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

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

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Article number100901
JournalSoftwareX
Volume17
DOIs
StatePublished - Jan 2022

Keywords

  • Automatic differentiation
  • Code generation

ASJC Scopus subject areas

  • Software
  • Computer Science Applications

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