The regularized global GMERR method for solving large-scale linear discrete ill-posed problems
From MaRDI portal
Publication:6630937
DOI10.4208/eajam.2023-161.081023MaRDI QIDQ6630937
Publication date: 31 October 2024
Published in: East Asian Journal on Applied Mathematics (Search for Journal in Brave)
multiple right-hand sideslinear discrete ill-posed problemsregularizing propertiesglobal GMERR method
Ill-posedness and regularization problems in numerical linear algebra (65F22) Iterative numerical methods for linear systems (65F10)
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- A GCV based Arnoldi-Tikhonov regularization method
- Regularization properties of Krylov iterative solvers CGME and LSMR for linear discrete ill-posed problems with an application to truncated randomized SVDs
- Oblique projection methods for linear systems with multiple right-hand sides
- Iterative regularization with minimum-residual methods
- Arnoldi-Tikhonov regularization methods
- The truncated SVD as a method for regularization
- A bidiagonalization algorithm for solving large and sparse ill-posed systems of linear equations
- On the stable implementation of the generalized minimal error method
- Regularization tools: A Matlab package for analysis and solution of discrete ill-posed problems
- Tikhonov regularization and the L-curve for large discrete ill-posed problems
- GMRES, L-curves, and discrete ill-posed problems
- On the regularizing properties of the GMRES method
- The block Lanczos algorithm for linear ill-posed problems
- Some results on the regularization of LSQR for large-scale discrete ill-posed problems
- Global FOM and GMRES algorithms for matrix equations
- The regularizing properties of global GMRES for solving large-scale linear discrete ill-posed problems with several right-hand sides
- Motivations and realizations of Krylov subspace methods for large sparse linear systems
- Arnoldi decomposition, GMRES, and preconditioning for linear discrete ill-posed problems
- Applications of regularized least squares to pattern classification
- On regularizing effects of MINRES and MR-II for large scale symmetric discrete ill-posed problems
- On the choice of subspace for large-scale Tikhonov regularization problems in general form
- IR tools: a MATLAB package of iterative regularization methods and large-scale test problems
- A generalized global Arnoldi method for ill-posed matrix equations
- Global least squares method (Gl-LSQR) for solving general linear systems with several right-hand sides
- Global Golub-Kahan bidiagonalization applied to large discrete ill-posed problems
- Choosing Regularization Parameters in Iterative Methods for Ill-Posed Problems
- Incremental Regularized Least Squares for Dimensionality Reduction of Large-Scale Data
- Optimal algorithms for linear ill-posed problems yield regularization methods
- LSMR: An Iterative Algorithm for Sparse Least-Squares Problems
- A Hybrid LSMR Algorithm for Large-Scale Tikhonov Regularization
- GMRES: A Generalized Minimal Residual Algorithm for Solving Nonsymmetric Linear Systems
- A Bidiagonalization-Regularization Procedure for Large Scale Discretizations of Ill-Posed Problems
- LSQR: An Algorithm for Sparse Linear Equations and Sparse Least Squares
- Solution of Sparse Indefinite Systems of Linear Equations
- Error-Minimizing Krylov Subspace Methods
- Hybrid Variational Model for Texture Image Restoration
- Regularizing algorithms based on the conjugate-gradient method
- Iterative Regularization and MINRES
- Structure Preserving Quaternion Generalized Minimal Residual Method
- Matrix Analysis and Computations
- Hybrid enriched bidiagonalization for discrete ill‐posed problems
- A Projection‐Based Approach to General‐Form Tikhonov Regularization
- A global Lanczos method for image restoration
This page was built for publication: The regularized global GMERR method for solving large-scale linear discrete ill-posed problems