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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.

Keywords

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}
}

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25 pages