Results 141 to 150 of about 180,343 (197)

Optimal discretization of Ill-posed problems

Ukrainian Mathematical Journal, 2000
Summary: We present a review of results obtained in the Institute of Mathematics of National Ukrainian Academy of Sciences when investigating the optimal digitization of ill-posed problems.
Pereverzev, S. V., Solodkij, S. G.
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An Efficient Discretization Scheme for Solving Nonlinear Ill-Posed Problems

Computational Methods in Applied Mathematics, 2023
Abstract Information based complexity analysis in computing the solution of various practical problems is of great importance in recent years. The amount of discrete information required to compute the solution plays an important role in the computational complexity of the problem. Although this approach has been applied successfully for
M. P. Rajan, Jaise Jose
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Preconditioned RRGMRES for Discrete Ill-Posed Problems

International Journal of Computational Methods, 2018
Range Restricted GMRES (RRGMRES) can be considered as regularizing iterative method and its iterates as regularized solutions. In this paper, we use two preconditioned versions of this method for solving ill-posed inverse problems. Our proposed preconditioner matrix is appropriate to use directly for linear systems of the form [Formula: see text] with
Aminikhah, H., Yousefi, M.
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Subspace Preconditioned LSQR for Discrete Ill-Posed Problems

BIT Numerical Mathematics, 2003
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Jacobsen, M., Hansen, P. C., Saunders, M. A.   +2 more
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Solution of linear discrete ill-posed problems by discretized Chebyshev expansion

2021 21st International Conference on Computational Science and Its Applications (ICCSA), 2021
Large-scale linear discrete ill-posed problems are generally solved by Krylov subspace iterative methods. However, these methods can be difficult to implement so that they execute efficiently in a multiprocessor environment, because some of the computations have to be carried out sequentially.
Bai X., Buccini A., Reichel L.
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A special modified Tikhonov regularization matrix for discrete ill-posed problems

Applied Mathematics and Computation, 2020
In this paper, we investigate the solution of large-scale linear discrete ill-posed problems with error-contaminated data. If the solution exists, it is very sensitive to perturbations in the data.
Jingjing Cui, Guohua Peng, Quan Lu, Zhengge Huang   +3 more
semanticscholar   +1 more source

Lanczos-Based Exponential Filtering for Discrete Ill-Posed Problems

Numerical Algorithms, 2002
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Calvetti, D., Reichel, L.
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Regularization of Discrete Ill-Posed Problems

BIT Numerical Mathematics, 2004
Discrete approximations \( A_n u_n = f_n \) of an ill-posed equation (1) \( Au = f \) with a linear compact operator \( A: X \to X \) in a Hilbert space \( X \) are considered. Here, \( A_n: X_n \to X_n \) is a linear bounded operator in a finite-dimensional Hilbert space \( X_n \), where \( \{X_n,r_n,p_n\} \) is a convergent and stable discrete ...
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Lanczos based preconditioner for discrete ill-posed problems

Computing, 2010
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Rezghi, Mansoor, Hosseini, S. M.
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Moment discretization for ill-posed problems with discrete weakly bounded noise

GEM - International Journal on Geomathematics, 2012
The paper studies Tikhonov regularization for compact linear operator equations in Hilbert spaces with discrete data in the form of point evaluations of the right hand side. A noise model called weakly bounded noise is considered, the most important example of which is additive random noise.
Eggermont, P. P.B., LaRiccia, V. N., Nashed, M. Z.   +2 more
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