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Inverse problems : Tikhonov theory and algorithms / Kazufumi Ito and Bangti Jin.

By: Contributor(s): Material type: TextTextSeries: Series on applied mathematics ; v 22.Publication details: New Jersey : World Scientific, 2014.Description: x, 319 p. : illustrations ; 24 cmISBN:
  • 9789814596190 (hardcover : alk. paper)
Subject(s): DDC classification:
  • 515.357 23 It89
Contents:
1. Introduction-- 2. Models in inverse problems-- 3. Tikhonov theory for linear problems-- 4. Tikhonov theory for nonlinear inverse problems-- 5. Nonsmooth optimization-- 6. Direct inversion methods-- 7. Bayesian inference-- Appendixes-- Bibliography-- Index.
Summary: 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.
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Holdings
Item type Current library Call number Status Date due Barcode Item holds
Books ISI Library, Kolkata 515.357 It89 (Browse shelf(Opens below)) Available 136512
Total holds: 0

Includes bibliographical references and index.

1. Introduction--
2. Models in inverse problems--
3. Tikhonov theory for linear problems--
4. Tikhonov theory for nonlinear inverse problems--
5. Nonsmooth optimization--
6. Direct inversion methods--
7. Bayesian inference--
Appendixes--
Bibliography--
Index.

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.

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