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ACORNS: An Easy-To-Use Code Generator for Gradients and Hessians
arXiv - CS - Symbolic Computation Pub Date : 2020-07-09 , DOI: arxiv-2007.05094
Deshana Desai, Etai Shuchatowitz, Zhongshi Jiang, Teseo Schneider, and Daniele Panozzo

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.

中文翻译:

ACORNS:用于梯度和 Hessians 的易于使用的代码生成器

一阶和二阶导数的计算是许多计算应用程序的主要内容,从机器学习到科学计算。我们提出了一种算法来自动区分用 C99 代码子集编写的算法及其作为 Python 脚本的有效实现。我们证明了我们的算法能够自动、可靠和有效地区分物理模拟和几何处理中使用的常用算法。
更新日期:2020-07-13
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