Riesz界与条件数

在多分辨分析与小波分析中,人们经常要估计Riesz基的上下界.在有限维空间中,这等价于计算Riesz基所对应Gramian矩阵的条件数.本文给出Riesz基与条件数的关系并且讨论了Riesz基加入元素后对Riesz界产生的影响.     

KKT系统结构条件数与条件数的比较

本文讨论Karush-Kuhn_Tucker(KKT)系统的条件数．首先利用单参数展开方法建立了Byers型不等式,然后讨论结构条件数与条件数的定性比较,结果表明,在极端情形,条件数与结构条件数之比可以任意大．     

一个自适应Dai-Liao共轭梯度法

共轭梯度法是求解大规模无约束优化问题的经典方法之一.基于搜索方向矩阵的谱条件数,给出了一个Dai-Liao(DL)共轭梯度法中参数的自适应形式,提出一种自适应DL共轭梯度算法.在适当的条件下,对于一致凸的目标函数证明了该方法具有全局收敛性.数值结果表明,提出的方法是可行的.     

矩阵范数条件数的估计（英文）

本文中,我们获得一类双对角矩阵普范数与Frobenius范数条件数的上、下界估计,所得结果可用于测度线性方程组解的敏度.     

代数Riccati方程可稳解的条件数

１．引言 关于代数Ｒｉｃｃａｔｉ方程（ＡＲＥ）的研究是大量的．从数值角度看,有关数值方法,扰动理论的研究已比较深入．而关于条件数理论的研究则还不多［３］,［６］． Ｒｙｅｒｓ［１］研究了时连续代数 Ｒｉｃｃａｔｉ方程可稳解的条件数; Ｋｅｎｎｅｙ和 Ｈｅｗｅｒ［３］讨论了时连续代数 Ｒｉｃｃａｔｉ方程（以下简称 ＣＴＡＲＥ）可稳解的敏度分析,给出了一阶扰动界,引进了条件数; Ｓｕｎ［６］从最佳向后扰动理论角度研究了时离散代数Ｒｉｃｃａｔｉ方程（以下简称ＤＴＡＲＥ）可稳解的条件数;徐树方［８］针对 ＣＴＡ…     

矩阵方程X—A*X~qA=I(0＜q＜1)Hermite正定解的扰动分析

首先证明了非线性矩阵方程X-A~*X~qA=I(0相似文献   

误差估计中矩阵求逆条件数的最优性在Hilbert空间中的推广

本文利用Hilbert空间中可逆算子的极发解定理，将误差估计中矩阵求逆条件数的最优性在Hilbert空间中进行推广，证明了线性有界算子A的求逆条件数K(A)=‖A‖A^-1‖在求算子扰动逆(A E)^-1的相对误差界中的极小性质，指出了算子求逆条件数在误差估计为仅与算子A有关的最佳常数值。     

一类二次矩阵方程的条件数和后向误差

主要讨论一类二次矩阵方程X^2-EX-F=0的条件数和后向误差,其中E是一个对角矩阵,F是一个M矩阵.这类二次矩阵方程来源于Markov链的噪声Wiener-Hopf问题.实际问题中人们感兴趣的是它的M矩阵的解.应用Rice创立的基于Frobenius范数下的条件数理论,导出此类二次矩阵方程的M矩阵解的条件数的显式表达式.同时,也给出近似解的后向误差的定义以及一个可计算的表达式.最后,通过数值例子验证理论结果是有效的.     

满足Condl(A)=1(l=1,∞)的矩阵A的充要条件

本文导出了长方矩阵A的条件数Cond_l(A)=‖A‖_l‖A⁺‖_l=1(l=1,∞)的充要条件;而且给出了Cond_l(A)=1时A的矩阵结构和元素结构形式;同时指出文[6]中两个主要定理的错误.     

矩阵方程ATXA=D的条件数与向后扰动分析

讨论矩阵方程ATXA=D,该方程源于振动反问题和结构模型修正.本文利用Moore-Penrose广义逆的性质,给出该方程解的条件数的上、下界估计.同时,利用Schauder不动点理论给出该方程的向后扰动界,这些结果可用于该矩阵方程的数值计算.     

On componentwise condition numbers for eigenvalue problems with structured matrices

We give explicit expressions for the componentwise condition number for eigenvalue problems with structured matrices. We will consider only linear structures and show a general result from which expressions for the condition numbers follow. We obtain explicit expressions for the following structures: Toeplitz and Hankel. Details for other linear structures should follow in a straightforward manner from our general result. Copyright © 2008 John Wiley & Sons, Ltd.     

Applications of statistical condition estimation to the solution of linear systems

This paper discusses some applications of statistical condition estimation (SCE) to the problem of solving linear systems. Specifically, triangular and bidiagonal matrices are studied in some detail as typical of structured matrices. Such a structure, when properly respected, leads to condition estimates that are much less conservative compared with traditional non‐statistical methods of condition estimation. Some examples of linear systems and Sylvester equations are presented. Vandermonde and Cauchy matrices are also studied as representative of linear systems with large condition numbers that can nonetheless be solved accurately. SCE reflects this. Moreover, SCE when applied to solving very large linear systems by iterative solvers, including conjugate gradient and multigrid methods, performs equally well and various examples are given to illustrate the performance. SCE for solving large linear systems with direct methods, such as methods for semi‐separable structures, are also investigated. In all cases, the advantages of using SCE are manifold: ease of use, efficiency, and reliability. Copyright © 2008 John Wiley & Sons, Ltd.     

抛物问题的拉格朗日乘子区域分解法

１．引言 近年来,一类新的非重叠区域分解方法一非匹配网格区域分解法,日益引起人们的广泛兴趣,并已成为当今区域分解方法研究的热门课题。这类区域分解方法的特点是：相邻子区域在公共边（或面）上的结点可以不重合,从而能解决许多传统区域分解方法不便解决的问题（如变动网格问题）．目前主要有两类方法来处理这种区域分解的强非协调性：Ｍｏｒｔａｒ无法（见［１－２］和［９－１０］）和拉格朗日乘子法（见［５］,［８］,［１１］和［１２］）．拉格朗日乘子法比Ｍｏｒｔａｒ无法有明显的优点：（１）界面变量（即拉格朗日乘子）…     

Towards an optimal condition number of certain augmented Lagrangian‐type saddle‐point matrices

We present an analysis for minimizing the condition number of nonsingular parameter‐dependent 2 × 2 block‐structured saddle‐point matrices with a maximally rank‐deficient (1,1) block. The matrices arise from an augmented Lagrangian approach. Using quasidirect sums, we show that a decomposition akin to simultaneous diagonalization leads to an optimization based on the extremal nonzero eigenvalues and singular values of the associated block matrices. Bounds on the condition number of the parameter‐dependent matrix are obtained, and we demonstrate their tightness on some numerical examples. Copyright © 2016 John Wiley & Sons, Ltd.     

Structured mixed and componentwise condition numbers of some structured matrices

Structured matrices, such as Cauchy, Vandermonde, Toeplitz, Hankel, and circulant matrices, are considered in this paper. We apply a Kronecker product-based technique to deduce the structured mixed and componentwise condition numbers for the matrix inversion and for the corresponding linear systems.     

Bounds for eigenvalues and condition numbers in the -version of the finite element method

In this paper, we present a theory for bounding the minimum eigenvalues, maximum eigenvalues, and condition numbers of stiffness matrices arising from the -version of finite element analysis. Bounds are derived for the eigenvalues and the condition numbers, which are valid for stiffness matrices based on a set of general basis functions that can be used in the -version. For a set of hierarchical basis functions satisfying the usual local support condition that has been popularly used in the -version, explicit bounds are derived for the minimum eigenvalues, maximum eigenvalues, and condition numbers of stiffness matrices. We prove that the condition numbers of the stiffness matrices grow like , where is the number of dimensions. Our results disprove a conjecture of Olsen and Douglas in which the authors assert that ``regardless of the choice of basis, the condition numbers grow like or faster". Numerical results are also presented which verify that our theoretical bounds are correct.

     

Indefinite Least Squares Problem with Quadratic Constraint and Its Condition Numbers

In this paper,we consider the indefinite least squares problem with quadratic constraint and its condition numbers.The conditions under which the problem has the unique solution are first presented.Then,the normwise,mixed,and componentwise condition numbers for solution and residual of this problem are derived.Numerical example is also provided to illustrate these results.     

Structured Backward Error and Condition Number for Linear Systems of the Type A* Ax = b

We present a formulation for the structured condition number and for the structured backward error for the linear system A* Ax = b, when the rectangular matrix A is subjected to normwise perturbations. Perturbations on the data A and the solution x are measured in the Frobenius norm. Numerical experiments are provided that show the relevance of this condition number in the prediction of the computing error when solving such systems.