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Iterative regularization methods for nonlinear ill-posed problems

Author: Barbara Kaltenbacher; Andreas Neubauer; Otmar Scherzer
Publisher: Berlin ; New York : Walter de Gruyter, ©2008.
Series: Radon series on computational and applied mathematics, 6.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Database:WorldCat
Summary:
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  Read more...
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Kaltenbacher, Barbara.
Iterative regularization methods for nonlinear ill-posed problems.
Berlin ; New York : Walter de Gruyter, ©2008
(OCoLC)232124307
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Barbara Kaltenbacher; Andreas Neubauer; Otmar Scherzer
ISBN: 9783110208276 311020827X 9783110204209 3110204207
OCLC Number: 301944889
Reproduction Notes: Electronic reproduction. [S.l.] : HathiTrust Digital Library, 2011. MiAaHDL
Description: 1 online resource (viii, 200 pages) : illustrations.
Details: Master and use copy. Digital master created according to Benchmark for Faithful Digital Reproductions of Monographs and Serials, Version 1. Digital Library Federation, December 2002.
Contents: Frontmatter; Contents; 1 Introduction; 2 Nonlinear Landweber iteration; 3 Modified Landweber methods; 4 Newton type methods; 5 Multilevel methods; 6 Level set methods; 7 Applications; 8 Comments; Backmatter.
Series Title: Radon series on computational and applied mathematics, 6.
Responsibility: Barbara Kaltenbacher, Andreas Neubauer, Otmar Scherzer.
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Abstract:

Nonlinear inverse problems appear in many applications, and typically they lead to mathematical models that are ill-posed, that is they are unstable under data perturbations. Those problems require a  Read more...

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"This well written monograph may become a standard reference on regularization theory for nonlinear inverse problems."Thorsten Hohage in: Mathematical Reviews 2010c

 
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