Estimating error norms in CG-like algorithms for least-squares and least-norm problems
From MaRDI portal
Publication:6590591
DOI10.1007/s11075-023-01691-xMaRDI QIDQ6590591
Publication date: 21 August 2024
Published in: Numerical Algorithms (Search for Journal in Brave)
Iterative numerical methods for linear systems (65F10) Linear equations (linear algebraic aspects) (15A06) Numerical analysis (65-XX)
Cites Work
- Unnamed Item
- Linear regression models, least-squares problems, normal equations, and stopping criteria for the conjugate gradient method
- Preconditioned conjugate gradients for solving singular systems
- Behavior of slightly perturbed Lanczos and conjugate-gradient recurrences
- BiCGstab(\(l\)) and other hybrid Bi-CG methods
- Estimates in quadratic formulas
- On error estimation in the conjugate gradient method and why it works in finite precision computations
- Solution of sparse rectangular systems using LSQR and Craig
- On computing quadrature-based bounds for the \(A\)-norm of the error in conjugate gradients
- Accurate error estimation in CG
- Error estimation in preconditioned conjugate gradients
- Estimates of the \(l_2\) norm of the error in the conjugate gradient algorithm
- Generalized Golub--Kahan Bidiagonalization and Stopping Criteria
- Iterated preconditioned LSQR method for inverse problems on unstructured grids
- Preconditioned Iterative Methods for Solving Linear Least Squares Problems
- The university of Florida sparse matrix collection
- Estimating the Backward Error in LSQR
- LSMR: An Iterative Algorithm for Sparse Least-Squares Problems
- Euclidean-Norm Error Bounds for SYMMLQ and CG
- LSLQ: An Iterative Method for Linear Least-Squares with an Error Minimization Property
- Stopping Criteria for the Iterative Solution of Linear Least Squares Problems
- LSQR: An Algorithm for Sparse Linear Equations and Sparse Least Squares
- Stability of Conjugate Gradient and Lanczos Methods for Linear Least Squares Problems
- Sharp 2-Norm Error Bounds for LSQR and the Conjugate Gradient Method
- LNLQ: An Iterative Method for Least-Norm Problems with an Error Minimization Property
- The Lanczos and Conjugate Gradient Algorithms
- Bidiagonalization of Matrices and Solution of Linear Equations
- Methods of conjugate gradients for solving linear systems
- The N‐Step Iteration Procedures
This page was built for publication: Estimating error norms in CG-like algorithms for least-squares and least-norm problems