解第一类算子方程的一种迭代正则化方法

提出了一种求解第一类算子方程的新的迭代正则化方法,并依据广义Arcangeli方法选取正则参数,建立了正则解的收敛性.与通常的Tikhonov正则化方法相比较,提高了正则解的渐近阶估计.     

基于埃特金加速的改进Landweber迭代正则化方法

为克服Landweber迭代正则化方法在求解大规模不适定问题时收敛速度慢的不足,将埃特金加速技巧与不动点迭代相结合,构建了能快速收敛的改进Landweber迭代正则化方法.数值实验结果表明:改进的迭代正则化方法在稳定求解不适定问题时,能够快速地收敛至问题的最优解,较Landweber迭代正则化方法大大提高了收敛速度.     

求解交替迭代正则化方程的多尺度配置法

<正>1引言第一类Fredholm积分方程应用于科学与工程领域,用来塑造某些具体问题的数学模型.例如,图像处理,信号识别,遥感技术等.因为病态积分方程的数值计算对舍入误差特别敏感,直接求得的数值解往往与精确解相差甚远.想要得到近似效果好且稳定的数值解,需要采用正则化方法.理论上,正则化方法已经很成熟了,有Richardson迭代正则化方法~([10,11]),交替迭代正则化方法~([10,11]),Tikhonov正则化方法~([1,4,6])和迭代Lavrentiev正则化方法~([12])等.常见的停止准则有偏差原理~([11,14]),平衡原理~([15]),Lepskii原则~([13])等.但是,求解病态积分方程的数值计算方法还有很多问题有待研究,如快速计算方法.     

一类球型区域上变系数反向热传导问题

考虑了一类球型区域上变系数反向热传导问题.这个问题是不适定的,即问题的解（若存在）并不连续依赖于测量数据.构造了投影迭代正则化方法,得到了该反问题的正则近似解,同时给出了在先验和后验参数选取规则下精确解与正则近似解之间的收敛性误差估计.最后,通过数值结果验证了该方法的有效性.     

不适定问题的迭代Tikhonov正则化方法

Tikhonov正则化方法是研究不适定问题最重要的正则化方法之一,但由于这种方法的饱和效应,使得不可能随着解的光滑性假设的提高而提高收敛率,即不能使正则解与准确解的误差估计达到阶数最优．本文所讨论的迭代的Tikhonov正则化方法对此进行了改进,保证了误差估计总可以达到阶数最优．数值试验结果表明计算效果良好．     

一类逆时反问题的改进正则化方法的收敛性

1引 言 非线性反问题广泛地存在于许多科学和工程问题中,反问题求解的主要困难在于问题的不适定性,即待求函数或参量不连续依赖于观测数据.用来求解非线性不适定问题的方法主要有Tikhonov正则化方法和迭代正则化方法[1,2,3,4].Tikhonov正则化方法是通过引入正则化参数及稳定泛函,将目标泛函离散化,从而得到解的一个稳定近似,即正则化解.     

一类半线性椭圆方程柯西问题的正则化方法

构造并利用一种广义分数Tikhonov正则化方法研究一类半线性椭圆方程柯西问题.基于所构造的正则化解满足一个非线性积分方程,首先证明正则化解的存在唯一性和稳定性;继而在对精确解的先验假设下给出并证明正则化方法的收敛性;最后设计一种迭代算法计算正则化解,并通过相应的计算结果验证了所提方法的稳定可行性.     

算子与右端都为近似的迭代正则化方法

讨论了解算子与右端都近似给定的第一类算子方程的迭代Tikhonov正则化方法,建立了一种选择正则参数的方法——广义Arcangeli方法,得到正则化逼近解的收敛速度估计。     

基于迭代正则化高斯-牛顿法的非线性Urysohn积分方程数值解

非线性Urysohn积分方程在许多领域中都有广泛的应用,但由于该方程具有不适定性的特点,数据的微小扰动可能导致解的巨大变化,给数值求解带来很大困难.为了获得稳定的、准确的数值解,本文利用迭代正则化高斯-牛顿法对此方程进行求解,给出了利用Sigmoid-型函数确定迭代正则化参数的方法.对一类重力测定问题进行了数值模拟,将得到的数值解和相应的精确解作比较.结果表明,本文提出的方法在求解非线性Urysohn积分方程时是可行的也是有效的.     

求解隐式迭代方程的多尺度配置法

用多尺度快速配置法求解病态积分方程的隐式迭代方程.在积分算子是扇形紧算子时,该方法得到了离散隐式迭代方程的近似解.采用Morozov偏差原理作为停止准则,并证明了在该准则下隐式迭代正则化方法所得近似解的收敛率.最后,用数值实验证实理论结果和说明数值方法的有效性.     

A FAST CONVERGENT METHOD OF ITERATED REGULARIZATION

This article presents a fast convergent method of iterated regularization based on the idea of Landweber iterated regularization, and a method for a-posteriori choice by the Morozov discrepancy principle and the optimum asymptotic convergence order of the regularized solution is obtained. Numerical test shows that the method of iterated regularization can quicken the convergence speed and reduce the calculation burden efficiently.     

Iterated Tikhonov regularization with a general penalty term

Tikhonov regularization is one of the most popular approaches to solving linear discrete ill‐posed problems. The choice of the regularization matrix may significantly affect the quality of the computed solution. When the regularization matrix is the identity, iterated Tikhonov regularization can yield computed approximate solutions of higher quality than (standard) Tikhonov regularization. This paper provides an analysis of iterated Tikhonov regularization with a regularization matrix different from the identity. Computed examples illustrate the performance of this method.     

On the choice of solution subspace for nonstationary iterated Tikhonov regularization

Tikhonov regularization is a popular method for the solution of linear discrete ill-posed problems with error-contaminated data. Nonstationary iterated Tikhonov regularization is known to be able to determine approximate solutions of higher quality than standard Tikhonov regularization. We investigate the choice of solution subspace in iterative methods for nonstationary iterated Tikhonov regularization of large-scale problems. Generalized Krylov subspaces are compared with Krylov subspaces that are generated by Golub–Kahan bidiagonalization and the Arnoldi process. Numerical examples illustrate the effectiveness of the methods.     

Iterative implementation of the adaptive regularization yields optimality

The adaptive regularization method is first proposed by Ryzhikov et al. for the deconvolution in elimination of multiples. This method is stronger than the Tikhonov regularization in the sense that it is adaptive, i.e. it eliminates the small eigenvalues of the adjoint operator when it is nearly singular. We will show in this paper that the adaptive regularization can be implemented iterately. Some properties of the proposed non-stationary iterated adaptive regularization method are analyzed. The rate of convergence for inexact data is proved. Therefore the iterative implementation of the adaptive regularization can yield optimality.     

关于迭代Tikhonov正则化的最优正则参数选取

本文讨论了算子和右端都近似给定的第一类算子方程的迭代Ｔｉｋｈｏｎｏｖ正则化，给出了不依赖于准确解的任何信息但能得到最优收敛阶的正则参数选取法。     

A fast multiscale Kantorovich method for weakly singular integral equations

In this paper, we use the idea of Kantorovich regularization to develop the fast multiscale Kantorovich method and the fast iterated multiscale Kantorovich method. For some kinds of weakly singular integral equations with nonsmooth inhomogeneous terms, we show that our two proposed methods can still obtain the optimal order of convergence and superconvergence order, respectively. Numerical examples are given to demonstrate the efficiency of the methods.     

求解Lavrentiev迭代方程的多尺度快速配置算法

考虑了第一类Fredholm积分方程的求解.采用有矩阵压缩策略的多尺度配置方法来离散Lavrentiev迭代方程,在积分算子是弱扇形紧算子时,给出近似解的先验误差估计,并给出了改进的后验参数的选择方法,得到了近似解的收敛率.最后,举例说明算法的有效性.     

On nondecreasing sequences of regularization parameters for nonstationary iterated Tikhonov

Nonstationary iterated Tikhonov is an iterative regularization method that requires a strategy for defining the Tikhonov regularization parameter at each iteration and an early termination of the iterative process. A classical choice for the regularization parameters is a decreasing geometric sequence which leads to a linear convergence rate. The early iterations compute quickly a good approximation of the true solution, but the main drawback of this choice is a rapid growth of the error for later iterations. This implies that a stopping criteria, e.g. the discrepancy principle, could fail in computing a good approximation. In this paper we show by a filter factor analysis that a nondecreasing sequence of regularization parameters can provide a rapid and stable convergence. Hence, a reliable stopping criteria is no longer necessary. A geometric nondecreasing sequence of the Tikhonov regularization parameters into a fixed interval is proposed and numerically validated for deblurring problems.