Regularization Algorithms for Ill-Posed Problems, Bakushinsky Anatoly B., Kokurin Mikhail M., Kokurin Mikhail Yu
Автор: S.F. Gilyazov; N.L. Gol`dman Название: Regularization of Ill-Posed Problems by Iteration Methods ISBN: 9048153824 ISBN-13(EAN): 9789048153824 Издательство: Springer Рейтинг: Цена: 93160.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Iteration regularization, i.e., utilization of iteration methods of any form for the stable approximate solution of ill-posed problems, is one of the most important but still insufficiently developed topics of the new theory of ill-posed problems.
Автор: Michel Thera; Rainer Tichatschke Название: Ill-posed Variational Problems and Regularization Techniques ISBN: 3540663231 ISBN-13(EAN): 9783540663232 Издательство: Springer Рейтинг: Цена: 102480.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Developments in the field of ill-posed variational problems and variational inequalities are presented here. The work covers a large range of theoretical, numerical and practical aspects.
Автор: S.F. Gilyazov; N.L. Gol`dman Название: Regularization of Ill-Posed Problems by Iteration Methods ISBN: 0792361318 ISBN-13(EAN): 9780792361312 Издательство: Springer Рейтинг: Цена: 102480.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Iteration regularization, i.e., utilization of iteration methods of any form for the stable approximate solution of ill-posed problems, is one of the most important but still insufficiently developed topics of the new theory of ill-posed problems.
Автор: Mongi A. Abidi; Andrei V. Gribok; Joonki Paik Название: Optimization Techniques in Computer Vision ISBN: 3319463632 ISBN-13(EAN): 9783319463636 Издательство: Springer Рейтинг: Цена: 121110.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book presents practical optimization techniques used in image processing and computer vision problems. Ill-posed problems are introduced and used as examples to show how each type of problem is related to typical image processing and computer vision problems. Unconstrained optimization gives the best solution based on numerical minimization of a single, scalar-valued objective function or cost function. Unconstrained optimization problems have been intensively studied, and many algorithms and tools have been developed to solve them. Most practical optimization problems, however, arise with a set of constraints. Typical examples of constraints include: (i) pre-specified pixel intensity range, (ii) smoothness or correlation with neighboring information, (iii) existence on a certain contour of lines or curves, and (iv) given statistical or spectral characteristics of the solution. Regularized optimization is a special method used to solve a class of constrained optimization problems. The term regularization refers to the transformation of an objective function with constraints into a different objective function, automatically reflecting constraints in the unconstrained minimization process. Because of its simplicity and efficiency, regularized optimization has many application areas, such as image restoration, image reconstruction, optical flow estimation, etc.
Optimization plays a major role in a wide variety of theories for image processing and computer vision. Various optimization techniques are used at different levels for these problems, and this volume summarizes and explains these techniques as applied to image processing and computer vision.
Автор: Adrian Doicu; Thomas Trautmann; Franz Schreier Название: Numerical Regularization for Atmospheric Inverse Problems ISBN: 3642424015 ISBN-13(EAN): 9783642424014 Издательство: Springer Рейтинг: Цена: 174130.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Written by brilliant mathematicians, this research monograph presents and analyzes numerical algorithms for atmospheric retrieval, pulling together all the relevant material in a consistent, very powerful manner.
Автор: Heinz Werner Engl; Martin Hanke; A. Neubauer Название: Regularization of Inverse Problems ISBN: 0792341570 ISBN-13(EAN): 9780792341574 Издательство: Springer Рейтинг: Цена: 102480.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Offers an overview over some classes of inverse problems. This book deals with the mathematical theory of regularization methods and gives an account of the results about regularization methods both for linear and for nonlinear ill-posed problems. It considers both continuous and iterative regularization methods.
Автор: Flemming Название: Variational Source Conditions, Quadratic Inverse Problems, Sparsity Promoting Regularization ISBN: 3319952633 ISBN-13(EAN): 9783319952635 Издательство: Springer Рейтинг: Цена: 51230.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: The book collects and contributes new results on the theory and practice of ill-posed inverse problems. The new methods are applied to a difficult inverse problem from laser optics.Sparsity promoting regularization is examined in detail from a Banach space point of view.
Автор: Richard Huber Название: Variational Regularization for Systems of Inverse Problems ISBN: 3658253894 ISBN-13(EAN): 9783658253899 Издательство: Springer Рейтинг: Цена: 51230.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Tikhonov regularization is a cornerstone technique in solving inverse problems with applications in countless scienti?c ?elds. Richard Huber discusses a multi-parameter Tikhonov approach for systems of inverse problems in order to take advantage of their speci?c structure. Such an approach allows to choose the regularization weights of each subproblem individually with respect to the corresponding noise levels and degrees of ill-posedness.
Автор: Wang Yanfei, Yagola Anatoly G., Yang Changchun Название: Optimization and Regularization for Computational Inverse Problems and Applications ISBN: 3642137415 ISBN-13(EAN): 9783642137419 Издательство: Springer Рейтинг: Цена: 93160.00 T Наличие на складе: Нет в наличии. Описание: This book covers both the methods, including standard regularization theory, Fejer processes for linear and nonlinear problems, the balancing principle, extrapolated regularization, nonstandard regularization, nonlinear gradient method, the nonmonotone gradient method, subspace method and Lie group method;
Автор: Bruno Cordani Название: The Kepler Problem ISBN: 303489421X ISBN-13(EAN): 9783034894210 Издательство: Springer Рейтинг: Цена: 93160.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Because of the correspondences existing among all levels of reality, truths pertaining to a lower level can be considered as symbols of truths at a higher level and can therefore be the "foundation" or support leading by analogy to a knowledge of the latter.
Автор: Javier Roa Название: Regularization in Orbital Mechanics: Theory and Practice ISBN: 3110558556 ISBN-13(EAN): 9783110558555 Издательство: Walter de Gruyter Цена: 140090.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание:
Regularized equations of motion can improve numerical integration for the propagation of orbits, and simplify the treatment of mission design problems. This monograph discusses standard techniques and recent research in the area. While each scheme is derived analytically, its accuracy is investigated numerically. Algebraic and topological aspects of the formulations are studied, as well as their application to practical scenarios such as spacecraft relative motion and new low-thrust trajectories.
Автор: Thomas Schuster, Barbara Kaltenbacher, Bernd Hofmann, Kamil S. Kazimierski Название: Regularization Methods in Banach Spaces ISBN: 3110255243 ISBN-13(EAN): 9783110255249 Издательство: Walter de Gruyter Цена: 161100.00 T Наличие на складе: Невозможна поставка. Описание: Regularization methods aimed at finding stable approximate solutions are a necessary tool to tackle inverse and ill-posed problems. Inverse problems arise in a large variety of applications ranging from medical imaging and non-destructive testing via finance to systems biology. Many of these problems belong to the class of parameter identification problems in partial differential equations (PDEs) and thus are computationally demanding and mathematically challenging. Hence there is a substantial need for stable and efficient solvers for this kind of problems as well as for a rigorous convergence analysis of these methods. This monograph consists of five parts. Part I motivates the importance of developing and analyzing regularization methods in Banach spaces by presenting four applications which intrinsically demand for a Banach space setting and giving a brief glimpse of sparsity constraints. Part II summarizes all mathematical tools that are necessary to carry out an analysis in Banach spaces. Part III represents the current state-of-the-art concerning Tikhonov regularization in Banach spaces. Part IV about iterative regularization methods is concerned with linear operator equations and the iterative solution of nonlinear operator equations by gradient type methods and the iteratively regularized Gau?-Newton method. Part V finally outlines the method of approximate inverse which is based on the efficient evaluation of the measured data with reconstruction kernels.
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