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Regularization Algorithms for Ill-Posed Problems

physics. The book presents in detail stable solution methods for ill-posed problems using the methodology of iterative regularization of classical;

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Regularization Methods for Ill-Posed Optimal Control Problems

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Optimal Methods for Ill-Posed Problems

The book covers fundamentals of the theory of optimal methods for solving ill-posed problems, as well as ways to obtain accurate and;

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Abstract Cauchy Problems

modern weak sense. Regularization methods provide approximate solutions to ill-posed problems. Although these approaches were extensively;

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Abstract Cauchy Problems

modern weak sense. Regularization methods provide approximate solutions to ill-posed problems. Although these approaches were extensively;

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Inverse Stefan Problems

boundaries. This new approach to the theory of ill-posed problems is useful for the modelling of nonlinear processes with phase transforms;

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Matrices, Moments and Quadrature with Applications

in iterative methods for solving linear systems and computing parameters in least squares and total least squares; and solving ill-posed problems using;

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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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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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Rank-Deficient and Discrete Ill-Posed Problems

for the analysis and solution of rank-deficient and discrete ill-posed problems. The emphasis is on insight into the stabilizing properties;

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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 and Ill-posed Problems

information about its solution. Inverse problems were first encountered in practice and are mostly ill-posed. The urgent need for their solution;

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Numerical Methods of Solving Ill-Posed Problems of Dielectric Spectrometry

Numerical Methods of Solving Ill-Posed Problems of Dielectric Spectrometry;

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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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Conjugate Gradient Type Methods for Ill-Posed Problems

) to ill posed problems and their regularization. Such problems occur in applications from almost all natural and technical sciences, including;

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Conjugate Gradient Type Methods for Ill-Posed Problems

) to ill posed problems and their regularization. Such problems occur in applications from almost all natural and technical sciences, including;

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Dynamical Systems Method and Applications

, well-posed or ill-posed. The book presents a systematic development of the DSM, and theoretical results are illustrated by a number;

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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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Ill-Posed Problems in Probability & Stability of Random Sums

This volume is concerned with the problems in probability and statistics. Ill-posed problems are usually understood as those results where;

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Optimization Techniques in Computer Vision

This book presents practical optimization techniques used in image processing and computer vision problems. Ill-posed problems are;

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Linear Inverse Problems and Tikhonov Regularization

Tikhonov regularization is the most popular general-purpose method for regularization, a mathematical technique to suppress the effect;

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Advanced Mechatronics

control of distributed systems is presented in the general framework of the recently developed ill-posed inverse problems. The book starts with an;

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Dynamical Systems Method for Solving Nonlinear Operator Equations

analysis, and ill-posed and inverse problems especially. The book presents a general method for solving operator equations, especially nonlinear;

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An Introduction to Data Analysis and Uncertainty Quantification for Inverse Problems

sources of error introduced by approximate mathematical models, regularization methods, numerical approximations for efficient computations, noisy;

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Regularization and Bayesian Methods for Inverse Problems in Signal and Image Processing

The focus of this book is on ill-posed inverse problems . These problems cannot be solved only on the basis of observed data. The building;

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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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