Regularization by dynamic programming

S. Kindermann; A. Leitao

doi:10.1515/jiip.2007.016

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arXiv · 2101.10325

Regularization by dynamic programming

S. Kindermann·A. Leitao

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Abstract

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.

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S. Kindermann, A. Leitao. 2021-01-22. Regularization by dynamic programming. https://doi.org/10.1515/jiip.2007.016

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