Iteration methods for convexly constrained ill-posed problems in hilbert space
作者:
Bertolt Eicke,
期刊:
Numerical Functional Analysis and Optimization
(Taylor Available online 1992)
卷期:
Volume 13,
issue 5-6
页码: 413-429
ISSN:0163-0563
年代: 1992
DOI:10.1080/01630569208816489
出版商: Marcel Dekker, Inc.
数据来源: Taylor
摘要:
Minimization problems in Hilbert space with quadratic objective function and closed convex constraint setCare considered. In case the minimum is not unique we are looking for the solution of minimal norm. If a problem is ill-posed, i.e. if the solution does not depend continuously on the data, and if the data are subject to errors then it has to be solved by means of regularization methods. The regularizing properties of some gradient projection methods—i.e. convergence for exact data, order of convergence under additional assumptions on the solution and stability for perturbed data—are the main issues of this paper.
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