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On reduction of informational expenses in solving ill-posed problems with not exactly given input data
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S. G. Solodky
Published/Copyright:
May 9, 2008
Abstract
A class of approximate methods to solve operator equations of first kind with not exactly given input data is constructed. For involved methods their optimality by the order on sets of sourcewise represented solutions is proved and the bound of informational expenses is obtained. These algorithms are numerically implemented in an efficient way. An example of application of two such algorithms is given.
Key words.: Ill-posed problems; regularization method; discretization; projection scheme; discrepancy principle
Received: 2006-08-10
Published Online: 2008-05-09
Published in Print: 2008-March
© de Gruyter 2008
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Articles in the same Issue
- The multidimensional refinement indicators algorithm for optimal parameterization
- Inverse problem for the Schrödinger operator in an unbounded strip
- Identification of two memory kernels in a fully hyperbolic phase-field system
- A priori weighting for parameter estimation
- On reduction of informational expenses in solving ill-posed problems with not exactly given input data
Keywords for this article
Ill-posed problems;
regularization method;
discretization;
projection scheme;
discrepancy principle
Articles in the same Issue
- The multidimensional refinement indicators algorithm for optimal parameterization
- Inverse problem for the Schrödinger operator in an unbounded strip
- Identification of two memory kernels in a fully hyperbolic phase-field system
- A priori weighting for parameter estimation
- On reduction of informational expenses in solving ill-posed problems with not exactly given input data
