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

SoftwareX(2022)

引用 3|浏览51
暂无评分
摘要
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.
更多
查看译文
关键词
Code generation,Automatic differentiation
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要