Generalized Tikhonov Regularization and Modern Convergence Rate Theory in Banach Spaces
Author: Jens Flemming
Publisher:
Published: 2012
Total Pages: 296
ISBN-13: 9783844008678
DOWNLOAD EBOOK →Author: Jens Flemming
Publisher:
Published: 2012
Total Pages: 296
ISBN-13: 9783844008678
DOWNLOAD EBOOK →Author: Torsten Hein
Publisher: Logos Verlag Berlin GmbH
Published: 2010
Total Pages: 174
ISBN-13: 3832527451
DOWNLOAD EBOOK →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.
Author: Kazufumi Ito
Publisher: World Scientific
Published: 2014-08-28
Total Pages: 330
ISBN-13: 9814596213
DOWNLOAD EBOOK →Inverse problems arise in practical applications whenever one needs to deduce unknowns from observables. This monograph is a valuable contribution to the highly topical field of computational inverse problems. Both mathematical theory and numerical algorithms for model-based inverse problems are discussed in detail. The mathematical theory focuses on nonsmooth Tikhonov regularization for linear and nonlinear inverse problems. The computational methods include nonsmooth optimization algorithms, direct inversion methods and uncertainty quantification via Bayesian inference.The book offers a comprehensive treatment of modern techniques, and seamlessly blends regularization theory with computational methods, which is essential for developing accurate and efficient inversion algorithms for many practical inverse problems.It demonstrates many current developments in the field of computational inversion, such as value function calculus, augmented Tikhonov regularization, multi-parameter Tikhonov regularization, semismooth Newton method, direct sampling method, uncertainty quantification and approximate Bayesian inference. It is written for graduate students and researchers in mathematics, natural science and engineering.
Author: Kamil S. Kazimierski
Publisher: Logos Verlag Berlin GmbH
Published: 2010
Total Pages: 149
ISBN-13: 3832527311
DOWNLOAD EBOOK →In recent years there has been an increasing interest in the regularization of ill-posed inverse problems for operators mapping between two Banach spaces. This thesis focuses on the case of linear, continuous operators and Banach spaces, which are convex of power type and/or smooth of power type. The main aim is to present new results regarding the Tikhonov regularization and the Landweber regularization, some of which are: convexity and smoothness properties of the wavelet characterization of the norm of Besov spaces, generalization of the discrepancy principle of Engl to the setting of Banach spaces, convergence rates for two minimization methods for the Tikhonov functional, adaptation of the Landweber iteration to Banach spaces convex of power type and smooth of power type and introduction of a modified version of the Landweber iteration. The quality of the algorithms introduced in this thesis is discussed with help of several numerical examples.
Author: Roderick Melnik
Publisher: John Wiley & Sons
Published: 2015-05-18
Total Pages: 321
ISBN-13: 1118853989
DOWNLOAD EBOOK →Mathematical and Computational Modeling Illustrates the application of mathematical and computational modeling in a variety of disciplines With an emphasis on the interdisciplinary nature of mathematical and computational modeling, Mathematical and Computational Modeling: With Applications in the Natural and Social Sciences, Engineering, and the Arts features chapters written by well-known, international experts in these fields and presents readers with a host of state-of-theart achievements in the development of mathematical modeling and computational experiment methodology. The book is a valuable guide to the methods, ideas, and tools of applied and computational mathematics as they apply to other disciplines such as the natural and social sciences, engineering, and technology. The book also features: Rigorous mathematical procedures and applications as the driving force behind mathematical innovation and discovery Numerous examples from a wide range of disciplines to emphasize the multidisciplinary application and universality of applied mathematics and mathematical modeling Original results on both fundamental theoretical and applied developments in diverse areas of human knowledge Discussions that promote interdisciplinary interactions between mathematicians, scientists, and engineers Mathematical and Computational Modeling: With Applications in the Natural and Social Sciences, Engineering, and the Arts is an ideal resource for professionals in various areas of mathematical and statistical sciences, modeling and simulation, physics, computer science, engineering, biology and chemistry, and industrial and computational engineering. The book also serves as an excellent textbook for graduate courses in mathematical modeling, applied mathematics, numerical methods, operations research, and optimization.
Author: Heinz H. Bauschke
Publisher: Springer Nature
Published: 2019-11-06
Total Pages: 489
ISBN-13: 3030259390
DOWNLOAD EBOOK →This book brings together research articles and state-of-the-art surveys in broad areas of optimization and numerical analysis with particular emphasis on algorithms. The discussion also focuses on advances in monotone operator theory and other topics from variational analysis and nonsmooth optimization, especially as they pertain to algorithms and concrete, implementable methods. The theory of monotone operators is a central framework for understanding and analyzing splitting algorithms. Topics discussed in the volume were presented at the interdisciplinary workshop titled Splitting Algorithms, Modern Operator Theory, and Applications held in Oaxaca, Mexico in September, 2017. Dedicated to Jonathan M. Borwein, one of the most versatile mathematicians in contemporary history, this compilation brings theory together with applications in novel and insightful ways.
Author: Thomas Schuster
Publisher: Walter de Gruyter
Published: 2012-07-30
Total Pages: 296
ISBN-13: 3110255723
DOWNLOAD EBOOK →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.
Author: Jens Flemming
Publisher: Springer
Published: 2018-09-08
Total Pages: 182
ISBN-13: 3319952641
DOWNLOAD EBOOK →The book collects and contributes new results on the theory and practice of ill-posed inverse problems. Different notions of ill-posedness in Banach spaces for linear and nonlinear inverse problems are discussed not only in standard settings but also in situations up to now not covered by the literature. Especially, ill-posedness of linear operators with uncomplemented null spaces is examined.Tools for convergence rate analysis of regularization methods are extended to a wider field of applicability. It is shown that the tool known as variational source condition always yields convergence rate results. A theory for nonlinear inverse problems with quadratic structure is developed as well as corresponding regularization methods. The new methods are applied to a difficult inverse problem from laser optics.Sparsity promoting regularization is examined in detail from a Banach space point of view. Extensive convergence analysis reveals new insights into the behavior of Tikhonov-type regularization with sparsity enforcing penalty.
Author: Stephan W. Anzengruber
Publisher: Sudwestdeutscher Verlag Fur Hochschulschriften AG
Published: 2012-04
Total Pages: 136
ISBN-13: 9783838131351
DOWNLOAD EBOOK →This book is about inverse problems. A classical example of such an inverse problem is computerized tomography (CT), where one attempts to recover an image of, say, the human brain from measured x-ray intensities. Such measurements will in general be noisy, but even very little noise can have a severe impact on the quality of the reconstructed images. To overcome these difficulties regularization methods have been developed, which stabilize the reconstruction process. We study here one such method, known as Tikhonov-regularization, and prove general regularizing properties as well as rates on the speed of the convergence.