A new investigation of the extended Krylov subspace method for matrix function evaluations

For large square matrices A and functions f , the numerical approximation of the action of f ( A ) to a vector v has received considerable attention in the last two decades. In this paper we investigate the extended Krylov subspace method , a technique that was recently proposed to approximate f ( A ) v for A symmetric. We provide a new theoretical analysis of the method, which improves the original result for A symmetric, and gives a new estimate for A nonsymmetric. Numerical experiments confirm that the new error estimates correctly capture the linear asymptotic convergence rate of the approximation. By using recent algorithmic improvements, we also show that the method is computationally competitive with respect to other enhancement techniques.