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  E-mail Message: I thought you might be interested in this item at http://uwashington.worldcat.org/oclc/784708356 Title: Regularization methods in Banach spaces Author: Thomas Schuster Publisher: Berlin ; Boston : De Gruyter, ©2012. ISBN/ISSN: 9783110255249 3110255243 9783110255720 3110255723 9783112204504 ) 3112204506 (set (print + e-book) OCLC:784708356

  
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Subjects

• Banach spaces.
• Parameter estimation.
• Differential equations, Partial.
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Regularization methods in Banach spaces

Author:	Thomas Schuster; et al
Publisher:	Berlin ; Boston : De Gruyter, ©2012.
Series:	Radon series on computational and applied mathematics, 10.
Edition/Format:	Book : EnglishView all editions and formats
Summary:	"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."--Publisher's website.  Read more...