解不适定算子方程的一个定常二步隐式迭代法

１．引言 设Ｘ,Ｙ是两个Ｈｉｌｂｅｒｔ空间,Ａ：Ｘ→Ｙ是有界线性算子,考虑算子方程 Ａｘ＝ｙ（１．１）如果Ａ的值域Ｒ（Ａ）在Ｙ中非闭,则方程（１．１）是不适定的［１］．许多应用科学中都归结出这一类方程,特别地,许多反问题是不适定的［２,３］．本文考虑方程（１．１）的 Ｍｏｏｒｅ－Ｐｅｎｒｏｓｅ广义解,这里Ａ是算子Ａ的Ｍｏｏｒｅ－Ｐｅｎｒｏｓｅ广义逆［１］．Ａ＋ｙ存在当且仅当本文均作这一假设．在实际中,通常代替（１．１）的是扰动方程这里右端项,为一给定的误差水平,Ｑ是Ｙ到Ｒ（Ａ）的正交投影算子．对扰…     

解线性不适定方程隐式迭代法的r-步拟残差准则

本文给出了解不适定算子方程隐式迭代法后验选取步数的一类准则,称为r-步拟残差准则,证明了它们总导致最佳收敛阶.这类准则包含著名的Morozov残差准则和Gfrerer准则作为特例.     

解线性不适定方程隐式迭代法的,γ-步拟残差准则

本文给出了解不适定算子方程隐式迭代法后验选取步数的一类准则,称为,ｒ－步拟残差准则,证明了它们总导致最佳收敛阶,这类准则包含著名的 Ｍｏｒｏｚｏｖ残差准则和 Ｇｆｒｅｒｅｒ 准则作为特例．     

解不适定算子方程的一个定常二步隐式迭代法

On the basis of an implicit iterative method for ill-posed operator equations,we introduce a relaxation factor and a weighted factor , and obtain a stationarytwo-step implicit iterative method. The range of the factors which guarantee theconvergence of iteration is explored.We also study the convergence properties and ratesfor both non-perturbed andperturbed equations.An implementable algorithm is presented by using Morozov discrepancy principle.The theoretical results show that the convergence rates of the new methods always lead to optimal convergentrates which are superior to those of the original one after choosing suitable relaxation and weightedfactors. Numerical examplesare also given, which coincide well with the theoretical results.     

求解非线性不适定问题的隐式迭代法

将处理线性不适定算子方程的隐式迭代法推广到非线性不适定问题,证明了迭代解误差序列的单调性,并进一步利用迭代误差的单调性得出求解非线性不适定问题隐式迭代法对精确方程和扰动方程的收敛性．     

非线性不适定算子方程算子与右端项皆有扰动的Landweber迭代法

考虑非线性算子方程 F（x）=y（1）其中 F:D（F）　 X→Y,X,Y为 Hilbert空间.F是 Frechet可微的。 这里考虑算子方程的解x+不连续依赖于右端数据的情况。由于不稳定性,并且在实际问题中只有近似数据yδ满足 ‖yδ-y‖≤δ（2）可以得到,方程（1）必须正则化.     

解非线性不适定算子方程的一种正则化Newton迭代法

本文探讨一种求解非线性不适定算子方程的正则化Newton迭代法.本文讨论了这种迭代法在一般条件下的收敛性以及其他的一些性质.这种迭代法结合确定迭代次数的残差准则有局部收敛性.     

求解不适定问题的快速Landweber迭代法

本文从一般迭代法的级数形式出发，将一般迭代法的每一步分解为矩阵计算和求解两步，并对其中的矩阵计算部分进行了修改，在此基础上提出了快速迭代法，最后通过数值实验验证了我们的算法不仅提高了计算速度，同时也大大减少了计算量，是一种效率很高的算法。     

非线性不适定算子方程的双参数正则化方法

对非线性不适定算子方程,引入一种双参数正则化方法求解,讨论了这种正则化方法解的存在性、稳定性和收敛性.     

Orlicz空间内第一类不适定积分方程的解

讨论由L~2[a,b]到Orlicz空间L_M~*[a,b]内第一类积分方程 integral from n=a to b(K(x,y)g(y)dy=f(x)) (1)f∈L_M~*[a,b]。这里K(x,y)满足 integral from n=a to b integral from n=a to b(|K(x,y)|~2dxdy〈∞) L_M~*[a,b]为N函数M(u)生成的Orlicz空间,并赋以Orlicz范数||·||_M;L_(N)~*[a,b]为M(u)的余N函数N(v)生成的Orlicz空间,赋以Luxemburg范数。     

Parameter choice by discrepancy principles for ill-posed problems leading to optimal convergence rates

Schock (Ref. 1) considered a general a posteriori parameter choice strategy for the Tikhonov regularization of the ill-posed operator equationTx=y which provides nearly the optimal rate of convergence if the minimal-norm least-squares solution belongs to the range of the operator (T ^* T) ^v , o<v1. Recently, Nair (Ref. 2) improved the result of Schock and also provided the optimal rate ifv=1. In this note, we further improve the result and show in particular that the optimal rate can be achieved for 1/2v1.The final version of this work was written while M. T. Nair was a Visiting Fellow at the Centre for Mathematics and Its Applications, Australian National University, Canberra, Australia. The work of S. George was supported by a Senior Research Fellowship from CSIR, India.     

An implicit Landweber method for nonlinear ill-posed operator equations

In this paper, we are interested in the solution of nonlinear inverse problems of the form F(x)=y. We propose an implicit Landweber method, which is similar to the third-order midpoint Newton method in form, and consider the convergence behavior of the implicit Landweber method. Using the discrepancy principle as a stopping criterion, we obtain a regularization method for ill-posed problems. We conclude with numerical examples confirming the theoretical results, including comparisons with the classical Landweber iteration and presented modified Landweber methods.     

A KIND OF IMPLICIT ITERATIVE METHODS FOR ILL-POSED OPERATOR EQUATIONS

1.IntroductionLetX,YbetworealHilbertspacesandletA:X-- Ybeaboundedlinearoperator.ConsidertheoperatorequationAx~y.(1.1)IfR(A),i.e.,therangeofA,isnonclosedinY,equation(1.1)isill-posed[1].Manyimportantproblemsinappliedsciencesresultinthiskindofequations...     

Dynamical System Method for Solving Ill-Posed Operator Equations

Two dynamical system methods are studied for solving linear ill-posed problems with both operator and right-hand nonexact. The methods solve a Cauchy problem for a linear operator equation which possesses a global solution. The limit of the global solution at infinity solves the original linear equation. Moreover, we also present a convergent iterative process for solving the Cauchy problem.     

算子方程X+A~*X~(-t)A=Q的正算子解的研究

研究了算子方程X+A*X~(-t)A=Q(t>1)的正算子解问题,分别给出了算子方程X+A*X~(-t)A=Q有正算子解的一些充分条件和必要条件,并确定了解的范围,用迭代的方法得到方程的正算子解.     

An Adaptive Strategy for the Restoration of Textured Images Using Fractional Order Regularization

Total variation regularization has good performance in noise removal and edge preservation but lacks in texture restoration. Here we present a texture-preserving strategy to restore images contaminated by blur and noise. According to a texture detection strategy, we apply spatially adaptive fractional order diffusion. A fast algorithm based on the half-quadratic technique is used to minimize the resulting objective function. Numerical results show the effectiveness of our strategy.     

The symmetric Meixner-Pollaczek polynomials with real parameter

The Symmetric Meixner-Pollaczek polynomials for λ>0 are well-studied polynomials. These are polynomials orthogonal on the real line with respect to a continuous, positive real measure. For λ?0, are also polynomials, however they are not orthogonal on the real line with respect to any real measure. This paper defines a non-standard inner product with respect to which the polynomials for λ?0, become orthogonal polynomials. It examines the major properties of the polynomials, for λ>0 which are also shared by the polynomials, for λ?0.     

一类非线性二元算子方程的迭代求解及其应用

运用锥与半序理论与混合单调算子理论,讨论半序Banach空间一类非线性二元算子方程解的存在唯一性,并给出迭代序列收敛于解的误差估计.作为应用,讨论了不具有单调性的算子方程的可解性,所得结果是某些已有结果的本质改进和推广.     

Alternating Krylov subspace image restoration methods

Alternating methods for image deblurring and denoising have recently received considerable attention. The simplest of these methods are two-way methods that restore contaminated images by alternating between deblurring and denoising. This paper describes Krylov subspace-based two-way alternating iterative methods that allow the application of regularization operators different from the identity in both the deblurring and the denoising steps. Numerical examples show that this can improve the quality of the computed restorations. The methods are particularly attractive when matrix-vector products with a discrete blurring operator and its transpose can be evaluated rapidly, but the structure of these operators does not allow inexpensive diagonalization.