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Inverse Problems: Tikhonov Theory And Algorithms (Series On Applied Mathematics)

By: Bangti Jin (Author) , Kazufumi Ito (Author)

Extended Catalogue

Ksh 26,600.00

Format: Hardback or Cased Book

ISBN-10: 9814596191

ISBN-13: 9789814596190

Collection / Series: Series On Applied Mathematics

Collection Type: Publisher collection

Publisher: World Scientific Publishing Co Pte Ltd

Imprint: World Scientific Publishing Co Pte Ltd

Country of Manufacture: GB

Country of Publication: GB

Publication Date: Oct 23rd, 2014

Publication Status: Active

Product extent: 332 Pages

Weight: 606.00 grams

Dimensions (height x width x thickness): 23.70 x 16.30 x 2.20 cms

Product Classification / Subject(s): Applied mathematics

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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.

 

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.


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