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

SoftwareX

Daniele Panozzo¹  Etai Shuchatowitz²  Zhongshi Jiang²  Teseo Schneider²  Deshana Desai² 

[1] Corresponding author.;New York University, 60 5th Ave, New York, NY 10011, United States of America;

关键词: Code generation;    Automatic differentiation;   

DOI  :  

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown