Results 181 to 190 of about 101,252 (223)

A New k-Space Model for Non-Cartesian Fourier Imaging. [PDF]

IEEE Trans Comput Imaging
Chan CC, Haldar JP.
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Enhanced Inversion for Distributed Acoustic Sensing: A Robust Approach with HOLp-OGS Regularization. [PDF]

Sensors (Basel)
Xu W, Li J, Wu Y, Geng W, Gao B, Han L.
europepmc   +1 more source

Machine learning framework for oxytetracycline removal using nanostructured cupric oxide supported on magnetic chitosan alginate biocomposite.

Sci Rep
Rasoulzadeh H, Azarpira H, Pourakbar M, Sheikhmohammadi A, Rezagholizade-Shirvan A.   +4 more
europepmc   +1 more source

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Tikhonov Regularization and Randomized GSVD

SIAM Journal on Matrix Analysis and Applications, 2016
Summary: The generalized singular value decomposition (GSVD) is one of the essential tools in numerical linear algebra. This paper proposes a regularization method, combining Tikhonov regularization in general form with the truncated GSVD. Then the randomized algorithms are adopted to implement the truncation process.
Yimin Wei, Liping Zhang, Pengpeng Xie
exaly   +3 more sources

Iterated Tikhonov regularization with a general penalty term

Numerical Linear Algebra With Applications, 2017
Tikhonov regularization is one of the most popular approaches to solving linear discrete ill-posed problems. The choice of the regularization matrix may significantly affect the quality of the computed solution.
Marco Donatelli, Alessandro Buccini, Lothar Reichel   +2 more
exaly   +2 more sources

Tikhonov regularization based on generalized Krylov subspace methods

Applied Numerical Mathematics, 2012
We consider Tikhonov regularization of large linear discrete ill-posed problems with a regularization operator of general form and present an iterative scheme based on a generalized Krylov subspace method.
Qiang Ye, Fiorella Sgallari, Lothar Reichel   +2 more
exaly   +2 more sources

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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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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The Tikhonov regularization for vector equilibrium problems

Computational Optimization and Applications, 2021
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Lam Quoc Anh, Tran Quoc Duy, Le Dung Muu, Truong Van Tri   +3 more
openaire   +1 more source

Augmented Tikhonov regularization

Inverse Problems, 2008
This paper proposes a regularizing functional of Tikhonov type that determines the regularization parameter and the noise level along with the solutions for linear inverse problems in the Bayesian framework. The existence of minimizers to the functional is shown, and properties of the minimizers are studied.
Bangti Jin, Jun Zou
openaire   +1 more source