In verse problems arise in a number of important practical applications, ranging from biomedical imaging to seismic prospecting. This book provides the reader with a basic understanding of both the underlying mathematics and the computational methods used to solve inverse problems. It also addresses specialized topics like image reconstruction, parameter identification, total variation methods, nonnegativity constraints, and regularization parameter selection methods. Because inverse problems typically involve the estimation of certain quantities based on indirect measurements, the estimation process is often ill-posed. Regularization methods, which have been developed to deal with this ill-posedness, are carefully explained in the early chapters of Computational Methods for Inverse Problems. The book also integrates mathematical and statistical theory with applications and practical computational methods, including topics like maximum likelihood estimation and Bayesian estimation. Several web-based resources are available to make this monograph interactive, including a collection of MATLAB m-files used to generate many of the examples and figures.
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Book Description
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Book Description
Provides the reader with a basic understanding of both the underlying mathematics and the computational methods used to solve inverse problems. It also addresses specialized topics like image reconstruction, parameter identification, total variation methods, nonnegativity constraints, and regularization parameter selection methods.
--This text refers to the
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About the Author
Curtis R. Vogel is a Professor in the Department of Mathematical Sciences at Montana State University. His research interests include numerical analysis, mathematical modeling, optimization, inverse and ill-posed problems, and scientific computing. He has written many refereed articles and reports on these topics.
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