Inverse problems need to be solved in order to properly interpret indirect measurements. Often, inverse problems are ill-posed and sensitive to data errors. Therefore one has to incorporate some sort of regularization to reconstruct significant information from the given data.
This book presents the main achievements that have emerged in regularization theory over the past 50 years, focusing on linear ill-posed problems and the development of methods that can be applied to them. Some of this material has previously appeared only in journal articles.
A Taste of Inverse Problems: Basic Theory and Examples
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Martin Hanke is a Professor of Mathematics at the Johannes Gutenberg-University in Mainz, Germany. He works in numerical analysis and his research focuses on inverse and ill-posed problems, the development of general regularization methods, and the analysis of sophisticated algorithms for specific inverse problems.
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