Error Estimates for Doubly-Generalized Tikhonov-Phillips Regularization

Authors

  • M. J. Carrió
  • G. L. Mazzieri
  • K. G. Temperini

DOI:

https://doi.org/10.5540/tcam.2022.024.01.00045

Keywords:

Inverse Problems, Tikhonov-Phillips, Error Estimate, Source Condition, Variational Inequality

Abstract

In this work, error estimates are presented for the case in which the regularized solution is obtained by minimizing doubly-generalized Tikhonov-Phillips functionals. The first result is based mainly on an assumption given by a source condition. It is proved that it is possible to replace this assumption by a variational inequality, obtaining analogous result of the error estimate. Finally, relationships are established between the optimality condition associated with the problem, the source condition and the variational inequality. On the other hand, it is known that, in certain cases, the use of two or more penalizing terms is useful. For this reason, generalizations of the results of error estimates are presented for cases in which the regularized solution is a minimizer of doubly-generalized Tikhonov-Phillips functionals with multiple penalizers.

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Published

2023-03-14

How to Cite

Carrió, M. J., Mazzieri, G. L., & Temperini, K. G. (2023). Error Estimates for Doubly-Generalized Tikhonov-Phillips Regularization. Trends in Computational and Applied Mathematics, 24(1), 45–61. https://doi.org/10.5540/tcam.2022.024.01.00045

Issue

Vol. 24 No. 1 (2023)

Section

Original Article

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