An adaptive \(s\)-step conjugate gradient algorithm with dynamic basis updating.
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Publication:778541
DOI10.21136/AM.2020.0136-19MaRDI QIDQ778541
Publication date: 2 July 2020
Published in: Applications of Mathematics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1908.04081
Computational methods for sparse matrices (65F50) Iterative numerical methods for linear systems (65F10) Parallel numerical computation (65Y05) Complexity and performance of numerical algorithms (65Y20)
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Cites Work
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- On the generation of Krylov subspace bases
- Adaptive procedure for estimating parameters for the nonsymmetric Tchebychev iteration
- Newton interpolation at Leja points
- Behavior of slightly perturbed Lanczos and conjugate-gradient recurrences
- s-step iterative methods for symmetric linear systems
- An adaptive Chebyshev iterative method for nonsymmetric linear systems based on modified moments
- On the evaluation of polynomial coefficients
- Varying the \(s\) in your \(s\)-step GMRES
- Reducing the effect of global communication in \(\text{GMRES} (m)\) and CG on parallel distributed memory computers
- Reliable updated residuals in hybrid Bi-CG methods
- A parallel GMRES version for general sparse matrices
- Approximating the extreme Ritz values and upper bounds for the \(A\)-norm of the error in CG
- Accuracy of Two Three-term and Three Two-term Recurrences for Krylov Space Solvers
- A Residual Replacement Strategy for Improving the Maximum Attainable Accuracy of $s$-Step Krylov Subspace Methods
- The university of Florida sparse matrix collection
- The Lanczos and conjugate gradient algorithms in finite precision arithmetic
- Parallelizable restarted iterative methods for nonsymmetric linear systems. part I: Theory
- The Condition of Polynomials in Power Form
- A Newton basis GMRES implementation
- Estimating the Attainable Accuracy of Recursively Computed Residual Methods
- Theory of Inexact Krylov Subspace Methods and Applications to Scientific Computing
- Residual Replacement Strategies for Krylov Subspace Iterative Methods for the Convergence of True Residuals
- The Adaptive $s$-Step Conjugate Gradient Method
- Hiding Global Communication Latency in the GMRES Algorithm on Massively Parallel Machines
- Accuracy of the $s$-Step Lanczos Method for the Symmetric Eigenproblem in Finite Precision
- Inexact Matrix-Vector Products in Krylov Methods for Solving Linear Systems: A Relaxation Strategy
- Methods of conjugate gradients for solving linear systems
