ACORNS: An easy-to-use code generator for gradients and Hessians - Desai, Deshana; Shuchatowitz, Etai; Jiang, Zhongshi; Schneider, Teseo; Panozzo, Daniele (SoftwareX, 2022)
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.