Generalizing the Fréchet Derivative Algorithm for the Matrix Exponential

The computation of off-diagonal blocks of matrix functions $f(T)$, where $T$ is block triangular, poses a challenging problem in scientific computing. We present a novel algorithm that exploits the structure of block triangular matrices, generalizing the algorithm of Al-Mohy and Higham for computing the Fr\'echet derivative of the matrix exponential. This work has significant applications in fields such as exponential integrators for solving systems of first-order differential equations, Hamiltonian linear systems in control theory, and option pricing in finance. Our approach introduces a linear operator that maps off-diagonal blocks of $T$ into their counterparts in $f(T)$. By studying the algebraic properties of the operator, we establish a comprehensive computational framework, paving the way to extend existing Fr\'echet derivative algorithms of matrix functions to more general settings. For the matrix exponential, in particular, the algorithm employs the scaling and squaring method with diagonal Pad\'e approximants to $\exp(x)$, with parameters chosen based on a rigorous backward error analysis, which notably does not depend on the norm of the off-diagonal blocks. The numerical experiment demonstrates that our algorithm surpasses existing algorithms in terms of accuracy and efficiency, making it highly valuable for a wide range of applications.