Computing covariance matrices for constrained nonlinear large scale parameter estimation problems using Krylov subspace methods
In the paper we show how, based on the preconditioned Krylov subspace methods, to compute the covariance matrix of parameter estimates, which is crucial for efficient methods of optimum experimental design.
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| Main Authors: | , |
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| Format: | Chapter/Article |
| Language: | English |
| Published: |
2012
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| In: |
Constrained Optimization and Optimal Control for Partial Differential Equations
Year: 2011, Pages: 197-212 |
| DOI: | 10.1007/978-3-0348-0133-1_11 |
| Online Access: | Resolving-System, Volltext: http://dx.doi.org/10.1007/978-3-0348-0133-1_11 Verlag, Volltext: https://link.springer.com/chapter/10.1007/978-3-0348-0133-1_11 
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| Author Notes: | Ekaterina Kostina, Olga Kostyukova |
| Summary: | In the paper we show how, based on the preconditioned Krylov subspace methods, to compute the covariance matrix of parameter estimates, which is crucial for efficient methods of optimum experimental design. |
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| Item Description: | First online: 28 October 2011 Gesehen am 03.08.2018 |
| Physical Description: | Online Resource |
| ISBN: | 9783034801331 |
| DOI: | 10.1007/978-3-0348-0133-1_11 |