A cross-product approach for low-rank approximations of large matrices

Abstract

In this paper we present a new type of restarted Krylov method for calculating low-rank approximations of large matrices. In contrast to former Krylov methods, our approach does not apply the Lanczos algorithm, or Lanczos bidiagonalization. This simplifies the basic iteration and allows the introduction of several innovations.An advantage of the proposed method is that it requires a minimal amount of computer storage. The Krylov matrix which is built at each iteration is using an improved starting vector and a new three-term recurrence relation. These modifications lead to fast rate of convergence. Numerical experiments illustrate the usefulness of the proposed approach.