Controlling Inner Iterations in the Jacobi–Davidson Method

Abstract

The Jacobi-Davidson method is an eigenvalue solver which uses an inner-outer scheme. In the outer iteration one tries to approximate an eigenpair while in the inner iteration a linear system has to be solved, often iteratively, with the ultimate goal to make progress for the outer loop. In this paper we prove a relation between the residual norm of the inner linear system and the residual norm of the eigenvalue problem. We show that the latter may be estimated inexpensively during the inner iterations. On this basis, we propose a stopping strategy for the inner iterations to maximally exploit the strengths of the method. These results extend previous results obtained for the special case of Hermitian eigenproblems with the conjugate gradient or the symmetric QMR method as inner solver. The present analysis applies to both standard and generalized eigenproblems, does not require symmetry, and is compatible with most iterative methods for the inner systems. It can also be extended to other types of inner-outer eigenvalue solvers, such as inexact inverse iteration or inexact Rayleigh quotient iteration. The effectiveness of our approach is illustrated by a few numerical experiments, including the comparison of a standard Jacobi-Davidson code with the same code enhanced by our stopping strategy.