Sharpness in rates of convergence for the symmetric Lanczos method
From MaRDI portal
Publication:3584783
DOI10.1090/S0025-5718-09-02258-3zbMath1206.65132OpenAlexW1968988636MaRDI QIDQ3584783
Publication date: 30 August 2010
Published in: Mathematics of Computation (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1090/s0025-5718-09-02258-3
numerical exampleeigenvectorerror boundsconvergence rateKrylov subspace methodextreme eigenvaluesChebyshev polynomialiterative solversymmetric Lanczos algorithm
Related Items (6)
Sharp Majorization-Type Cluster Robust Bounds for Block Filters and Eigensolvers ⋮ On the Generalized Lanczos Trust-Region Method ⋮ A Lanczos Method for Large-Scale Extreme Lorentz Eigenvalue Problems ⋮ Convergence of the block Lanczos method for eigenvalue clusters ⋮ Global convergence of the restarted Lanczos and Jacobi-Davidson methods for symmetric eigenvalue problems ⋮ Error bounds of Lanczos approach for trust-region subproblem
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- The convergence behavior of Ritz values in the presence of close eigenvalues
- Behavior of slightly perturbed Lanczos and conjugate-gradient recurrences
- Eigenvalues of Rayleigh quotient matrices
- On eigenvalues of a Rayleigh quotient matrix
- Superlinear convergence rates for the Lanczos method applied to elliptic operators
- Expressions and bounds for the GMRES residual
- Accuracy of computed eigenvectors via optimizing a Rayleigh quotient
- Further results on the convergence behavior of conjugate-gradients and Ritz values
- Vandermonde matrices with Chebyshev nodes
- Some remarks on the spectra of Hermitian matrices
- An analysis of the Rayleigh--Ritz method for approximating eigenspaces
- Which Eigenvalues Are Found by the Lanczos Method?
- Least Squares Residuals and Minimal Residual Methods
- On the Rates of Convergence of the Lanczos and the Block-Lanczos Methods
- Predicting the Behavior of Finite Precision Lanczos and Conjugate Gradient Computations
- How to Make the Lanczos Algorithm Converge Slowly
- Iterative Solution Methods
- On Meinardus' examples for the conjugate gradient method
- Convergence Analysis of Krylov Subspace Iterations with Methods from Potential Theory
- Estimates for Some Computational Techniques in Linear Algebra
This page was built for publication: Sharpness in rates of convergence for the symmetric Lanczos method
