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Iterative Regularization Methods for Nonlinear Ill-Posed Problems
Abstract
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.
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Cited by
Inverse problems in systems biology
Engl, H. W., Flamm, C., Kügler, P., Lu, J., Müller, S., & Schuster, P. (2009) · Inverse Problems · doi:10.1088/0266-5611/25/12/123014 · Cited by 122
Metadata
- Authors
- Kaltenbacher, B., Neubauer, A., & Scherzer, O.
- Venue
- De Gruyter eBooks
- Publisher
- Walter de Gruyter
- Published
- 2008-05-20
- OpenAlex
- W1564864701
- Cited by
- 780
- References
- 0
- FWCI
- 13.91
- Type
- book
- Language
- en
- Open access
- closed
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- Kaltenbacher, B., Neubauer, A., & Scherzer, O. (2008). Iterative Regularization Methods for Nonlinear Ill-Posed Problems. De Gruyter eBooks. https://doi.org/10.1515/9783110208276
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