概念的EI代数表示与布尔矩阵表示的研究

在AFS理论的基础上,用AFS方法给出了EI代数与布尔矩阵环之间的一个同态关系,并证明了与每个布尔矩阵对应的所有概念在EI代数上形成一个子代数.找到了子代数的一些性质和研究子代数的新方法.应用这些新方法和子代数的性质可以深入研究概念的数学本质.     

利用特征矩阵求实对称矩阵的特征向量

本文利用Hamilton-Cayley定理和特征矩阵的性质,给出了求实对称矩阵的特征向量的新方法,并通过例子验证了该方法.     

逆H矩阵的新性质

本文在文[4]给出的逆H矩阵定义的基础上，给出了逆H矩阵的新性质．     

反对称正交对称矩阵反问题

本文讨论一类反对称正交对称矩阵反问题及其最佳逼近．研究了这类矩阵的一些性质，利用这些性质给出了反问题解存在的一些条件和解的一般表达式，不仅证明了最佳逼近解的存在唯一性，而且给出了此解的具体表达式．     

格矩阵的行列式与伴随矩阵

研究了格矩阵的行列式与伴随矩阵,给出了它们的一些代数性质,同时给出了由一个格矩阵构造一个传递矩阵的方法.     

广义正定矩阵的进一步研究

基于正定矩阵的几个定义,首先给出了广义正定矩阵的一些新性质,其次研究了广义正定矩阵与H-矩阵、M-矩阵的关系,推广和改进了文献中的有关行列式不等式.     

正规矩阵的方程构造

通过对复数域上的群逆矩阵、Moore-Penrose (简称MP)可逆矩阵和共轭转置矩阵的研究,给出矩阵range-Hermitian (简称EP)性的一些新刻画,借助于这些性质刻画,构造出相关的矩阵方程,研究所得方程在给定集合中解的存在性对矩阵EP性的影响,从而给出正规矩阵的刻画.利用这些刻画再反过来构造矩阵方程,研究其一般解形式,通过变化其特解形式给出矩阵EP性的若干新性质.     

关于形式三角矩阵环

本文研究形式三角矩阵环 R 的若干新性质,讨论 R-模的伪投射性,给出了形式三角矩阵环 R 是 V-环或半 V-环的充要条件．同时,给出了 R 是 PS-环的条件．     

一类Z-矩阵的研究

称Ａ是有性质Ｐ的Ｚ－矩阵．如果Ａ是Ｚ－矩体且Ａ的每个真主子式是正的．该类矩阵包含了几个著名的矩阵类．本文主要刻画了有性质Ｐ的Ｚ－矩阵，改进了［１，４］的结果，并给出了若干著名矩阵类的一些公共特性．     

M-矩阵Sylvester方程的一类Smith-like迭代法（英文）

本文研究了M-矩阵Sylvester方程的数值解法,这类矩阵方程广泛出现在科学计算和工程应用的许多领域.利用M-矩阵的性质和Smith方法的思想,提出了一类Smith-like迭代法以求解M-矩阵Sylvester方程,并给出了新方法的收敛性分析.数值实验表明,新方法是可行的,而且在一定条件下也是较为有效的.     

A New Class AOR Preconditioner for $L$-Matrices

Hadjidimos(1978) proposed a classical accelerated overrelaxation(AOR) iterative method to solve the system of linear equations, and discussed its convergence under the conditions that the coefficient matrices are irreducible diagonal dominant, L-matrices, and consistently orders matrices. Several preconditioned AOR methods have been proposed to solve system of linear equations Ax = b, where A ∈ R~(n×n) is an L-matrix. In this work, we introduce a new class preconditioners for solving linear systems and give a comparison result and some convergence result for this class of preconditioners. Numerical results for corresponding preconditioned GMRES methods are given to illustrate the theoretical results.     

求解广义鞍点问题的混合并行迭代法(英文)

为了在高性能计算机上求解广义鞍点问题,对于合适的系数矩阵,本文提出混合并行迭代法及其加速形式.并详细讨论了新方法的收敛性.     

一类非线性方程组的Newton-PSS迭代法

正定反Hermite分裂(PSS)方法是求解大型稀疏非Hermite正定线性代数方程组的一类无条件收敛的迭代算法.将其作为不精确Newton方法的内迭代求解器,我们构造了一类用于求解大型稀疏且具有非Hermite正定Jacobi矩阵的非线性方程组的不精确Newton-PSS方法,并对方法的局部收敛性和半局部收敛性进行了详细的分析.数值结果验证了该方法的可行性与有效性.     

Iterative solutions to the Kalman-Yakubovich-conjugate matrix equation

Two operations are introduced for complex matrices. In terms of these two operations an infinite series expression is obtained for the unique solution of the Kalman-Yakubovich-conjugate matrix equation. Based on the obtained explicit solution, some iterative algorithms are given for solving this class of matrix equations. Convergence properties of the proposed algorithms are also analyzed by using some properties of the proposed operations for complex matrices.     

一类新的曲线搜索下的多步下降算法

提出一类新的曲线搜索下的多步下降算法,在较弱条件下证明了算法具有全局收敛性和线性收敛速率.算法利用前面多步迭代点的信息和曲线搜索技巧产生新的迭代点,收敛稳定,不用计算和存储矩阵,适于求解大规模优化问题.数值试验表明算法是有效的.     

关于Toeplitz+Hankel线性方程组的迭代解法

本文研究Toeplitz+Hankel线性方程组的预处理迭代解法.我们提出了几个新的预条件子,并分析了预处理矩阵的谱性质,当生成函数在Wiener类中时,预处理矩阵的特征值聚集在1附近.数值实验表明该预处理子比文[5]中的预处理子更有效.     

Solutions of symmetry‐constrained least‐squares problems

In this paper, two new matrix‐form iterative methods are presented to solve the least‐squares problem: and matrix nearness problem: where matrices and are given; ??₁ and ??₂ are the set of constraint matrices, such as symmetric, skew symmetric, bisymmetric and centrosymmetric matrices sets and S_XY is the solution pair set of the minimum residual problem. These new matrix‐form iterative methods have also faster convergence rate and higher accuracy than the matrix‐form iterative methods proposed by Peng and Peng (Numer. Linear Algebra Appl. 2006; 13 : 473–485) for solving the linear matrix equation AXB+CYD=E. Paige's algorithms, which are based on the bidiagonalization procedure of Golub and Kahan, are used as the framework for deriving these new matrix‐form iterative methods. Some numerical examples illustrate the efficiency of the new matrix‐form iterative methods. Copyright © 2008 John Wiley & Sons, Ltd.     

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.     

A new version of the Smith method for solving Sylvester equation and discrete-time Sylvester equation

Recently, Xue etc. \cite{28} discussed the Smith method for solving Sylvester equation $AX+XB=C$, where one of the matrices $A$ and $B$ is at least a nonsingular $M$-matrix and the other is an (singular or nonsingular) $M$-matrix. Furthermore, in order to find the minimal non-negative solution of a certain class of non-symmetric algebraic Riccati equations, Gao and Bai \cite{gao-2010} considered a doubling iteration scheme to inexactly solve the Sylvester equations. This paper discusses the iterative error of the standard Smith method used in \cite{gao-2010} and presents the prior estimations of the accurate solution $X$ for the Sylvester equation. Furthermore, we give a new version of the Smith method for solving discrete-time Sylvester equation or Stein equation $AXB+X=C$, while the new version of the Smith method can also be used to solve Sylvester equation $AX+XB=C$, where both $A$ and $B$ are positive definite. % matrices. We also study the convergence rate of the new Smith method. At last, numerical examples are given to illustrate the effectiveness of our methods     

关于解线性系统的一个有效的贝叶斯迭代方法

This paper concerns with the statistical methods for solving general linear systems. After a brief review of Bayesian perspective for inverse problems,a new and efficient iterative method for general linear systems from a Bayesian perspective is proposed.The convergence of this iterative method is proved,and the corresponding error analysis is studied.Finally, numerical experiments are given to support the efficiency of this iterative method,and some conclusions are obtained.