可对称化矩阵特征值的扰动界

在[1]中,Kahan证明了如下的定理:设A为n×n Hermite矩阵,B为n×n。可对称化矩阵,即存在非奇异矩阵Q,使得Q~(-1)BQ为实对角矩阵。又设A,B的特征值分别为λ_1     

关于Lanczos方法解对称方程组的一个判据

设A是n×n实对称非定矩阵,b是n维列向量,考虑方程组 Ax=b的求解问题。因为A是非定的,因此不能使用共轭斜量法,对于大型稀疏矩阵A的求解,文[1]和[2]提出使用Lanczos算法:取初始近似向量x_0,r_0=b—Ax_0,β_0=||r_0||z,令 q_1=r_0/β_0,逐次构造Lancoz序列q_1,q_2,…,q_(j 1),即     

正定可对称化矩阵与预对称迭代算法

１．问题的提出 我们引入正定可对称化矩阵定义的背景是为了研究求解二阶椭圆型非自共轭方程的离散迭代有效算法、这类方程的椭圆型是本质的分析性质。是由二阶项决定的,在离散方程中表现为正定性;非自共轭性则是由方程中的一阶项引起的,在相当广泛一类问题中可通过变量代换化为自共轭。因此,我们称这类问题为正定可对称化问题。 例１．高维二阶常系数椭圆型方程其中 Ａ为常系数正定对称（ｓ．ｐ．ｄ）阵, 为正交阵, Ｄ是对角元素为正的对角阵。 先作变量代换,通过演算,偏微分方程对于新变量变成这里进而令可将原非自共轭偏微分算子…     

试探算法与SSOR预处理共轭梯度算法的拟最佳因子

1引言在求解系数矩阵为对称正定的大型线性代数方程组Au=b (1．1)的迭代法方面,七十年代以来发展了各种预处理共轭梯度法．由于SSOR分裂中具有对称因子,可用于加速共轭梯度法,称为SSOR预处理共轭梯度法(简记为;SSORPCG．同时,由于当松弛因子ω∈(0,2)时,SSOR迭代法收敛,从而进一步发展了m步SSOR预处理共轭梯度法(简记为:m-step SSORPCG．胡家赣证明,经过最优的SSOR预条件,预优     

非齐次对称特征值问题

引言 用SR~(n×n)表示所有。n×n实对称矩阵的集合。R~n表示n维线性空间。||·||_2表示向量的Euclid范数或矩阵的谱范数。 本文研究如下问题: 问题ISEP 给定矩阵A∈SR~n×n和向量b∈R~n,求实数λ和向量X∈R~n使得 AX=λX+b, (1) ||X||_2=1. (2) 若b=0,则问题ISEP就是通常的实对称矩阵特征值问题,若b≠0,则问题ISEP称为非齐次对称特征值问题,使(1)和(2)式成立的数λ和向量X分别称为非齐次特征值和相应的非齐     

求解大型超定线性代数方程组的块Kaczmarz算法

正1 引言考虑大型超定线性代数方程组Ax=b,(1)其中 A ∈ C~(m×n) (m n),b ∈C~m.当m=n时,线性代数方程组求解的相关理论和算法较为成熟,但在很多实际问题中,系数矩阵A的行数和列数不相等(m≠n),如超定或欠定线性代数方程组.因此,有必要研究此类线性代数方程组的数值解法.在结构分析,计算机辅助几何设计,图像恢复,模型参数估计等众多领域中,经常需要求解大型超定线性代数方程组.Vuik [1]研究了大型超定线性代数方程组最小二乘问题的预处理Krylov迭代方法;Bai [2]提出列分解松弛法;Yin[3]提出了求解大型稀疏最小二乘问题的不完备Givens正交化的预处理GMRES方法;Hayami[4]考虑引入一个新的矩阵将GMRES方法应用到最小二乘问题,求得方程组的最小二乘解;Finta [5]推导了加权超定线性代数方程组的梯度法,并证明该方法是收敛的.     

双变量矩阵方程组对称最小二乘解的迭代算法

本文研究了求双变量线性矩阵方程组的对称最小二乘解的问题.利用求解线性代数方程组的共轭梯度法的基本思想,通过对有关矩阵和系数的变形与近似处理,建立了一种迭代算法.拓宽了共轭梯度法的适用范围.算例表明,迭代算法是有效的.     

研究简报：并行矩阵多分裂多参数松弛算法

求解大型稀疏线性方程组Ax=b，A∈L(R^n)，x，b∈R^n的并行矩阵多分裂算法最早由[1]提出，[2]提出了当系数矩阵是非奇H—矩阵时的多分裂多参数松弛算法，但是对于奇异H—矩阵的理论及算法的研究结果都很少，为此，     

双矩阵变量Riccati矩阵方程对称解的迭代算法

研究一类双矩阵变量Riccati矩阵方程(R-ME)对称解的数值计算问题.运用牛顿算法求R-ME的对称解时,会导出求双矩阵变量线性矩阵方程的对称解或者对称最小二乘解的问题,采用修正共轭梯度法解决导出的线性矩阵方程约束解问题,可建立求R-ME的对称解的迭代算法.数值算例表明,迭代算法是有效的.     

并行矩阵多分裂多参数松弛算法

1 引言和算法 求解大型稀疏线性方程组Ax=6, A∈L(Rn), x,b∈Rn的并行矩阵多分裂算法最早由[1]提出, [2]提出了当系数矩阵是非奇H-矩阵时的多分裂多参数松弛算法.但是对于奇异H-矩阵的理论及算法的研究结果都很少,为此,[3]对于奇异H-矩阵的并行算法进行了有益的研究.本文给出了当系数矩阵是奇异H-     

A REGULARIZED CONJUGATE GRADIENT METHOD FOR SYMMETRIC POSITIVE DEFINITE SYSTEM OF LINEAR EQUATIONS

AbstractA class of regularized conjugate gradient methods is presented for solving the large sparse system of linear equations of which the coefficient matrix is an ill-conditioned symmetric positive definite matrix. The convergence properties of these methods are discussed in depth, and the best possible choices of the parameters involved in the new methods are investigated in detail. Numerical computations show that the new methods are more efficient and robust than both classical relaxation methods and classical conjugate direction methods.     

Low-rank elastic-net regularized multivariate Huber regression model

Heavy-tailed noise or strongly correlated predictors often go with the multivariate linear regression model. To tackle with these problems, this paper focuses on the matrix elastic-net regularized multivariate Huber regression model. This new model possesses the grouping effect property and the robustness to heavy-tailed noise. Meanwhile, it also has the ability of reducing the negative effect of outliers due to Huber loss. Furthermore, an accelerated proximal gradient algorithm is designed to solve the proposed model. Some numerical studies including a real data analysis are dedicated to show the efficiency of our method.     

Preconditioned Iterative Methods for Algebraic Systems from Multiplicative Half-Quadratic Regularization Image Restorations

<正>Image restoration is often solved by minimizing an energy function consisting of a data-fidelity term and a regularization term.A regularized convex term can usually preserve the image edges well in the restored image.In this paper,we consider a class of convex and edge-preserving regularization functions,i.e.,multiplicative half-quadratic regularizations,and we use the Newton method to solve the correspondingly reduced systems of nonlinear equations.At each Newton iterate,the preconditioned conjugate gradient method,incorporated with a constraint preconditioner,is employed to solve the structured Newton equation that has a symmetric positive definite coefficient matrix. The eigenvalue bounds of the preconditioned matrix are deliberately derived,which can be used to estimate the convergence speed of the preconditioned conjugate gradient method.We use experimental results to demonstrate that this new approach is efficient, and the effect of image restoration is reasonably well.     

基于小波网络的干旱程度评估方法

本根据干旱事件识别的基本原理,同时基于小波基具有很强的自适应性和函数变化能力,提出了一种基于小波网络的干旱程度评估新方法,并在最小均方能量准则下,采用相应的共轭梯度学习算法求解子波函数线性组合的尺度和时延参数,以及小波网络的权值,仿真实验表明采用该方法极大地提高了对干旱程度辩识的正确率,可为干旱研究提一条新的途径。     

An inviscid regularization for the surface quasi‐geostrophic equation

Inspired by recent developments in Berdina‐like models for turbulence, we propose an inviscid regularization for the surface quasi‐geostrophic (SQG) equations. We are particularly interested in the celebrated question of blowup in finite time of the solution gradient of the SQG equations. The new regularization yields a necessary and sufficient condition, satisfied by the regularized solution, when a regularization parameter α tends to 0 for the solution of the original SQG equations to develop a singularity in finite time. As opposed to the commonly used viscous regularization, the inviscid equations derived here conserve a modified energy. Therefore, the new regularization provides an attractive numerical procedure for finite‐time blowup testing. In particular, we prove that, if the initial condition is smooth, then the regularized solution remains as smooth as the initial data for all times. © 2007 Wiley Periodicals, Inc.     

Projection Method Approach for General Regularized Non-convex Variational Inequalities

In this paper, we investigate or analyze non-convex variational inequalities and general non-convex variational inequalities. Two new classes of non-convex variational inequalities, named regularized non-convex variational inequalities and general regularized non-convex variational inequalities, are introduced, and the equivalence between these two classes of non-convex variational inequalities and the fixed point problems are established. A projection iterative method to approximate the solutions of general regularized non-convex variational inequalities is suggested. Meanwhile, the existence and uniqueness of solution for general regularized non-convex variational inequalities is proved, and the convergence analysis of the proposed iterative algorithm under certain conditions is studied.     

基于非线性相位恢复的X射线相位衬度断层成像算法

对全息测量下的X射线相位衬度断层成像问题提出了一种新的重建算法．该算法的主要想法是利用牛顿迭代法求解非线性的相位恢复问题．我们证明了牛顿方向满足的线性方程是非适定的,并利用共轭梯度法得到方程的正则化解．最后利用模拟数据进行了数值实验,数值结果验证了算法的合理性以及对噪声数据的数值稳定性,同时通过与线性化相位恢复算法的数值结果比较说明了新算法对探测数据不要求限制在Fresnel区域的近场,适用范围更广．     

A new regularized quasi-Newton method for unconstrained optimization

In this paper, we propose a new regularized quasi-Newton method for unconstrained optimization. At each iteration, a regularized quasi-Newton equation is solved to obtain the search direction. The step size is determined by a non-monotone Armijo backtracking line search. An adaptive regularized parameter, which is updated according to the step size of the line search, is employed to compute the next search direction. The presented method is proved to be globally convergent. Numerical experiments show that the proposed method is effective for unconstrained optimizations and outperforms the existing regularized Newton method.     

Convergence results of a modified regularized gradient method for nonlinear ill-posed problems

A modified iteratively regularized gradient method and its continuous version are proposed for nonlinear ill-posed problems, in which the Tikhonov regularization term is generated by a linear operator. The linear operator may have some physical meaning. And by employing the linear operator, scaling the problem can be avoided in the case that the nonlinear operator in the problem has larger gradient. Adopting a posteriori and a priori stopping rule respectively, we establish the convergence results by using a modified approximate source condition. The numerical results show that the linear operator effects the performance greatly.     

A partial regularity result for an anisotropic smoothing functional for image restoration in BV space

Here we examine the partial regularity of minimizers of a functional used for image restoration in BV space. This functional is a combination of a regularized p-Laplacian for the part of the image with small gradient and a total variation functional for the part with large gradient. This model was originally introduced in Chambolle and Lions using the Laplacian. Due to the singular nature of the p-Laplacian we study a regularized p-Laplacian. We show that where the gradient is small, the regularized p-Laplacian smooths the image u, in the sense that u∈C1,α for some 0<α<1. This functional thus anisotropically smooths the image where the gradient is small and preserves edges via total variation where the gradient is large.