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.
@article{arxiv.2101.10325,
title = {Regularization by dynamic programming},
author = {S. Kindermann and A. Leitao},
journal= {arXiv preprint arXiv:2101.10325},
year = {2021}
}
Comments
17 pages, 2 figures. arXiv admin note: substantial text overlap with arXiv:2101.09327; text overlap with arXiv:2101.09339