@article{8628294423a3451fbc71b3178eb765e7,
title = "ACORNS: An easy-to-use code generator for gradients and Hessians",
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 = "Automatic differentiation, Code generation",
author = "Deshana Desai and Etai Shuchatowitz and Zhongshi Jiang and Teseo Schneider and Daniele Panozzo",
note = "Funding Information: We thank NYU High Performance Computing for resources, services, and staff expertise. This work was partially supported by the National Science Foundation, United States of America CAREER award under Grant No. 1652515 , the National Science Foundation, United States of America grants OAC-1835712 , OIA1937043 , CHS-1908767 , CHS-1901091 , the NSERC, Canada grants RGPIN-2021-03707 and DGECR-2021-00461 , a Sloan Fellowship, a gift from Adobe Research, a gift from nTopology, and a gift from Advanced Micro Devices, Inc. Publisher Copyright: {\textcopyright} 2021 The Authors",
year = "2022",
month = jan,
doi = "10.1016/j.softx.2021.100901",
language = "English (US)",
volume = "17",
journal = "SoftwareX",
issn = "2352-7110",
publisher = "Elsevier BV",
}