ACORNS: An Easy-To-Use Code Generator for Gradients and Hessians

Deshana Desai, Etai Shuchatowitz, Zhongshi Jiang, Teseo Schneider, Daniele Panozzo

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.