Abstract
This paper proposes a class of polynomial preconditioners for solving non-Hermitian linear systems of equations. The polynomial is obtained from a least-squares approximation in polynomial space instead of a standard Krylov subspace. The process for building the polynomial relies on an Arnoldi-like procedure in a small dimensional polynomial space and is equivalent to performing GMRES in polynomial space. It is inexpensive and produces the desired polynomial in a numerically stable way. A few improvements to the basic scheme are discussed including the development of a short-term recurrence and the use of compound preconditioners. Numerical experiments, including a test with challenging nonnormal three-dimensional Helmholtz equations and a few publicly available sparse matrices, are provided to illustrate the performance of the proposed preconditioners.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 1248-1267 |
| Number of pages | 20 |
| Journal | SIAM Journal on Matrix Analysis and Applications |
| Volume | 42 |
| Issue number | 3 |
| DOIs | |
| State | Published - 2021 |
Bibliographical note
Funding Information:\ast Received by the editors June 2, 2020; accepted for publication (in revised form) by L. Giraud May 27, 2021; published electronically August 5, 2021. https://doi.org/10.1137/20M1342562 Funding: The work of the authors was supported by the National Science Foundation grants DMS-1521573, DMS-1912048, and OAC-2003720. \dagger Hewlett Packard Enterprise, Bloomington, MN 55425 USA (xin.ye@hpe.com). \ddagger Department of Mathematics, Emory University, Atlanta, GA 30322 USA (yxi26@emory.edu). \S Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN 55455 USA (saad@umn.edu).
Publisher Copyright:
© 2021 Society for Industrial and Applied Mathematics Publications. All rights reserved.
Keywords
- Helmholtz equation
- Orthogonal polynomial
- Polynomial iteration
- Polynomial preconditioning
- Shortterm recurrence