Buch, Englisch, 322 Seiten, Format (B × H): 160 mm x 240 mm, Gewicht: 1030 g
Buch, Englisch, 322 Seiten, Format (B × H): 160 mm x 240 mm, Gewicht: 1030 g
Reihe: Mathematics and Its Applications
ISBN: 978-0-7923-6140-4
Verlag: Springer Netherlands
This book is devoted to the mathematical theory of regularization methods and gives an account of the currently available results about regularization methods for linear and nonlinear ill-posed problems. Both continuous and iterative regularization methods are considered in detail with special emphasis on the development of parameter choice and stopping rules which lead to optimal convergence rates.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
- Technische Wissenschaften Maschinenbau | Werkstoffkunde Technische Mechanik | Werkstoffkunde Werkstoffkunde, Materialwissenschaft: Forschungsmethoden
- Mathematik | Informatik Mathematik Mathematische Analysis Reelle Analysis
- Mathematik | Informatik Mathematik Mathematische Analysis Moderne Anwendungen der Analysis
- Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Numerische Mathematik
- Technische Wissenschaften Maschinenbau | Werkstoffkunde Technische Mechanik | Werkstoffkunde Werkstoffprüfung
- Mathematik | Informatik Mathematik Mathematische Analysis Differentialrechnungen und -gleichungen
- Naturwissenschaften Chemie Physikalische Chemie
- Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Computeranwendungen in der Mathematik
Weitere Infos & Material
1. Introduction: Examples of Inverse Problems.- 1.1. Differentiation as an Inverse Problem.- 1.2. Radon Inversion (X-Ray Tomography).- 1.3. Examples of Inverse Problems in Physics.- 1.4. Inverse Problems in Signal and Image Processing.- 1.5. Inverse Problems in Heat Conduction.- 1.6. Parameter Identification.- 1.7. Inverse Scattering.- 2. Ill-Posed Linear Operator Equations.- 2.1. The Moore-Penrose Generalized Inverse.- 2.2. Compact Linear Operators: Singular Value Expansion.- 2.3. Spectral Theory and Functional Calculus.- 3. Regularization Operators.- 3.1. Definition and Basic Results.- 3.2. Order Optimality.- 3.3. Regularization by Projection.- 4. Continuous Regularization Methods.- 4.1. A-priori Parameter Choice Rules.- 4.2. Saturation and Converse Results.- 4.3. The Discrepancy Principle.- 4.4. Improved A-posteriori Rules.- 4.5. Heuristic Parameter Choice Rules.- 4.6. Mollifier Methods.- 5. Tikhonov Regularization.- 5.1. The Classical Theory.- 5.2. Regularization with Projection.- 5.3. Maximum Entropy Regularization.- 5.4. Convex Constraints.- 6. Iterative Regularization Methods.- 6.1. Landweber Iteration.- 6.2. Accelerated Landweber Methods.- 6.3. The ?-Methods.- 7. The Conjugate Gradient Method.- 7.1. Basic Properties.- 7.2. Stability and Convergence.- 7.3. The Discrepancy Principle.- 7.4. The Number of Iterations.- 8. Regularization With Differential Operators.- 8.1. Weighted Generalized Inverses.- 8.2. Regularization with Seminorms.- 8.3. Examples.- 8.4. Hilbert Scales.- 8.5. Regularization in Hilbert Scales.- 9. Numerical Realization.- 9.1. Derivation of the Discrete Problem.- 9.2. Reduction to Standard Form.- 9.3. Implementation of Tikhonov Regularization.- 9.4. Updating the Regularization Parameter.- 9.5. Implementation of Iterative Methods.- 10. TikhonovRegularization of Nonlinear Problems.- 10.1. Introduction.- 10.2. Convergence Analysis.- 10.3. A-posteriori Parameter Choice Rules.- 10.4. Regularization in Hilbert Scales.- 10.5. Applications.- 10.6. Convergence of Maximum Entropy Regularization.- 11. Iterative Methods for Nonlinear Problems.- 11.1. The Nonlinear Landweber Iteration.- 11.2. Newton Type Methods.- A. Appendix.- A.1. Weighted Polynomial Minimization Problems.- A.2. Orthogonal Polynomials.- A.3. Christoffel Functions.
