Choosing regularization parameters in iterative methods for ill-posed problems (Q2719194)
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scientific article; zbMATH DE number 1608858
| Language | Label | Description | Also known as |
|---|---|---|---|
| English | Choosing regularization parameters in iterative methods for ill-posed problems |
scientific article; zbMATH DE number 1608858 |
Statements
21 June 2001
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ill-posed problems
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Tikhonov regularization
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discrepancy principle
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iterative methods
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L-curve
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truncated singular value decomposition
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Krylov subspace method
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numerical examples
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Choosing regularization parameters in iterative methods for ill-posed problems (English)
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A common framework is presented for efficient algorithms that regularize after the application of a Krylov subspace method to project the problem into much smaller dimensional space, rather than before it. It is shown that the new algorithm often involves less computational costs. The approximate equivalence of this approach to other forms of regularization is examined, and numerical examples are presented.
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