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Regularization by dynamic programming
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Veröffentlicht/Copyright:
25. Juni 2007
We investigate continuous regularization methods for linear inverse problems of static and dynamic type. These methods are based on dynamic programming approaches for linear quadratic optimal control problems. We prove regularization properties and also obtain rates of convergence for our methods. A numerical example concerning a dynamical electrical impedance tomography (EIT) problem is used to illustrate the theoretical results.
Key Words: Regularization,; dynamic programming,; inverse problems,; Hamilton–Jacobi equation,; electrical impedance tomography,; dynamic inverse problems.
Published Online: 2007-06-25
Published in Print: 2007-06-19
Copyright 2007, Walter de Gruyter
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- Inverse problems and model validation: an example from latent virus reactivation
- Sensitivity analysis and parameter estimation for a model of Chlamydia Trachomatis infection
- Fast fully iterative Newton-type methods for inverse problems
- Use of extrapolation in regularization methods
- Regularization by dynamic programming
- Convergence analysis of an inexact iteratively regularized Gauss-Newton method under general source conditions
Schlagwörter für diesen Artikel
Regularization,;
dynamic programming,;
inverse problems,;
Hamilton–Jacobi equation,;
electrical impedance tomography,;
dynamic inverse problems.
Artikel in diesem Heft
- Inverse problems and model validation: an example from latent virus reactivation
- Sensitivity analysis and parameter estimation for a model of Chlamydia Trachomatis infection
- Fast fully iterative Newton-type methods for inverse problems
- Use of extrapolation in regularization methods
- Regularization by dynamic programming
- Convergence analysis of an inexact iteratively regularized Gauss-Newton method under general source conditions
