Convergence Properties of the Method of Regularization for Noisy Linear Operator Equations

Convergence Properties of the Method of Regularization for Noisy Linear Operator Equations PDF Author: Grace Wahba
Publisher:
ISBN:
Category : Convergence
Languages : en
Pages : 40

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Convergence Properties of the Method of Regularization for Noisy Linear Operator Equations

Convergence Properties of the Method of Regularization for Noisy Linear Operator Equations PDF Author: Grace Wahba
Publisher:
ISBN:
Category : Convergence
Languages : en
Pages : 40

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Book Description


Convergence Properties of the Method of Regularization for Noisy Linear Operation Equations

Convergence Properties of the Method of Regularization for Noisy Linear Operation Equations PDF Author: Grace Wahba
Publisher:
ISBN:
Category :
Languages : en
Pages : 45

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Book Description
Convergence properties of the method of regulation for finding approximate solutions to the linear operator equation g = Kf are found when g is contaminated by noise. If f belongs to (H sub R), a reproducing kernel Hilbert Space and K((H sub R)) = (H sub Q), another r.k.h.s. which is topologically equivalent to W2(m), it is shown how the optimum choice of lambda depends on n, m, the mean square noise, and f. (Author).

Recovery Methodologies: Regularization and Sampling

Recovery Methodologies: Regularization and Sampling PDF Author: Willi Freeden
Publisher: American Mathematical Society
ISBN: 1470473453
Category : Mathematics
Languages : en
Pages : 505

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Book Description
The goal of this book is to introduce the reader to methodologies in recovery problems for objects, such as functions and signals, from partial or indirect information. The recovery of objects from a set of data demands key solvers of inverse and sampling problems. Until recently, connections between the mathematical areas of inverse problems and sampling were rather tenuous. However, advances in several areas of mathematical research have revealed deep common threads between them, which proves that there is a serious need for a unifying description of the underlying mathematical ideas and concepts. Freeden and Nashed present an integrated approach to resolution methodologies from the perspective of both these areas. Researchers in sampling theory will benefit from learning about inverse problems and regularization methods, while specialists in inverse problems will gain a better understanding of the point of view of sampling concepts. This book requires some basic knowledge of functional analysis, Fourier theory, geometric number theory, constructive approximation, and special function theory. By avoiding extreme technicalities and elaborate proof techniques, it is an accessible resource for students and researchers not only from applied mathematics, but also from all branches of engineering and science.

Regularization Algorithms for Ill-Posed Problems

Regularization Algorithms for Ill-Posed Problems PDF Author: Anatoly B. Bakushinsky
Publisher: Walter de Gruyter GmbH & Co KG
ISBN: 3110556383
Category : Mathematics
Languages : en
Pages : 447

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Book Description
This specialized and authoritative book contains an overview of modern approaches to constructing approximations to solutions of ill-posed operator equations, both linear and nonlinear. These approximation schemes form a basis for implementable numerical algorithms for the stable solution of operator equations arising in contemporary mathematical modeling, and in particular when solving inverse problems of mathematical physics. The book presents in detail stable solution methods for ill-posed problems using the methodology of iterative regularization of classical iterative schemes and the techniques of finite dimensional and finite difference approximations of the problems under study. Special attention is paid to ill-posed Cauchy problems for linear operator differential equations and to ill-posed variational inequalities and optimization problems. The readers are expected to have basic knowledge in functional analysis and differential equations. The book will be of interest to applied mathematicians and specialists in mathematical modeling and inverse problems, and also to advanced students in these fields. Contents Introduction Regularization Methods For Linear Equations Finite Difference Methods Iterative Regularization Methods Finite-Dimensional Iterative Processes Variational Inequalities and Optimization Problems

On the Convergence of Regularization Methods for Ill-posed Linear Operator Equations

On the Convergence of Regularization Methods for Ill-posed Linear Operator Equations PDF Author: Heinz W. Engl
Publisher:
ISBN:
Category :
Languages : en
Pages : 15

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Regularization Methods in Banach Spaces

Regularization Methods in Banach Spaces PDF Author: Thomas Schuster
Publisher: Walter de Gruyter
ISBN: 3110255723
Category : Mathematics
Languages : en
Pages : 296

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Book Description
Regularization methods aimed at finding stable approximate solutions are a necessary tool to tackle inverse and ill-posed problems. Inverse problems arise in a large variety of applications ranging from medical imaging and non-destructive testing via finance to systems biology. Many of these problems belong to the class of parameter identification problems in partial differential equations (PDEs) and thus are computationally demanding and mathematically challenging. Hence there is a substantial need for stable and efficient solvers for this kind of problems as well as for a rigorous convergence analysis of these methods. This monograph consists of five parts. Part I motivates the importance of developing and analyzing regularization methods in Banach spaces by presenting four applications which intrinsically demand for a Banach space setting and giving a brief glimpse of sparsity constraints. Part II summarizes all mathematical tools that are necessary to carry out an analysis in Banach spaces. Part III represents the current state-of-the-art concerning Tikhonov regularization in Banach spaces. Part IV about iterative regularization methods is concerned with linear operator equations and the iterative solution of nonlinear operator equations by gradient type methods and the iteratively regularized Gauß-Newton method. Part V finally outlines the method of approximate inverse which is based on the efficient evaluation of the measured data with reconstruction kernels.

Iterative Regularization Methods for Nonlinear Ill-Posed Problems

Iterative Regularization Methods for Nonlinear Ill-Posed Problems PDF Author: Barbara Kaltenbacher
Publisher: Walter de Gruyter
ISBN: 311020827X
Category : Mathematics
Languages : en
Pages : 205

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Book Description
Nonlinear inverse problems appear in many applications, and typically they lead to mathematical models that are ill-posed, i.e., they are unstable under data perturbations. Those problems require a regularization, i.e., a special numerical treatment. This book presents regularization schemes which are based on iteration methods, e.g., nonlinear Landweber iteration, level set methods, multilevel methods and Newton type methods.

Technical Report

Technical Report PDF Author: University of Wisconsin--Madison. Department of Statistics
Publisher:
ISBN:
Category :
Languages : en
Pages : 732

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SIAM Journal on Numerical Analysis

SIAM Journal on Numerical Analysis PDF Author: Society for Industrial and Applied Mathematics
Publisher:
ISBN:
Category : Electronic journals
Languages : en
Pages : 648

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Book Description
Contains research articles on the development and analysis of numerical methods, including their convergence, stability, and error analysis as well as related results in functional analysis and approximation theory. Computational experiments and new types of numerical applications are also included.

Regularization in Banach Spaces - Convergence Rates Theory

Regularization in Banach Spaces - Convergence Rates Theory PDF Author: Torsten Hein
Publisher: Logos Verlag Berlin GmbH
ISBN: 3832527451
Category : Mathematics
Languages : en
Pages : 174

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Book Description
Motivated by their successful application in image restoring and sparsity reconstruction this manuscript deals with regularization theory of linear and nonlinear inverse and ill-posed problems in Banach space settings. Whereas regularization in Hilbert spaces has been widely studied in literature for a long period the developement and investigation of regularization methods in Banach spaces have become a field of modern research. The manuscript is twofolded. The first part deals with convergence rates theory for Tikhonov regularization as classical regularization method. In particular, generalizations of well-established results in Hilbert spaces are presented in the Banach space situation. Since the numerical effort of Tikhonov regularization in applications is rather high iterative approaches were considered as alternative regularization variants in the second part. In particular, two Gradient-type methods were presented and their behaviour concerning convergence and stability is investigated. For one of the methods, additionally, a convergence rates result is formulated. All the theoretical results are illustrated by some numerical examples.