论文标题
橡子：梯度和黑姐妹的易于使用的代码生成器
ACORNS: An Easy-To-Use Code Generator for Gradients and Hessians
论文作者
论文摘要
一阶和二阶导数的计算是许多计算应用程序中的主食，从机器学习到科学计算。我们提出了一种算法，以自动区分以C99代码子集及其有效实现为Python脚本的算法。我们证明我们的算法可以对物理模拟和几何处理中使用的常见算法进行自动，可靠和有效的分化。
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.
