Results 41 to 50 of about 180,343 (197)

A GCV based Arnoldi-Tikhonov regularization method

, 2013
For the solution of linear discrete ill-posed problems, in this paper we consider the Arnoldi-Tikhonov method coupled with the Generalized Cross Validation for the computation of the regularization parameter at each iteration.
Novati, Paolo, Russo, Maria Rosaria
core   +1 more source

Comparison of A-posteriori parameter choice rules for linear discrete ill-posed problems

Journal of Computational and Applied Mathematics, 2020
Tikhonov regularization is one of the most popular methods for computing approximate solutions of linear discrete ill-posed problems with error-contaminated data.
A. Buccini, Yonggi Park, L. Reichel
semanticscholar   +1 more source

Some matrix nearness problems suggested by Tikhonov regularization

, 2016
The numerical solution of linear discrete ill-posed problems typically requires regularization, i.e., replacement of the available ill-conditioned problem by a nearby better conditioned one.
Noschese, Silvia, Reichel, Lothar
core   +1 more source

Fully discrete finite element data assimilation method for the heat equation [PDF]

, 2017
We consider a finite element discretization for the reconstruction of the final state of the heat equation, when the initial data is unknown, but additional data is given in a sub domain in the space time.
Burman, Erik, Ish-Horowicz, Jonathan, Oksanen, Lauri   +2 more
core   +3 more sources

A two-dimensional backward heat problem with statistical discrete data [PDF]

, 2017
We focus on the nonhomogeneous backward heat problem of finding the initial temperature θ =
Minh, Nguyen Dang, To, DUC KHANH, Trong, Dang Duc, Tuan, Nguyen Huy   +3 more
core   +1 more source

Calculation of Relaxation Spectra from Stress Relaxation Measurements [PDF]

, 2010
Application of stress on materials increases the energy of the system. After removal of stress, macromolecules comprising the material shift towards equilibrium to minimize the total energy of the system.
Kontogiorgos, Vassilis
core   +2 more sources

Some results on the regularization of LSQR for large-scale discrete ill-posed problems [PDF]

, 2015
LSQR, a Lanczos bidiagonalization based Krylov subspace iterative method, and its mathematically equivalent conjugate gradient for least squares problems (CGLS) applied to normal equations system, are commonly used for large-scale discrete ill-posed ...
Yi Huang, Zhongxiao Jia
semanticscholar   +1 more source

On Singular Bayesian Inference of Underdetermined Quantities—Part I: Invariant Discrete Ill-Posed Inverse Problems in Small and Large Dimensions

Physical Sciences Forum
When the quantities of interest remain underdetermined a posteriori, we would like to draw inferences for at least one particular solution. Can we do so in a Bayesian way? What is a probability distribution over an underdetermined quantity? How do we get
Fabrice Pautot
doaj   +1 more source

Tensor Conjugate Gradient Methods with Automatically Determination of Regularization Parameters for Ill-Posed Problems with t-Product

Mathematics
Ill-posed problems arise in many areas of science and engineering. Tikhonov is a usual regularization which replaces the original problem by a minimization problem with a fidelity term and a regularization term.
Shi-Wei Wang, Guang-Xin Huang, Feng Yin
doaj   +1 more source

Estimation of the Regularization Parameter in Linear Discrete Ill-Posed Problems Using the Picard parameter

, 2017
Accurate determination of the regularization parameter in inverse problems still represents an analytical challenge, owing mainly to the considerable difficulty to separate the unknown noise from the signal.
Levin, Eitan, Meltzer, Alexander Y.
core   +1 more source