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1.
解第一类算子方程的一种新的正则化方法 总被引:4,自引:0,他引:4
对算子与右端都为近似给定的第一类算子方程提出一种新的正则化方法,依据广义Arcangeli方法选取正则参数,建立了正则解的收敛性。这种新的正则化方法与通常的Tikhonov正则化方法相比较,提高了正则解的渐近阶估计。 相似文献
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对于带有右端扰动数据的第一类紧算子方程的病态问题 ,本文应用正则化子建立了一类新的正则化求解方法 ,称之为改进的Tikonov正则化 ;通过适当选取正则参数 ,证明了正则解具有最优的渐近收敛阶 .与通常的Tikhonov正则化相比 ,这种改进的正则化可使正则解取到足够高的最优渐近阶 相似文献
3.
应用正则化子建立求解不适定问题的正则化方法的探讨 总被引:9,自引:0,他引:9
根据紧算子的奇异系统理论,提出一种新的正则化子进而建立了一类新的求解不适定问题的正则化方法。分别通过正则参数的先验选取和后验确定方法,证明了正则解的收敛性并得到了其最优的渐近收敛阶;验证了应用Newton迭代法计算最佳参数的可行性。最后建立了当算子与右端均有扰动时相应的正则化求解策略。文中所述方法完善了一般优化正则化策略的构造理论。 相似文献
4.
求解病态问题的一种改进的Tikhonov正则化:⑴正则化方法的建立 总被引:1,自引:0,他引:1
对于带有右扰动数据的第一类紧算子方程的病态问题。本文应用正则化子建立了一类新的正则化求解方法,称之为改进的Tikonov正则化;通过适当选取2正则参数,证明了正则解具有最优的渐近收敛阶,与通常的Tikhonov正则化相比,这种改进的正则化可使正则解取到足够高的最优渐近阶。 相似文献
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A—光滑正则化算子 总被引:3,自引:0,他引:3
贺国强 《高校应用数学学报(A辑)》1992,7(4):568-578
本文研究了紧算子方程的Moore-Penrose广义解的逼近,引进了A-导数的概念和对应的A-光滑正则化算子.这个双参数的A-光滑正则化算子族有明显的变分意义,并且包含正则化算子作为它的特殊情形,以(修正的)截断奇异值分解方法作为它的极限情形.这些正则化算子的性质表明它们有广阔的实际应用可能性. 相似文献
7.
一种新的正则化方法的正则参数的最优后验选取 总被引:1,自引:0,他引:1
应用紧算子的奇异系统和广义Arcangeli方法后验选取正则参数,证明了文[1]中所建立的求解第一类算子方程的正则化方法是收敛的,且正则解具有最优的渐近阶。 相似文献
8.
关于迭代Tikhonov正则化的最优正则参数选取 总被引:2,自引:0,他引:2
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。 相似文献
9.
根据紧算子的奇异系统理论,引入一种正则化滤子函数,从而建立一种新的正则化方法来求解右端近似给定的第一类算子方程,并给出了正则解的误差分析。通过正则参数的先验选取,证明了正则解的误差具有渐进最优阶。 相似文献
10.
刘德金 《纯粹数学与应用数学》2013,(6):559-564
给出了关于子基的正则空间和相对正则性概念,研究了各种正则性之间的关系,证明了各种正则空间的充要条件,丰富了一般拓扑学中的正则空间和相对正则性理论. 相似文献
11.
Alessandro Buccini Marco Donatelli Lothar Reichel 《Numerical Linear Algebra with Applications》2017,24(4)
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. 相似文献
12.
The numerical solution of linear discrete ill-posed problems typically requires regularization, i.e., replacement of the available
ill-conditioned problem by a nearby better conditioned one. The most popular regularization methods for problems of small
to moderate size, which allow evaluation of the singular value decomposition of the matrix defining the problem, are the truncated
singular value decomposition and Tikhonov regularization. The present paper proposes a novel choice of regularization matrix
for Tikhonov regularization that bridges the gap between Tikhonov regularization and truncated singular value decomposition.
Computed examples illustrate the benefit of the proposed method. 相似文献
13.
In this paper we establish the error estimates for multi-penalty regularization under the general smoothness assumption in the context of learning theory. One of the motivation for this work is to study the convergence analysis of two-parameter regularization theoretically in the manifold learning setting. In this spirit, we obtain the error bounds for the manifold learning problem using more general framework of multi-penalty regularization. We propose a new parameter choice rule “the balanced-discrepancy principle” and analyze the convergence of the scheme with the help of estimated error bounds. We show that multi-penalty regularization with the proposed parameter choice exhibits the convergence rates similar to single-penalty regularization. Finally on a series of test samples we demonstrate the superiority of multi-parameter regularization over single-penalty regularization. 相似文献
14.
This paper deals with an inverse problem for identifying an unknown time-dependent heat source in a one-dimensional heat equation, with the aid of an extra measurement of temperature at an internal point. Since this problem is ill-posed, two regularization solutions are obtained by employing a Fourier truncation regularization and a Quasi-reversibility regularization. Furthermore, the Hölder type stability estimate between the regularization solutions and the exact solution, are obtained, respectively. Numerical examples show that these regularization methods are effective and stable. 相似文献
15.
提出了一种新的解第一类算子方程的迭代正则化方法,与通常的迭代正则化方法相比,提高了j次迭代正则解的渐近阶估计.同时,给出了后验正则化参数的选择. 相似文献
16.
Zewen Wang 《Journal of Computational and Applied Mathematics》2012,236(7):1815-1832
In this paper, we study the multi-parameter Tikhonov regularization method which adds multiple different penalties to exhibit multi-scale features of the solution. An optimal error bound of the regularization solution is obtained by a priori choice of multiple regularization parameters. Some theoretical results of the regularization solution about the dependence on regularization parameters are presented. Then, an a posteriori parameter choice, i.e., the damped Morozov discrepancy principle, is introduced to determine multiple regularization parameters. Five model functions, i.e., two hyperbolic model functions, a linear model function, an exponential model function and a logarithmic model function, are proposed to solve the damped Morozov discrepancy principle. Furthermore, four efficient model function algorithms are developed for finding reasonable multiple regularization parameters, and their convergence properties are also studied. Numerical results of several examples show that the damped discrepancy principle is competitive with the standard one, and the model function algorithms are efficient for choosing regularization parameters. 相似文献
17.
Heinz W. Engl 《Numerical Functional Analysis & Optimization》2013,34(2):201-222
In this paper, we give necessary and sufficient conditions for weak and strong convergence of general regularization methods for the solution of linear ill-posed problems in Hilbert space. As special cases we obtain convergence criteria for Tychonoff regularization, for a regularization method based on Showalter's integral formula, and for regularization by truncated iteration 相似文献
18.
《Journal of Complexity》2006,22(3):371-381
We discuss adaptive strategies for choosing regularization parameters in Tikhonov–Phillips regularization of discretized linear operator equations. Two rules turn out to be based entirely on data from the underlying regularization scheme. Among them, only the discrepancy principle allows us to search for the optimal regularization parameter from the easiest problem. This potential advantage cannot be achieved by the standard projection scheme. We present a modified scheme, in which the discretization level varies with the successive regularization parameters, which has the advantage, mentioned before. 相似文献
19.
反问题是现在数学物理研究中的一个热点问题,而反问题求解面临的一个本质性困难是不适定性。求解不适定问题的普遍方法是:用与原不适定问题相“邻近”的适定问题的解去逼近原问题的解,这种方法称为正则化方法.如何建立有效的正则化方法是反问题领域中不适定问题研究的重要内容.当前,最为流行的正则化方法有基于变分原理的Tikhonov正则化及其改进方法,此类方法是求解不适定问题的较为有效的方法,在各类反问题的研究中被广泛采用,并得到深入研究. 相似文献
20.
探讨了半带状区域上二维Poisson方程只含有一个空间变量的热源识别反问题.这类问题是不适定的,即问题的解(如果存在的话)不连续依赖于测量数据.利用Carasso-Tikhonov正则化方法,得到了问题的一个正则近似解,并且给出了正则解和精确解之间具有Holder型误差估计.数值实验表明Carasso-Tikhonov正则化方法对于这种热源识别是非常有效的. 相似文献