
doi: 10.1137/140968276
Summary: We look at solving large nonsymmetric systems of linear equations using polynomial preconditioned Krylov methods. We give a simple way to find the polynomial. It is shown that polynomial preconditioning can significantly improve restarted GMRES for difficult problems, and the reasons for this are examined. Stability is discussed, and algorithms are given for increased stability. Next, we apply polynomial preconditioning to GMRES with deflated restarting. It is shown that this is worthwhile for sparse matrices and for problems with many small eigenvalues. Multiple right-hand sides are also considered.
Linear equations (linear algebraic aspects), Iterative numerical methods for linear systems, polynomial preconditioning, eigenvalues, GMRES, linear equations, deflation, QCD, GMRES-DR
Linear equations (linear algebraic aspects), Iterative numerical methods for linear systems, polynomial preconditioning, eigenvalues, GMRES, linear equations, deflation, QCD, GMRES-DR
| 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). | 17 | |
| 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). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
