Inverse Problems: Tikhonov Theory And Algorithms
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A01=Bangti Jin
A01=Kazufumi Ito
Author_Bangti Jin
Author_Kazufumi Ito
Bayesian Inference
Category=PBF
Category=PBKD
Category=PBKJ
Category=PBW
Direct Inversion
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
Inverse Problems
Nonsmooth Optimization
Tikhonov Regularization
Product details
- ISBN 9789814596190
- Publication Date: 23 Oct 2014
- Publisher: World Scientific Publishing Co Pte Ltd
- Publication City/Country: SG
- Product Form: Hardback
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Inverse problems arise in practical applications whenever one needs to deduce unknowns from observables. This monograph is a valuable contribution to the highly topical field of computational inverse problems. Both mathematical theory and numerical algorithms for model-based inverse problems are discussed in detail. The mathematical theory focuses on nonsmooth Tikhonov regularization for linear and nonlinear inverse problems. The computational methods include nonsmooth optimization algorithms, direct inversion methods and uncertainty quantification via Bayesian inference.The book offers a comprehensive treatment of modern techniques, and seamlessly blends regularization theory with computational methods, which is essential for developing accurate and efficient inversion algorithms for many practical inverse problems.It demonstrates many current developments in the field of computational inversion, such as value function calculus, augmented Tikhonov regularization, multi-parameter Tikhonov regularization, semismooth Newton method, direct sampling method, uncertainty quantification and approximate Bayesian inference. It is written for graduate students and researchers in mathematics, natural science and engineering.
