Oversmoothing regularization with $\ell^1$-penalty term

Oversmoothing regularization with $\ell^1$-penalty term

In Tikhonov-type regularization for ill-posed problems with noisy data, the penalty functionalis typically interpreted to carry a-priori information about the unknown true solution.We consider in this paper the case that the corresponding a-priori information is too strong such that thepenalty funct...

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Bibliographic Details
Main Authors: Daniel Gerth, Bernd Hofmann
Format: Article
Language:English
Published: AIMS Press 2019-08-01
Series:AIMS Mathematics
Subjects:
regularization
inverse problems
linear ill-posed operator equations
sparsity
$\ell^1$-regularization
Tikhonov functional
oversmoothing penalty
convergence rate
Online Access:https://www.aimspress.com/article/10.3934/math.2019.4.1223/fulltext.html

Internet

https://www.aimspress.com/article/10.3934/math.2019.4.1223/fulltext.html

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