Abstract
SummaryIn this paper, we present a new type of restarted Krylov method for calculating the smallest eigenvalues of a symmetric positive definite matrix G. The new framework avoids the Lanczos tridiagonalization process and the use of polynomial filtering. This simplifies the restarting mechanism and allows the introduction of several modifications. Convergence is assured by a monotonicity property that pushes the eigenvalues toward their limits. Another innovation is the use of inexact inversions of G to generate the Krylov matrices. In this approach, the inverse of G is approximated by using an iterative method to solve the related linear system. Numerical experiments illustrate the usefulness of the proposed approach.
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