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 language | English (US) |
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Article number | 100901 |
Journal | SoftwareX |
Volume | 17 |
DOIs | |
State | Published - Jan 2022 |
Keywords
- Automatic differentiation
- Code generation
ASJC Scopus subject areas
- Software
- Computer Science Applications