Results 221 to 230 of about 208,745 (278)

On simplified Tikhonov regularization

Journal of Optimization Theory and Applications, 1988
Approximations to the minimal norm, least-square solution of a linear equation with positive semidefinite operator are defined in such a way that fewer computations are needed than in Tikhonov's approach. We establish necessary and sufficient conditions for convergence, and we provide a choice for the regularization parameter \(\alpha\) that brings the
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Distributed Tikhonov regularization for ill-posed inverse problems from a Bayesian perspective

Computational optimization and applications
In this article, we exploit the similarities between Tikhonov regularization and Bayesian hierarchical models to propose a regularization scheme that acts like a distributed Tikhonov regularization where the amount of regularization varies from component
D. Calvetti, E. Somersalo
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Tikhonov regularization of metrically regular inclusions

Positivity, 2008
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Gaydu, Michaël, Geoffroy, Michel H.
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Whittaker–Stockwell Transform and Tikhonov Regularization Problem

Journal of Mathematical Sciences, 2022
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Soltani, F., Aledawish, S.
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An Improved Tikhonov Regularization Method for Lung Cancer Monitoring Using Electrical Impedance Tomography

IEEE Sensors Journal, 2019
Bedside monitoring plays an important role in the treatment of lung cancer. As a mostly used technique, X-ray computed tomography cannot provide medical surveillance for patients suffering from lung cancer in real-time.
Benyuan Sun, Shihong Yue, Zhenhua Hao, Z. Cui, Huaxiang Wang   +4 more
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Tikhonov-regularization-based projecting sparsity pursuit method for fluorescence molecular tomography reconstruction

, 2020
For fluorescence molecular tomography (FMT), image quality could be improved by incorporating a sparsity constraint. The L1 norm regularization method has been proven better than the L2 norm, like Tikhonov regularization. However, the Tikhonov method was
Jiaju Cheng, Jianwen Luo
semanticscholar   +1 more source

Tikhonov Regularization of Large Linear Problems

BIT Numerical Mathematics, 2003
The authors present a new numerical method for computing regularized solutions of Tikhonov regularization applied to large scale discrete linear ill-posed problems. The method is based on Lanczos bidiagonalization and Gauss quadrature. For choosing the regularization parameter the discrepancy principle is used.
Calvetti, Daniela, Reichel, Lothar
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A Regularization Parameter for Nonsmooth Tikhonov Regularization

SIAM Journal on Scientific Computing, 2011
In this paper we develop a novel rule for choosing regularization parameters in nonsmooth Tikhonov functionals. It is solely based on the value function and applicable to a broad range of nonsmooth models, and it extends one known criterion. A posteriori error estimates of the approximations are derived.
Kazufumi Ito, Bangti Jin, Tomoya Takeuchi   +2 more
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Tikhonov Fixed—Point Regularization

2000
The main purpose of this note is to propose viscosity approximation methods which amount to selecting a particular fixed-point of a given nonexpansive self mapping in a general Hilbert space. The connection with the selection principles of Attouch, in the context of convex minimization and monotone inclusion problems, is made and an application to a ...
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Projected nonstationary iterated Tikhonov regularization

BIT Numerical Mathematics, 2015
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Huang, Guangxin, Reichel, Lothar, Yin, Feng   +2 more
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