
arXiv: 1911.07065
AbstractWe present a polynomial preconditioner for solving large systems of linear equations. The polynomial is derived from the minimum residual polynomial (the GMRES polynomial) and is more straightforward to compute and implement than many previous polynomial preconditioners. Our current implementation of this polynomial using its roots is naturally more stable than previous methods of computing the same polynomial. We implement further stability control using added roots, and this allows for high degree polynomials. We discuss the effectiveness and challenges of root‐adding and give an additional check for stability. In this article, we study the polynomial preconditioner applied to GMRES; however it could be used with any Krylov solver. This polynomial preconditioning algorithm can dramatically improve convergence for some problems, especially for difficult problems, and can reduce dot products by an even greater margin.
Iterative numerical methods for linear systems, Computational methods for sparse matrices, polynomial preconditioning, FOS: Mathematics, Preconditioners for iterative methods, harmonic Ritz values, Mathematics - Numerical Analysis, Numerical Analysis (math.NA), GMRES, linear equations, Orthogonalization in numerical linear algebra
Iterative numerical methods for linear systems, Computational methods for sparse matrices, polynomial preconditioning, FOS: Mathematics, Preconditioners for iterative methods, harmonic Ritz values, Mathematics - Numerical Analysis, Numerical Analysis (math.NA), GMRES, linear equations, Orthogonalization in numerical linear algebra
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 8 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
