Minimal Residual Method Stronger than Polynomial Preconditioning

V. Faber, W. Joubert, E. Knill, T. Manteuffel
1996 SIAM Journal on Matrix Analysis and Applications  
This paper compares the convergence behavior of two popular iterative methods for solving systems of linear equations: the s-step restarted minimal residual method (commonly implemented by algorithms such as GMRES(s)), and (s?1)-degree polynomial preconditioning. It is known that for normal matrices, and in particular for symmetric positive de nite matrices, the convergence bounds for the two methods are the same. In this paper we demonstrate that for matrices unitarily equivalent to an upper
more » ... iangular Toeplitz matrix, a similar result holds, namely, either both methods converge or both fail to converge. However, we show this result cannot be generalized to all matrices. Speci cally, we develop a method, based on convexity properties of the generalized eld of values of powers of the iteration matrix, to obtain examples of real matrices for which GMRES(s) converges for every initial vector, but every (s ? 1)degree polynomial preconditioning stagnates or diverges for some initial vector.
doi:10.1137/s0895479895286748 fatcat:3lkvt6tc4ratvojfn4qnxrxa2i