# On the conditioning of the matrix-matrix exponentiation

## TL;DR

This paper investigates the conditioning and derivatives of the matrix-matrix exponentiation function, providing new theoretical insights, algorithms for condition number computation, and numerical experiments to understand its stability and sensitivity.

## Contribution

It introduces new results on the Fréchet derivative and conditioning of the matrix-matrix exponentiation, including algorithms for computing the condition number and applications to other matrix functions.

## Key findings

- Derived new formulas for the Fréchet derivative of the matrix-matrix exponentiation.
- Proposed an algorithm for computing the relative condition number of A^B.
- Numerical experiments demonstrate the effectiveness of the proposed methods.

## Abstract

If ${A}$ has no eigenvalues on the closed negative real axis, and $B$ is arbitrary square complex, the matrix-matrix exponentiation is defined as $A^B:=e^{\log({A}){B}}$. This function arises, for instance, in Von Newmann's quantum-mechanical entropy, which in turn finds applications in other areas of science and engineering. Since in general $A$ and $B$ do not commute, this bivariate matrix function may not be a primary matrix function as commonly defined, which raises many challenging issues. In this paper, we revisit this function and derive new related results. Particular emphasis is given to its Fr\'echet derivative and conditioning. We present a general result on the Fr\'echet derivative of bivariate matrix functions with applications not only to the matrix-matrix exponentiation but also to other functions, such as the second order Fr\'echet derivatives and some iteration functions arising in matrix iterative methods. The numerical computation of the Fr\'echet derivative is discussed and an algorithm for computing the relative condition number of $A^B$ is proposed. Some numerical experiments are included.