即若A=(aij),B=(bij),则A*B=(ajibij),A(?)B=(aijB),vec4表示矩阵A的按行拉直,即若A=[aT1,aT2,…,aTm],其中ai为A的行向量,则vecA=(a1a2…am)T.设A∈Rn×m,B∈Rp×m,D∈Rm×m,我们考虑不相容线性矩阵方程ATXB+BTXTA=D(1.1)     

Computing the complete CS decomposition

An algorithm for computing the complete CS decomposition of a partitioned unitary matrix is developed. Although the existence of the CS decomposition (CSD) has been recognized since 1977, prior algorithms compute only a reduced version. This reduced version, which might be called a 2-by-1 CSD, is equivalent to two simultaneous singular value decompositions. The algorithm presented in this article computes the complete 2-by-2 CSD, which requires the simultaneous diagonalization of all four blocks of a unitary matrix partitioned into a 2-by-2 block structure. The algorithm appears to be the only fully specified algorithm available. The computation occurs in two phases. In the first phase, the unitary matrix is reduced to bidiagonal block form, as described by Sutton and Edelman. In the second phase, the blocks are simultaneously diagonalized using techniques from bidiagonal SVD algorithms of Golub, Kahan, Reinsch, and Demmel. The algorithm has a number of desirable numerical features.      

（m，n）型二重（r1,r2）-循环矩阵的逆和广义逆

利用矩阵的Kronecker积给出了非奇异的（m,n）型二重（r1,r2）－循环矩阵求逆矩阵的一个计算公式,同时该方法还可以推广到求奇异的（m,n）型二重（r1,r2）－循环矩阵的反射g逆。     

对角占优矩阵奇异-非奇异的充分必要判据

本文研究对角占优矩阵奇异-非奇异的充分必要条件.基于Taussky定理,本文得出,可约对角占优矩阵的奇异性由其独立Frobenius块的奇异性决定,从而将这一问题化为不可约对角占优矩阵的奇异-非奇异性问题;运用Taussky定理研究奇异不可约对角占优矩阵的相似性和酉相似性,获得这类矩阵元素辐角间的关系;并与Taussky定理给出的这类矩阵元素模之间的关系结合在一起,研究不可约对角占优矩阵奇异的充分必要条件;最后给出不可约对角占优矩阵奇异-非奇异性的判定方法.     

矩阵方程AXAT+BYBT=C的对称与反对称最小范数最小二乘解

对于任意给定的矩阵A∈Rk×m,B∈Rk×n和C∈Rk×k,利用奇异值分解和广义奇异值分解,我们给出了矩阵方程AXAT+BYBT=C的对称与反对称最小范数最小二乘解的表达式.     

On some methods for performance ranking and correspondence analysis in the DEA context

Two novel methods named performance baseline and performance correspondence matrices are proposed to evaluate the performance of decision making units (DMUs) based on the techniques of singular value decomposition (SVD). The performance baseline matrix can be used to rank all the DMUs because it provides a common basis for performance comparison. The performance correspondence matrix can be used to conduct performance cluster analysis, with which to explore the structure of input/output variables that are associated with DMUs. The analysis can reveal the performance difference of the DMUs and the key input/output variables determining the efficiency of a certain DMU, and provides valuable quantitative information for adjusting variables to improve efficiency of the DMU. Three case studies are presented to demonstrate that the proposed methods in this work are effective and easy to use and can provide insights into proper selection of input/output variables for performance comparison to avoid over manipulating DEA models in practice.