Results 31 to 40 of about 208,745 (278)

Convergence Rates for Inverse Problems with Impulsive Noise [PDF]

, 2014
We study inverse problems F(f) = g with perturbed right hand side g^{obs} corrupted by so-called impulsive noise, i.e. noise which is concentrated on a small subset of the domain of definition of g. It is well known that Tikhonov-type regularization with
Hohage, Thorsten, Werner, Frank
core   +1 more source

Iterated fractional Tikhonov regularization [PDF]

Inverse Problems, 2015
AbstractWe consider linear operator equations of the form where $K:{\cal X}\to{\cal Y}$ is a compact linear operator between Hilbert spaces ${\cal X} \hbox{ and } {\cal Y}.$ We assume y to be attainable, i.e., that problem (1) has a solution x† = K†y of minimal norm.
Bianchi, Davide, Buccini, Alessandro, DONATELLI, MARCO, SERRA CAPIZZANO, STEFANO   +3 more
openaire   +4 more sources

Intelligent Particle Swarm Optimization Method for Parameter Selecting in Regularization Method for Integral Equation [PDF]

BIO Web of Conferences
We use the Tikhonov method as a regularization technique for solving the integral equation of the first kind with noisy and noise-free data. Following that, we go over how to choose the Tikhonov regularization parameter by implementing the Intelligent ...
Al-Mahdawi H.K., Hatem Khorsheed Farah, Subhi Alhumaima Ali, Ramadhan Ali J., Hussien Kilan M, Alkattan Hussein   +5 more
doaj   +1 more source

Fractional regularization matrices for linear discrete ill-posed problems [PDF]

, 2015
The numerical solution of linear discrete ill-posed problems typically requires regularization. Two of the most popular regularization methods are due to Tikhonov and Lavrentiev. These methods require the choice of a regularization matrix. Common choices
Lothar Reichel, Michiel E. Hochstenbach, NOSCHESE, Silvia   +2 more
core   +1 more source

Arnoldi–Tikhonov regularization methods

Journal of Computational and Applied Mathematics, 2009
The problem is to solve a large, ill-conditioned linear system \(Ax=b\) of size \(n\), where \(b=\hat{b}+e\) with \(\hat{b}\) the ``true'' vector and \(e\) some error. Tikhonov regularization minimizes \(\|Ax-b\|^2+\mu^{-1}\|x\|\) with \(\mu\) a regularization parameter.
Lewis, Bryan, Reichel, Lothar
openaire   +1 more source

An Adaptive Image Denoising Model Based on Tikhonov and TV Regularizations

Advances in Multimedia, 2014
To avoid the staircase artifacts, an adaptive image denoising model is proposed by the weighted combination of Tikhonov regularization and total variation regularization.
Kui Liu, Jieqing Tan, Benyue Su
doaj   +1 more source

Regularization matrices determined by matrix nearness problems [PDF]

, 2016
This paper is concerned with the solution of large-scale linear discrete ill-posed problems with error-contaminated data. Tikhonov regularization is a popular approach to determine meaningful approximate solutions of such problems.
Brezinski, Calvetti, Calvetti, Calvetti, Donatelli, Donatelli, Dykes, Eldén, Engl, Gazzola, Guangxin Huang, Hansen, Hansen, Hansen, Kindermann, Lewis, Lothar Reichel, Morigi, Neuman, Noschese, Phillips, Reichel, Reichel, Silvia Noschese   +23 more
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Multidirectional Subspace Expansion for One-Parameter and Multiparameter Tikhonov Regularization [PDF]

, 2016
Tikhonov regularization is a popular method to approximate solutions of linear discrete ill-posed problems when the observed or measured data is contaminated by noise.
C Brezinski, C Groetsch, CC Paige, D Calvetti, D Fong, GH Golub, Ian N. Zwaan, J Lampe, K Kunisch, L Reichel, L Reichel, L Reichel, M Belge, M Fornasier, ME Hochstenbach, ME Hochstenbach, ME Kilmer, Michiel E. Hochstenbach, P Perona, PC Hansen, PC Hansen, RC Li, S Gazzola, S Lu, S Lu   +24 more
core   +3 more sources

Projected Newton Method for noise constrained Tikhonov regularization

, 2020
Tikhonov regularization is a popular approach to obtain a meaningful solution for ill-conditioned linear least squares problems. A relatively simple way of choosing a good regularization parameter is given by Morozov's discrepancy principle.
Cornelis, Jeffrey, Schenkels, Nick, Vanroose, Wim   +2 more
core   +1 more source

On fractional Tikhonov regularization [PDF]

Journal of Inverse and Ill-posed Problems, 2015
Abstract It is well known that Tikhonov regularization in standard form may determine approximate solutions that are too smooth, i.e., the approximate solution may lack many details that the desired exact solution might possess. Two different approaches, both referred to as fractional Tikhonov methods have been introduced to remedy this ...
Gerth, Daniel, Klann, Esther, Ramlau, Ronny, Reichel, Lothar   +3 more
openaire   +1 more source