On the Occurrence of Superlinear Convergence of Exact and Inexact Krylov Subspace Methods

Krylov subspace methods often exhibit superlinear convergence. We present a general analytic model which describes this superlinear convergence, when it occurs. We take an invariant subspace approach, so that our results apply also to inexact methods, and to nondiagonalizable matrices. Thus, we provide a unified treatment of the superlinear convergence of GMRES, conjugate gradients, block versions of these, and inexact subspace methods. Numerical experiments illustrate the bounds obtained.