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Linear Inverse Problems and Tikhonov Regularization

class of linear inverse problems which are defined on Hilbert spaces. To explain why and how Tikhonov regularization works, the singular value;

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Inverse Problems

problems are discussed in detail. The mathematical theory focuses on nonsmooth Tikhonov regularization for linear and nonlinear inverse problems;

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Variational Regularization for Systems of Inverse Problems

Tikhonov regularization is a cornerstone technique in solving inverse problems with applications in countless scientific fields. Richard;

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Optimal Methods for Ill-Posed Problems

for constructing approximate solutions of linear operator equations of the first kind Tikhonov regularization method Projection;

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Numerical Methods for Inverse Problems

and imaging inverse problems. It includes a variety of numerical methods for solving inverse problems such as that of the Tikhonov;

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Regularization Algorithms for Ill-Posed Problems

operator equations, both linear and nonlinear. These approximation schemes form a basis for implementable numerical algorithms for the stable;

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Functional Analysis

applications of Hilbert spaces, including least squares approximation, inverse problems, and Tikhonov regularization, should appeal not only to;

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Inverse Theory and Applications in Geophysics

detailed exposition of the methods of regularized solution of inverse problems based on the ideas of Tikhonov regularization, and shows the;

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Theory of Reproducing Kernels and Applications

spaces are dealt with. Chapter 2 is the heart of this book.Chapter 3 is devoted to the Tikhonov regularization using the theory of reproducing;

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Inverse Problems

: discretization, regularization and practical solution of the regularized optimization problem. These steps are shown in detail for model problems from;

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Matrices, Moments and Quadrature with Applications

Tikhonov regularization. This book will interest researchers in numerical linear algebra and matrix computations, as well as scientists and;

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An Introduction to Data Analysis and Uncertainty Quantification for Inverse Problems

Inverse problems are found in many applications, such as medical imaging, engineering, astronomy, and geophysics, among others. To solve an;

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Inverse Problems in Engineering Mechanics III

problems are ill-posed and have to be overcome through development of new computational schemes, regularization techniques, objective functionals;

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Spline Models for Observational Data

the very important area of (Tikhonov) regularization methods for ill posed inverse problems. Convergence properties, data based smoothing;

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Computational Methods for Applied Inverse Problems

-statistical methods, e.g., regularization and optimization techniques for solving practical inverse problems.;

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An Introduction to the Mathematical Theory of Inverse Problems

ill-posed problems, analyzing basic properties of regularization methods for ill-posed problems via several simple analytical and numerical;

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An Introduction to the Mathematical Theory of Inverse Problems

ill-posed problems, analyzing basic properties of regularization methods for ill-posed problems via several simple analytical and numerical;

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Inverse Problems in Engineering Mechanics IV

have to be overcome through the development of new computational schemes, regularization techniques, objective functionals, and experimental;

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Linear Inverse Problems

The book describes a useful tool for solving linear inverse problems subject to convex constraints. The method of maximum entropy in the;

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Inverse Stefan Problems

in thermophysics and mechanics of continuous media. Regularization methods and algorithms are developed for the numerical solution of inverse Stefan;

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Inverse Sturm-Liouville Problems & Their Applications

This book presents the main results and methods on inverse spectral problems for Sturm-Liouville differential operators and their;

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Bayesian Approach to Inverse Problems

recover a quantity of interest from measurable data. Unfortunately, most inverse problems are ill-posed, which means that precise and stable;

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New Trends in Parameter Identification for Mathematical Models

regularization methods in Hilbert and Banach spaces for the stable approximate solution of ill-posed inverse problems, novel methods for parameter;

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Computational Methods for Electromagnetic Inverse Scattering

A comprehensive and updated overview of the theory, algorithms and applications of for electromagnetic inverse scattering problems;

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Computational Methods for Inverse Problems in Imaging

for smooth and nonsmooth convex minimization, on the use of structured (numerical) linear algebra, and on multilevel techniques. It also;

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Computational Methods for Inverse Problems in Imaging

for smooth and nonsmooth convex minimization, on the use of structured (numerical) linear algebra, and on multilevel techniques. It also;

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Inverse Problems

geophysics. The treatment is mathematically rigorous, relying on calculus and linear algebra only; familiarity with more advanced mathematical;

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