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


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
StatePublished - Jan 2022


  • Automatic differentiation
  • Code generation

ASJC Scopus subject areas

  • Software
  • Computer Science Applications


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