The Exponential of Skew-Symmetric Matrices: A Nearby Inverse and Efficient Computation of Derivatives

TL;DR

This paper characterizes the invertibility of the derivative of the matrix exponential on skew-symmetric matrices, introduces a nearby inverse for efficient computation, and demonstrates significant speed improvements over existing methods.

Contribution

It provides a new characterization of invertibility, constructs an explicit nearby inverse, and offers efficient formulas for derivatives and their inverses in the context of skew-symmetric matrices.

Findings

The inverse derivative is efficiently computable for matrices outside the conjugate locus.

The proposed formulas are up to 3.9 times faster than existing methods.

Numerical experiments confirm the efficiency and accuracy of the new approach.

Abstract

The matrix exponential restricted to skew-symmetric matrices has numerous applications, notably in view of its interpretation as the Lie group exponential and Riemannian exponential for the special orthogonal group. We characterize the invertibility of the derivative of the skew-restricted exponential, thereby providing a simple expression of the tangent conjugate locus of the orthogonal group. In view of the skew restriction, this characterization differs from the classic result on the invertibility of the derivative of the exponential of real matrices. Based on this characterization, for every skew-symmetric matrix $A$ outside the (zero-measure) tangent conjugate locus, we explicitly construct the domain and image of a smooth inverse -- which we term \emph{nearby logarithm} -- of the skew-restricted exponential around $A$. This nearby logarithm reduces to the classic principal logarithm of special orthogonal matrices when $A=\mathbf{0}$. The symbolic formulae for the differentiation and its inverse are derived and implemented efficiently. The extensive numerical experiments show that the proposed formulae are up to $3.9$-times and $3.6$-times faster than the current state-of-the-art robust formulae for the differentiation and its inversion, respectively.