Near-Optimal Approximation of Matrix Functions by the Lanczos Method
We study the widely used Lanczos method for approximating the action of a matrix function $f(\mathbf{A})$ on a vector $\mathbf{b}$ (Lanczos-FA). For the function $f(\mathbf{A})=\mathbf{A}^{-1}$, it is known that, when $\mathbf{A}$ is positive definite, the $\mathbf{A}$-norm error of Lanczos-FA after $k$ iterations matches the optimal approximation from the Krylov subspace of degree $k$ generated by $\mathbf A$ and $\mathbf b$. In this work, we ask whether Lanczos-FA also obtains similarly strong optimality guarantees for other functions $f$. We partially answer this question by showing that, up to a multiplicative approximation factor, Lanczos-FA also matches the optimal approximation from the Krylov subspace for any rational function with real poles outside the interval containing $\mathbf{A}$'s eigenvalues. The approximation factor depends on the degree of $f$'s denominator and the condition number of $\mathbf{A}$, but not on the number of iterations $k$. This result provides theoretical justification for the excellent performance of Lanczos-FA on functions that are well approximated by rationals. Additionally, using different techniques, we prove that Lanczos-FA achieves a weaker notion of optimality for the functions $\mathbf A^{1/2}$ and $\mathbf A^{-1/2}$. Experiments confirm that our new bounds more accurately predict the convergence of Lanczos-FA than existing bounds, in particular those based on uniform polynomial approximation.