ACORNS: An easy-to-use code generator for gradients and Hessians

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 scri...

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Bibliographic Details
Published inSoftwareX Vol. 17; p. 100901
Main Authors Desai, Deshana, Shuchatowitz, Etai, Jiang, Zhongshi, Schneider, Teseo, Panozzo, Daniele
Format Journal Article
LanguageEnglish
Published Elsevier B.V 01.01.2022
Elsevier
Subjects
Automatic differentiation
Code generation
Code generation
Automatic differentiation
Online AccessGet full text
ISSN2352-7110
2352-7110
DOI10.1016/j.softx.2021.100901

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Summary: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.
ISSN:2352-7110
2352-7110
DOI:10.1016/j.softx.2021.100901