Results 31 to 40 of about 180,343 (197)

Approximation accuracy of the Krylov subspaces for linear discrete ill-posed problems [PDF]

Journal of Computational and Applied Mathematics, 2018
For the large-scale linear discrete ill-posed problem min ‖ A x − b ‖ or A x = b with b contaminated by Gaussian white noise, the Lanczos bidiagonalization based Krylov solver LSQR and its mathematically equivalent CGLS, the Conjugate Gradient (CG ...
Zhongxiao Jia
semanticscholar   +1 more source

A hybrid splitting method for smoothing Tikhonov regularization problem

Journal of Inequalities and Applications, 2016
In this paper, a hybrid splitting method is proposed for solving a smoothing Tikhonov regularization problem. At each iteration, the proposed method solves three subproblems.
Yu-Hua Zeng, Zheng Peng, Yu-Fei Yang
doaj   +1 more source

Solving ill-posed magnetic inverse problem using a Parameterized Trust-Region Sub-problem

Contributions to Geophysics and Geodesy, 2013
The aim of this paper is to find a plausible and stable solution for the inverse geophysical magnetic problem. Most of the inverse problems in geophysics are considered as ill-posed ones.
Maha ABDELAZEEM Mohamed
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
core   +2 more sources

Arnoldi decomposition, GMRES, and preconditioning for linear discrete ill-posed problems [PDF]

Applied Numerical Mathematics, 2018
GMRES is one of the most popular iterative methods for the solution of large linear systems of equations that arise from the discretization of linear well-posed problems, such as Dirichlet boundary value problems for elliptic partial differential ...
S. Gazzola, S. Noschese, P. Novati, L. Reichel   +3 more
semanticscholar   +1 more source

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

A novel modified TRSVD method for large-scale linear discrete ill-posed problems

, 2020
The truncated singular value decomposition (TSVD) is a popular method for solving linear discrete ill-posed problems with a small to moderately sized matrix A.
Xianglan Bai, G. Huang, Xiao-Jun Lei, L. Reichel, Fengli Yin   +4 more
semanticscholar   +1 more source

A new nonstationary preconditioned iterative method for linear discrete ill‐posed problems with application to image deblurring

Numerical Linear Algebra with Applications, 2020
Discrete ill‐posed inverse problems arise in many areas of science and engineering. Their solutions are very sensitive to perturbations in the data. Regularization methods aim at reducing this sensitivity.
A. Buccini, M. Donatelli, L. Reichel, Wei-Hong Zhang   +3 more
semanticscholar   +1 more source

Regularization parameter determination for discrete ill-posed problems

Journal of Computational and Applied Mathematics, 2015
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Hochstenbach, ME, Reichel, L, RODRIGUEZ, GIUSEPPE   +2 more
openaire   +3 more sources

Inexact searches on the L-curve

Publicaciones en Ciencias y Tecnología, 2009
In a Tikhonov regularization scheme to solve discrete linear ill-posed problems, selecting the parameter value is a key task. We use Wolfe inexact search on the L-curve to choose a λ regularization parameter value far from critical areas of the L-curve ...
Hugo Lara Urdaneta, Adalys Alvarez, Freddy Torrealba Anzola   +2 more
doaj   +2 more sources