Software-based automatic differentiation is flawed
Year: 2025
Type: article
Abstract: Software efforts in automatic differentiation are built on the observation that every computation is implemented as a series of elementary operations, and that symbolic partial derivatives can be supplied for each of these operations. Any computation can then be traversed (either forwards or backwards) utilizing the chain rule to accumulate derivatives. These frameworks have no mechanism for simplifying these expressions before evaluating them. As we illustrate below, the resulting errors tend to be unbounded.
Source: Journal of Computational Physics
Authors: Daniel Johnson, Trevor Maxfield, Yongxu Jin, Ronald Fedkiw
Institution: Stanford University
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,980
Funder: Office of Naval Research
Awards: G4662603624, G5639600206
OpenAlex ID: W4413923598