Elimination Techniques for Algorithmic Differentiation Revisited

Abstract

All known elimination techniques for (first-order) algorithmic differentiation (AD) rely on Jacobians to be given for a set of relevant elemental functions. Realistically, elemental tangents and adjoints are given instead. They can be obtained by applying software tools for AD to the parts of a given modular numerical simulation. The novel generalized face elimination rule proposed in this article facilitates the rigorous exploitation of associativity of the chain rule of differentiation at arbitrary levels of granularity ranging from elemental scalar (state of the art) to multivariate vector functions with given elemental tangents and adjoints. The implied combinatorial Generalized Face Elimination problem asks for a face elimination sequence of minimal computational cost. Simple branch and bound and greedy heuristic methods are employed as a baseline for further research into more powerful algorithms motivated by promising first test results. The latter can be reproduced with the help of an open-source reference implementation.