Automatic smoothness detection of the resolvent Krylov subspace method for the approximation of $C_0$-semigroups
This provides a theoretical guarantee for a practical advantage of the resolvent Krylov subspace method, benefiting users of numerical linear algebra for operator functions.
The paper proves that the resolvent Krylov subspace method for approximating C0-semigroups automatically converges faster when the input vector is smoother, without requiring user knowledge or method modifications. Numerical experiments confirm the theoretical findings.
The resolvent Krylov subspace method builds approximations to operator functions $f(A)$ times a vector $v$. For the semigroup and related operator functions, this method is proved to possess the favorable property that the convergence is automatically faster when the vector $v$ is smoother. The user of the method does not need to know the presented theory and alterations of the method are not necessary in order to adapt to the (possibly unknown) smoothness of $v$. The findings are illustrated by numerical experiments.