15 years of Adjoint Algorithmic Differentiation (AAD) in finance
教程式地介绍了伴随算法微分（AAD）技术，说明其如何应用于蒙特卡洛和偏微分方程这两种期权定价的主要数值方法，并回顾了过去十五年间量化金融领域的重要文献。
Following the seminal ‘Smoking Adjoint’ paper by Giles and Glasserman [Smoking adjoints: Fast monte carlo greeks. Risk, 2006, 19, 88–92], the development of Adjoint Algorithmic Differentiation (AAD) has revolutionized the way risk is computed in the financial industry. In this paper, we provide a tutorial of this technique, illustrate how it is immediately applicable for Monte Carlo and Partial Differential Equations applications, the two main numerical techniques used for option pricing, and review the most significant literature in quantitative finance of the past fifteen years.