ACORNS: An Easy-To-Use Code Generator for Gradients and Hessians
Mathematical Software
2022-02-04 v1 Symbolic Computation
Abstract
The computation of first and second-order derivatives is a staple in many computing applications, ranging from machine learning to scientific computing. We propose an algorithm to automatically differentiate algorithms written in a subset of C99 code and its efficient implementation as a Python script. We demonstrate that our algorithm enables automatic, reliable, and efficient differentiation of common algorithms used in physical simulation and geometry processing.
Keywords
Cite
@article{arxiv.2007.05094,
title = {ACORNS: An Easy-To-Use Code Generator for Gradients and Hessians},
author = {Deshana Desai and Etai Shuchatowitz and Zhongshi Jiang and Teseo Schneider and Daniele Panozzo},
journal= {arXiv preprint arXiv:2007.05094},
year = {2022}
}