Jordan标准形过渡矩阵的一种算法

给出Jordan定理的一个证明,以及Jordan标准形过渡矩阵的一种算法:求出一线性方程组解空间的基,解空间即是矩阵关于某特征值的特征向量、广义特征向量所张成的子空间,在该解空间中依次找出各特征向量及所对应的广义特征向量.一个8阶矩阵的计算实例表明算法简便实用.     

基于Cramer法则计算矩阵的特征向量

基于Cramer法则,得到了复数域上满足一定条件的线性方程组的求解方法.通过分析原矩阵的特征值与所构造矩阵的特征值之间的内在关系,构造了一种求解原矩阵特征向量各个分量的方法.     

常系数线性差分方程组的一种解法

本文给出常系数线性差分方程组求通解的一种方法：循环特征向量列法。     

具有内部构造安全保障体系的冗余机器系统的特征值的问题

对具有内部构造安全保障体系的冗余机器系统中的特征值的存在性进行了分析求解,给出了实例,并对该系统的特征值进行了一个特征值对应一个特征向量的求征.     

特征值反问题的唯一性

证明了由特征值及特征向量反求矩阵时,特征值在对角矩阵中的排序可以是任意的,只须将对应特征向量作相应排序,所得矩阵唯一。对于重特征值的线性无关的特征向量可任意选取,所得矩阵唯一。     

二环链系统的稳定性和能控性

提出了二环链系统,并利用二环链特征向量,给出了一个系统可实现二环链分解的充分必要条件,给出了可实现二环链线性系统的分解算法,研究了二环链系统的稳定性和能控性.     

矩阵的特征根与特征向量及其相似对角形的统一求法

给出了解决矩阵对角化问题的一个简便方法.应用这个方法,可同时求出A的特征根及特征向量,判断A是否可对角化,在A可对角化时,可直接写出相应的可逆矩阵T,使T-1AT为对角形矩阵.     

特征值与特征向量

特征值与特征向量的两种不同定义是一致的：线性变换/A与其对应的n阶矩阵A有相同的特征值,而n阶矩阵A的特征向量x是/A的特征向量ξ在基ε1,…εn下的坐标。     

矩阵乘积AB与BA的相似问题

首先给出利用广义特征向量构造矩阵Jordan标准形的方法,进而通过分析矩阵乘积的广义特征子空间给出矩阵乘积AB与BA相似的充分必要条件.特别地,还给出希尔维斯特定理的一个推广形式.     

对“矩阵的特征根与特征向量的同步求解方法探讨”的改进意见

贵刊1991年12月发表高吉全同志“矩阵的特征根与特征向量的同步求解方法探讨”一文,阅后想提些改进意见,供大家参考。[1]是通过对n阶矩阵A的特征矩阼F(λ)施以列初等变换,将其化为下三角的λ—矩阵B(λ)来解决问题的。美中不足的是:设λ_0是A的一个特征根,当B(λ_0)中非0列向量线性相     

One projection method for linear third order equation

In this paper we study the Galyorkin method with a special basis for a linear operator-differential equation of the third order in a separable Hilbert space. The projection method is based on the eigenvectors of the operator similar to the leading operator of equation. We obtain estimates for the convergence rate of approximate solutions in uniform topology.     

Steklov特征值问题的一种有效的Legendre-Galerkin谱逼近

本文给出Steklov特征值问题基于Legendre-Galerkin逼近的一种有效的谱方法.首先利用Legendre多项式构造了一组适当的基函数使得离散变分形式中的矩阵是稀疏的,然后推导了2维及3维情形下离散变分形式基于张量积的矩阵形式,由此可以快速地计算出离散的特征值和特征向量.文章还给出了误差分析和数值试验,数值结果表明本文提出的方法是稳定和有效的.     

Error Estimation for Eigenvalues of Unbounded Linear Operators and an Application to Energy Levels in Graphene Quantum Dots

The eigenvalue problem for linear differential operators is important since eigenvalues correspond to the possible energy levels of a physical system. It is also important to have good estimates of the error in the computed eigenvalues. In this work, we use spline interpolation to construct approximate eigenfunctions of a linear operator using the corresponding eigenvectors of a discretized approximation of the operator. We show that an error estimate for the approximate eigenvalues can be obtained by evaluating the residual for an approximate eigenpair. The interpolation scheme is selected in such a way that the residual can be evaluated analytically. To demonstrate that the method gives useful error bounds, we apply it to a problem originating from the study of graphene quantum dots where the goal was to investigate the change in the spectrum from incorporating electron–electron interactions in the potential.     

Riesz basis for strongly continuous groups

Given a Hilbert space and the generator of a strongly continuous group on this Hilbert space. If the eigenvalues of the generator have a uniform gap, and if the span of the corresponding eigenvectors is dense, then these eigenvectors form a Riesz basis (or unconditional basis) of the Hilbert space. Furthermore, we show that none of the conditions can be weakened. However, if the eigenvalues (counted with multiplicity) can be grouped into subsets of at most K elements, and the distance between the groups is (uniformly) bounded away from zero, then the spectral projections associated to the groups form a Riesz family. This implies that if in every range of the spectral projection we construct an orthonormal basis, then the union of these bases is a Riesz basis in the Hilbert space.     

Hecke Operators on Linear Ordinary Differential Equations

We construct Hecke operators acting on the space of certain linear ordinary differential equations, and describe a Hermitian inner product on the space of such differential equations. We also determine the adjoint of the Hecke operator with respect to this inner product, and prove that the space of ordinary differential equations associated to an automorphic form for a certain discrete subgroup of SL(2, R) has a basis consisting of common eigenvectors of a class of Hecke operators.     

Stabilization of string system with linear boundary feedback

In this paper we consider the uniform stabilization of a vibrating string with Neumann-type boundary conditions. Herein we do not consider a controller stabilizing the system, but emphasize the simplicity and effectiveness of the controller. We adopt the linear feedback control law, which comprises both boundary velocity and position, and prove that the closed loop system is dissipative and asymptotically stable. By asymptotic analysis of frequency of the closed loop system, we give asymptotic expression of the frequencies and the Riesz basis property of eigenvectors and generalized eigenvectors of the system operator under some conditions, and hence obtain the exponential stability of the closed loop system. We show that, for a particular case, the system may be super-stable in subspace of a codimensional one. From the above result, we conclude that one can design a much simpler linear controller by choice of parameters such that the closed loop system is of Riesz basic properties and exponentially stable.     

Riesz Basis Property and Stability of Planar Networks of Controlled Strings

In this paper we study the basis property of root vectors of a star-shaped networks of strings whose exterior ends are clamped and common node has a damping. The main operator determined by the networks is non-normal. By the asymptotical technique, we show that its spectra distribute in a strip parallel to the imaginary axis, and there is a sequence of root vectors (generalized eigenvectors and eigenvectors) that forms a Riesz basis with parentheses for the Hilbert state space. As a application of this result, we discuss the stability of the system.     

Fast sensitivity analysis of defective system

Sensitivity analysis of linear vibration system is of wide interest. In this paper, sensitivity analysis based on non-defective system and defective system is summarized in all cases. Specially, for the defective systems, a fast method for the perturbation problem of state vectors is constructed in terms of the theories of generalized eigenvectors and adjoint matrices. By this method, the state vector derivatives can be expressed by a linear combination of generalized eigenvectors. The expansion coefficients can be obtained without solving large-scale equations based on eigensolutions of original system. Numerical results demonstrate the effectiveness and the stability of the method.     

Eigenvectors of a kurtosis matrix as interesting directions to reveal cluster structure

In this paper we study the properties of a kurtosis matrix and propose its eigenvectors as interesting directions to reveal the possible cluster structure of a data set. Under a mixture of elliptical distributions with proportional scatter matrix, it is shown that a subset of the eigenvectors of the fourth-order moment matrix corresponds to Fisher’s linear discriminant subspace. The eigenvectors of the estimated kurtosis matrix are consistent estimators of this subspace and its calculation is easy to implement and computationally efficient, which is particularly favourable when the ratio n/p is large.     

Backward error of polynomial eigenvalue problems solved by linearization

It is commonplace in many application domains to utilize polynomial eigenvalue problems to model the behaviour of physical systems. Many techniques exist to compute solutions of these polynomial eigenvalue problems. One of the most frequently used techniques is linearization, in which the polynomial eigenvalue problem is turned into an equivalent linear eigenvalue problem with the same eigenvalues, and with easily recoverable eigenvectors. The eigenvalues and eigenvectors of the linearization are usually computed using a backward stable solver such as the QZ algorithm. Such backward stable algorithms ensure that the computed eigenvalues and eigenvectors of the linearization are exactly those of a nearby linear pencil, where the perturbations are bounded in terms of the machine precision and the norms of the matrices defining the linearization. Although we have solved a nearby linear eigenvalue problem, we are not certain that our computed solution is in fact the exact solution of a nearby polynomial eigenvalue problem. Here, we perform a backward error analysis for the solution of a specific linearization for polynomials expressed in the monomial basis. We use a suitable one-sided factorization of the linearization that allows us to map generic perturbations of the linearization onto structured perturbations of the polynomial coefficients. (© 2015 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)