Computational Methods for Inverse Problems

Curtis R. Vogel

ISBN 10: 0898715504 / ISBN 13: 9780898715507

Published by Society for Industrial and Applied Mathematics,U.S., US, 1987

About this Item

Inverse 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.

Seller Inventory # LU-9780898715507

Bibliographic Details

• Title: Computational Methods for Inverse Problems
• Publisher: Society for Industrial and Applied Mathematics,U.S., US
• Publication Date: 1987
• Language: English
• ISBN 10: 0898715504
• ISBN 13: 9780898715507
• Binding: Paperback
• Condition: New
• Dimensions: 17.78W x 1.4H x 25.4D centimeters 17.78 centimeters width by 1.4 centimeters height by 25.4 centimeters depth
• Weight: 365 grams

About this title

Synopsis

Inverse 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.

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.