| Regularization of Ill-Posed Problems by Iteration Methods 2000 Edition Contributor(s): Gilyazov, S. F. (Author), Gol'dman, N. L. (Author) |
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ISBN: 0792361318 ISBN-13: 9780792361312 Publisher: Springer OUR PRICE: $52.24 Product Type: Hardcover - Other Formats Published: December 1999 Annotation: This volume presents new results in regularization of ill-posed problems by iteration methods, which is one of the most important and rapidly developing topics of the theory of ill-posed problems. The new theoretical results are connected with the proposed united approach to the proof of regularizing properties of the classical' iteration methods (steepest descent, conjugate direction) complemented by the stopping rule depending on the level of errors in the input data. Much emphasis is given to the choice of the iteration index as the regularization parameter and to the rate convergence estimates of the approximate solutions. Results of calculations for important applications in non-linear thermophysics are also presented. Audience: This work will be a useful resource for specialists in the theory of partial differential and integral equations, in numerical analysis and in theory and methods. |
| Additional Information |
| BISAC Categories: - Mathematics | Differential Equations - Partial |
| Dewey: 515.353 |
| LCCN: 99058511 |
| Series: Mathematics and Its Applications |
| Physical Information: 0.81" H x 6.14" W x 9.21" (1.48 lbs) 342 pages |
| Descriptions, Reviews, Etc. |
| Publisher Description: 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. In this monograph, a general approach to the justification of iteration regulari- zation algorithms is developed, which allows us to consider linear and nonlinear methods from unified positions. Regularization algorithms are the 'classical' iterative methods (steepest descent methods, conjugate direction methods, gradient projection methods, etc.) complemented by the stopping rule depending on level of errors in input data. They are investigated for solving linear and nonlinear operator equations in Hilbert spaces. Great attention is given to the choice of iteration index as the regularization parameter and to estimates of errors of approximate solutions. Stabilizing properties such as smoothness and shape constraints imposed on the solution are used. On the basis of these investigations, we propose and establish efficient regularization algorithms for stable numerical solution of a wide class of ill-posed problems. In particular, descriptive regularization algorithms, utilizing a priori information about the qualitative behavior of the sought solution and ensuring a substantial saving in computational costs, are considered for model and applied problems in nonlinear thermophysics. The results of calculations for important applications in various technical fields (a continuous casting, the treatment of materials and perfection of heat-protective systems using laser and composite technologies) are given. |