Polynomial Preconditioned Arnoldi
Numerical Analysis
2018-06-22 v1 Spectral Theory
Abstract
Polynomial preconditioning can improve the convergence of the Arnoldi method for computing eigenvalues. Such preconditioning significantly reduces the cost of orthogonalization; for difficult problems, it can also reduce the number of matrix-vector products. Parallel computations can particularly benefit from the reduction of communication-intensive operations. The GMRES algorithm provides a simple and effective way of generating the preconditioning polynomial. For some problems high degree polynomials are especially effective, but they can lead to stability problems that must be mitigated. A two-level "double polynomial preconditioning" strategy provides an effective way to generate high-degree preconditioners.
Cite
@article{arxiv.1806.08020,
title = {Polynomial Preconditioned Arnoldi},
author = {Mark Embree and Jennifer A. Loe and Ronald B. Morgan},
journal= {arXiv preprint arXiv:1806.08020},
year = {2018}
}
Comments
25 pages