求分块三对角矩阵和分块周期三对角矩阵逆矩阵的快速算法

给出了分块三对角矩阵逆矩阵的快速算法,并利用所给算法得到了求分块周期三对角矩阵逆矩阵的快速算法.最后通过算例表示算法的有效性.     

周期三对角矩阵逆的一种新算法

给出了一类周期三对角矩阵逆的新的递归算法.新方法充分利用周期三对角矩阵的结构特点,采用递归方法将高阶周期三对角矩阵求逆转化为低阶周期三对角矩阵的求逆.并同时得到简化的计算方法,方法可以有效地减少运算量和存储量,计算精度也有明显的优势.数值实验表明此算法是有效的.     

三对角矩阵求逆的算法

研究了一般的非奇三对角矩阵的求逆,并给出了一个求逆矩阵的简单算法.首先研究了具有Doolittle分解的三对角矩阵的求逆,得到一个求逆的算法,然后将该算法推广到一般的非奇三对角矩阵上.最后给出了该算法与其它求逆方法的比较,可以看到该算法一方面计算量低,另一方面适用于不需任何附加条件的一般的非奇三对角矩阵.     

三对角与五对角Toeplitz矩阵求逆的算法

提出了一种求三对角与五对角Toeplitz矩阵逆的快速算法,其思想为先将Toeplitz矩阵扩展为循环矩阵,再快速求循环矩阵的逆,进而运用恰当矩阵分块求原Toeplitz矩阵的逆的算法.算法稳定性较好且复杂度较低.数值例子显示了算法的有效性和稳定性,并指出了算法的适用范围.     

计算周期三对角矩阵行列式和逆矩阵的新算法

给出了一种计算周期三对角矩阵行列式和逆矩阵的新递推算法,它们的运算复杂度分别为O(n)和O(n2),该算法是文献[5]和[6]中相关算法的拓广.     

局部环上幂等矩阵线性组合广义逆之间的关系

设R是2为单位的局部环.研究了R上三个两两可换的n阶非零幂等矩阵的线性组合广义逆之间的包含关系,确定了R上一类特殊矩阵广义逆的列表算法.利用这种列表算法和相关的矩阵理论,得到了这些矩阵线性组合广义逆之间的包含关系的充要条件,推广了矩阵自反广义逆的逆反律的相关结果.     

几种约束广义逆矩阵的有限算法

1引言与引理众所周知，关于非奇异方阵的正则逆的有限算法是由Faddeev大给在1949年之前提出的，这就是著名的Faddeev算法[1，P…334-336]。自从五十年代中期广义逆矩阵的研究复兴与发展以来，有不少学者提出了关于广义逆矩阵的有限算法。第一个给出关于广义逆矩     

非负不可约矩阵的广义Perron补矩阵

1989年Meyor为计算马尔可夫链的平稳分布向量构造了一个算法，首次提出非负不可约矩阵的Perron补矩阵的概念，本给出非负不可约矩阵A的广义Perron补矩阵若干性质，并且证明若矩阵A是不可约逆M-矩阵，其广义Perron补矩阵也是不可约逆M-矩阵。     

基于线性算子的广义逆矩阵的几何表示

广义逆矩阵是矩阵理论的重要内容.由于广义逆矩阵的定义众多,计算较为繁杂,使得初学者很难理解和掌握其本质.基于线性方程组求解问题的等价表示,从线性算子的角度展示多种广义逆矩阵定义的背景及其几何直观意义.通过对一个特殊算例的分析与求解,实现了对多种广义逆矩阵的几何解释及其在线性方程组求解中的作用,淡化了广义逆矩阵计算的繁杂,加深初学者对广义逆矩阵的理解与掌握.     

基于线性算子的广义逆矩阵的几何表示北大核心

广义逆矩阵是矩阵理论的重要内容.由于广义逆矩阵的定义众多,计算较为繁杂,使得初学者很难理解和掌握其本质.基于线性方程组求解问题的等价表示,从线性算子的角度展示多种广义逆矩阵定义的背景及其几何直观意义.通过对一个特殊算例的分析与求解,实现了对多种广义逆矩阵的几何解释及其在线性方程组求解中的作用,淡化了广义逆矩阵计算的繁杂,加深初学者对广义逆矩阵的理解与掌握.     

A normalized algorithm for the solution of sparse symmetric seven-diagonal linear systems of semi-bandwidths M and P

In a recent paper [3] normalized factorization procedures for the solution of self-adjoint elliptic partial differential equations in three space dimensions are introduced. In this procedure the coefficient matrix derived from the finite difference discretization of p.d.e.'s is factorized exactly to yield a normalized direct method of solution. The numerical implementation of the algorithm is presented in this paper and FORTRAN subroutines for the efficient solution of the resulting large sparse symmetric seven-diagonal systems are given.     

An Approach to Improving Consistency of Fuzzy Preference Matrix

Based on the transfer formulas of fuzzy preference matrix and multiplicative preference matrix, this paper presents an approach to improving consistency of fuzzy preference matrix and gives a practical iterative algorithm to derive a modified fuzzy preference matrix with acceptable consistency. By using two criteria, we can judge whether the modification is acceptable or not. Finally, a numerical example is given to show the feasibility and effectiveness of the algorithm.     

Iterative solutions to the extended Sylvester-conjugate matrix equations

This paper is concerned with iterative solutions to a class of complex matrix equations. By applying the hierarchical identification principle, an iterative algorithm is constructed to solve this class of complex matrix equations. The range of the convergence factor is given to guarantee that the proposed algorithm is convergent for arbitrary initial matrix by applying a real representation of a complex matrix as a tool. By using some properties of the real representation, a sufficient convergence condition that is easier to compute is also given by original coefficient matrices. Two numerical examples are given to illustrate the effectiveness of the proposed methods.     

具有高消失矩的3-进双正交对称小波的构造

本文用矩阵对称扩充来构造了具有高消失矩的3带对称双正交小波.利用矩阵扩充,获得了3维矩阵对称扩充方法和小波构造的算法,并且,该算法便于计算机程序化实现;利用两个实例验证了相关的结论.     

Iterative algorithms for solving a class of complex conjugate and transpose matrix equations

This paper is concerned with iterative solutions to a class of complex matrix equations, which include some previously investigated matrix equations as special cases. By applying the hierarchical identification principle, an iterative algorithm is constructed to solve this class of matrix equations. A sufficient condition is presented to guarantee that the proposed algorithm is convergent for an arbitrary initial matrix with a real representation of a complex matrix as tools. By using some properties of the real representation, a convergence condition that is easier to compute is also given in terms of original coefficient matrices. A numerical example is employed to illustrate the effectiveness of the proposed methods.     

基于梯度的Sylvester共轭矩阵方程的迭代解

本文提出了一种基于梯度的Sylvester共轭矩阵方程的迭代算法.通过引入一个松弛参数和采用递阶辨识原理,构造一个迭代算法求解Sylvester矩阵方程.通过应用复矩阵的实数表达以及实数表示的一些性质,收敛性分析表明在一定假设条件下,对于任意初始值,迭代方法均收敛到精确解,数值算例也表明了所给方法的有效性.     

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.     

An alternating direction algorithm for matrix completion with nonnegative factors

This paper introduces an algorithm for the nonnegative matrix factorization-and-completion problem, which aims to find nonnegative low-rank matrices X and Y so that the product XY approximates a nonnegative data matrix M whose elements are partially known (to a certain accuracy). This problem aggregates two existing problems: (i) nonnegative matrix factorization where all entries of M are given, and (ii) low-rank matrix completion where nonnegativity is not required. By taking the advantages of both nonnegativity and low-rankness, one can generally obtain superior results than those of just using one of the two properties. We propose to solve the non-convex constrained least-squares problem using an algorithm based on the classical alternating direction augmented Lagrangian method. Preliminary convergence properties of the algorithm and numerical simulation results are presented. Compared to a recent algorithm for nonnegative matrix factorization, the proposed algorithm produces factorizations of similar quality using only about half of the matrix entries. On tasks of recovering incomplete grayscale and hyperspectral images, the proposed algorithm yields overall better qualities than those produced by two recent matrix-completion algorithms that do not exploit nonnegativity.     

求解Sylvester矩阵方程的一种改进的梯度方法

提出了一种改进的梯度迭代算法来求解Sylvester矩阵方程和Lyapunov矩阵方程.该梯度算法是通过构造一种特殊的矩阵分裂,综合利用Jaucobi迭代算法和梯度迭代算法的求解思路.与已知的梯度算法相比,提高了算法的迭代效率.同时研究了该算法在满足初始条件下的收敛性.数值算例验证了该算法的有效性.