BIB-VERSION:: CS-TR-v2.0 ID:: STAN//NA-M-87-01 ENTRY:: January 28, 1996 ORGANIZATION:: Stanford University, Department of Computer Science, Numerical Analysis Project TITLE:: The convergence of inexact Chebyshev and Richardson iterative methods for solving linear systems TYPE:: Manuscript AUTHOR:: Golub, Gene H. AUTHOR:: Overton, Michael L. DATE:: February 1987 PAGES:: 38 ABSTRACT:: The Chebyshev and second-order Richardson methods are classical iterative schemes for solving linear systems. We consider the convergence analysis of these methods when each step of the iteration is carried out inexactly. This has many applications, since a preconditioned iteration requires, at each step, the solution of linear system which may be solved inexactly using an "inner" iteration. We derive an error bound which applies to the general nonsymmetric inexact Chebyshev iteration. We show how this simplifies slightly in the case of a symmetric or skew-symmetric iteration, and we consider both the cases of underestimating and overestimating the spectrum. We show that in the symmetric case, it is actually advantageous to underestimate the spectrum when the spectral radius and the degree of inexactness are both large. This is not true in the case of the skew-symmetric iteration. We show how similar results apply to the Richardson iteration. Finally, we describe numerical experiments which illustrate the results and suggest that the Chevyshev and Richardson methods, with reasonable param eter choices, may be more effective than the conjugate gradient method in the presence of inexactness. NOTES:: [Adminitrivia V1/Prg/19960128] END:: STAN//NA-M-87-01