代数特征值反问题之解的稳定性分析

考虑如下代数特征值反问题: 问题 G(A;{A_k}_1~n;λ).设 A=(a_(ij)),A_k=(a_(ij)~((k))),k=1,…,n是n+1个n×n的实对称矩阵,λ=(λ_1,…,λ_n)是n维实向量且λ_i≠λ_j,i≠j.求n维实向量c=(c_1,…,c_n)~T,使矩阵A(c)=A+sum from k=1 to n (c_kA_k)的特征值是λ_1,…,λ_n. 这一问题是经典加法问题的推广.当A_k-e_ke_k~~T(e_k是n阶单位阵的第k列)时,     

对称次反对称矩阵的一类反问题

1 引言 用R~(m×n),SR~(n×n),ASR~(n×n),OR~(n×n)分别表示所有m×n实矩阵,n阶实对称矩阵,n阶实反对称矩阵和n阶实正交矩阵组成的集合,I_k表示k阶单位矩阵,S_k表示k阶反序单位矩阵,||A||表示矩阵A的Frobenius范数。若A=(a_(ij))∈R~(n×n),记D_A=diag(a_(11),a_(22),…,a_(nn)),L_A=(l_(ij))∈R_(n×n)其中当i>j时,l_(ij)=a_(ij),当i≤j时,l_(ij)=0,(i,j=1,2,…,n).若A=(a_(ij)),B=(b_(ij))∈R~(m×n),A*B表示A与B的Hadamard乘积,其定义为A*B=(a_(ij)b_(ij))。     

关于二次型教学的浅见

<正> 任给二次型f=sum from i,i=1 to n (a_(ij)x_ix_j(a_(ij)=a(j1)),总有正交变换X=PY,使f化成标准形:f=λ_1y_1~2=…=λ_ny_n~2,其中λ_1,…,λ_n是f的矩阵A=(a_(ij))的特征值。这里我们只在实数范围内进行讨论。用正交变换化二次型为标准形的问题,也就是用正交矩阵P化实对称矩阵A为对角矩阵A的问题。这历来是教学中的重点和难点。一则由于这方面的内容有着广泛的应用,因而     

布尔矩阵广义逆的若干判定定理

本文所论的矩阵均指 n 阶布尔方阵。A=(a_(ij)),B=(b_(ij)),若 a_(ij)≤b_(ij),i,j=1,2,…,n,则称 A≤B.对 A=(a_(ij)),若存在矩阵 G,使 AGA=A,称 G 是 A 的广义逆(g 逆),又令(?)称矩阵 A_0=(g_(ij))为 A 的相伴阵。A_0的转置阵为 A_0~T=(g_(ij)~T).     

加法与乘法逆特征值问题的可解性

1.引言 本文讨论如下代数特征值反问题可解的充分条件: 问题A(加法逆特征值问题)。给定一Hermite矩阵A=(a_(ij))_(n×n)及n个实数λ_1,…,λ_n,求一实对角阵D=diag(c_1…,c_n),使得A+D的特征值为λ_1,…,λ_n。 问题M(乘法逆特征值问题)。给定一正定Hermite矩阵A=(a_(ij))_(n×n)和n个正实数     

广义严格对角占优阵的判定程序

1 引言和符号 在本文中,均采用下列符号而不再重申.恒用N表示前n个自然数的集合;而用Mn(C)和Mn(R)分别表示所有n阶复矩阵和所有n阶实矩阵的集合. Z_N={A|A=(a_(ij))_(n×n)∈Mn(R),a_(ij)≤0,i,j∈N,i≠j},I恒表示单位矩阵. 如果A∈Mn(R)且A的所有元素都为非负实数,则称A为非负方阵,并记为A≥0;若A的所有元素都为正数,则称A为正矩阵,并记为A>0. 对A=(a_(ij))(n×n)∈Mn(C),令A_i(A)=sum from j=1 j≠i to n (|a_(ij)|(i=1、2…… n)) ;若把A的非零元用1代替 而得到—个n阶(0,1)矩阵。称为A的导出矩阵。记为;而把A的比较矩阵记为 u(A)=(b_(ij))_(n×n))其中b_(ij)=|a_(ij)|,b_(ij)=-|a_(ij)|(i,j∈N i≠j)     

代数特征值反问题可解的充分条件

本文讨论如下代数特征值反问题的可解性:问题G.设A=(a_(ij))和A_k=(a_(ij)~((k)))(k=1,…,n)是一组n+1个n×n实矩     

关于代数特征值反问题对称情况可解的充分条件

§1.引言 本文讨论下述特征值反问题的可解性: 问题 G.设A_0=(a_(ij)~((0)))和A_k=(a_(ij)~((k)))(k=1,…,n)是一组n+1个n×n实对称矩阵,λ_1,…,λ_n是n个不同的实数.求实数c_1,…,c_n使得矩阵A_0+sum from k-1 to n C_k·A_k的特征值为λ_1,…,λ_n. [1]和[2]曾给出此问题可解的充分条件.本文应用Rothe不动点定理[3]给出问题G可解的另外两个充分条件.本文的结果可判定[1]和[2]中定理所不能判定的某些问题     

求解逆特征值问题的牛顿类方法的收敛判据

<正>1引言多年来,众多数学工作者在推导和分析如下定义的逆特征值问题(IEP)的理论和算法上表现出了相当大的兴趣.以下我们设c=(c_1,c_2,….c_n)~T E R~n,{A_i}_(i=1)~n是n个实对称的n×n矩阵.定义A(c)=∑ni=1c_iA_i.(1)设A(c)的特征值为{λ_i(c)}_(i=1)~n且λ_1(c)≤λ_2(c)≤…≤λ_n(c).设{λ_i~*)_(i=1)~n为任意给定的n个数并且满足λ_1~*≤λ_2~*≤…≤λ_n~*.我们这里考虑的IEP就是寻找向量c~*∈R~n使得λ_i(c~*)=λ_i~*对任意的i=1,2,…,n.(2)     

Classification of Cartan Matrices of Hyperbolic Type

In the theory of finite dimensional semisimple Lie algebras,it is known thatthe Cartan matrix A=(a_(ij))_i~n, i=1 has the following properties: (1)a_(ii)=2,i=1,…,n; (2)a_(ij)≤0 for i≠j,a_(ij)∈Z; (3)a_(ij)=0 a_(ji)=0. Now if a matrix A=(a_(ij))_i~n,j\j=1 satisfies (1),(2),(3),then A is called     

具有奇点的Laplace方程边值问题的原始能量-有限元结合法

在用变分原理离散数理方程时,可取函数在部分求解区域内,取成解析函数或奇异函数类;而在其余求解区域内取成分片低阶插值多项式.在两区域交界的结点上,可取函数连续.我们称此法为原始能量一有限元结合法.[3,p135]中提到过这种方法,但缺少理论分析.[4]曾给出初步的分析.本文与[5,6]就此方法给出较严格的理论分析.     

On Numerical Methods for Robust Pole Assignment in Control System Design

It is known that selection of a well-conditioned set of vectors from given subspaces is the key step for solving the robust pole assignment problem. In this paper we suggest two numerical methods for selecting such set of vectors. The numericalmethods, Method (I) and Method (II), are described, and some numerical results are presented.     

Homotopy perturbation method for (2+1) -dimensional coupled Burgers system

In this paper, a numerical solution of the (2+1)-dimensional coupled Burgers system is studied by using the Homotopy Perturbation Method (HPM). For this purpose, the available analytical solutions obtained by tanh method will be compared to show the validity and accuracy of the proposed numerical algorithm. The results approve the convergence and accuracy of the Homotopy Perturbation Method for numerically analyzed (2+1) coupled Burgers system.     

带比例关系的实带状矩阵特征值反问题

研究了通过矩阵A的顺序主子矩阵A_((k))=(aij)_(i,j=1)^(n-k+1)的特征值{λ_i(n-k+1)的特征值{λ_i^((k)))}_(i=1)((k)))}_(i=1)^(n-k+1)k=1,2,…,r+1来构造一个带比例关系的实带状矩阵的特征值反问题.对当特征值{λ_i(n-k+1)k=1,2,…,r+1来构造一个带比例关系的实带状矩阵的特征值反问题.对当特征值{λ_i^((k))}_(i=1)((k))}_(i=1)^(n-k+1)中有多重特征值出现时,应当如何来构造这类矩阵进行了讨论,并给出了问题的具体算法及数值例子.     

Jacobi elliptic function solutions of the (1 + 1)‐dimensional dispersive long wave equation by Homotopy Perturbation Method

In this article, we try to obtain approximate Jacobi elliptic function solutions of the (1 + 1)‐dimensional long wave equation using Homotopy Perturbation Method. This method deforms a difficult problem into a simple problem which can be easily solved. In comparison with HPM, numerical methods leads to inaccurate results when the equation intensively depends on time, while He's method overcome the above shortcomings completely and can therefore be widely applicable in engineering. As a result, we obtain the approximate solution of the (1 + 1)‐dimensional long wave equation with initial conditions. © 2008 Wiley Periodicals, Inc. Numer Methods Partial Differential Eq 2008     

弦割法与Muller法收敛阶的新证明方法

弦割法、Muller法与牛顿法一样,都是求解非线性方程的著名算法之一.然而在目前众多优秀的数值分析教材或论著中.关于弦割法和Muller法收敛阶的证明过程都是比较复杂的,无一例外的都是借助于差分方程的求解.本文对这两个算法的收敛阶给出了一种新的简单、直接的证明方法,达到了与牛顿法收敛阶证明方法的统一,同时还能够方便地求...     

有界线性算子和的Drazin逆表示

本文讨论了两个有界线性算子和的Drazin可逆性及其表达式.在PQ~3=0,P~2Q=0,QPQ~2=0的条件下,采用预解式的Laurent展开方法,证明了P+Q是Drazin可逆的,并得到了P+Q的Drazin逆的表达式.同时,还确定出P+Q的指标的范围ind(P+Q)≤2t+r+s—1,给出数值算例说明结论的有效性.     

The Nearest Bisymmetric Solutions of Linear Matrix Equations

The necessary and sufficient conditions for the existence of and the expressions for the bisymmetric solutions of the matrix equations (Ⅰ)A1X1B1 A2X2B2 ^… AkXkBk=D,(Ⅱ)A1XB1 A2XB2 … AkXBk=D and (Ⅲ) (A1XB1,A2XB2,…,AkXBk)=(D1,D2,…,Dk) are derived by using Kronecker product and Moore-Penrose generalized inverse of matrices. In addition, in corresponding solution set of the matrix equations, the explicit expression of the nearest matrix to a given matrix in the Frobenius norm is given. Numerical methods and numerical experiments of finding the neaxest solutions axe also provided.     

向前型分段连续微分方程的数值解

本文讨论了向前型分段连续微分方程Euler-Maclaurin方法的收敛性和稳定性,给出了Euler-Maclaurin方法的稳定条件,证明了方法的收敛阶是2n+2,并且得到了数值解稳定区域包含解析解稳定区域的条件,最后给出了一些数值例子用以验证本文结论的正确性.     

A new model for sparse and low-rank matrix decomposition

The robust principal component analysis (RPCA) model is a popular method for solving problems with the nuclear norm and $\ell_1$ norm. However, it is time-consuming since in general one has to use the singular value decomposition in each iteration. In this paper, we introduce a novel model to reformulate the existed model by making use of low-rank matrix factorization to surrogate the nuclear norm for the sparse and low-rank decomposition problem. In such case we apply the Penalty Function Method (PFM) and Augmented Lagrangian Multipliers Method (ALMM) to solve this new non-convex optimization problem. Theoretically, corresponding to our methods, the convergence analysis is given respectively. Compared with classical RPCA, some practical numerical examples are simulated to show that our methods are much better than RPCA.