摘要翻译：
计算金融中最重要的两个领域：希腊语和校准，分别是基于对大量敏感性的高效和准确的计算。本文概述了伴随微分和自动微分(AD)，也称为算法微分，是计算这些灵敏度的技术。与有限差分近似相比，该方法可以将计算量降低几个数量级，灵敏度可达到机器精度。文中还提供了实例和文献综述。
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英文标题：
《Adjoints and Automatic (Algorithmic) Differentiation in Computational
  Finance》
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作者：
Cristian Homescu
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最新提交年份：
2011
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Computational Finance        计算金融学
分类描述：Computational methods, including Monte Carlo, PDE, lattice and other numerical methods with applications to financial modeling
计算方法，包括蒙特卡罗，偏微分方程，格子和其他数值方法，并应用于金融建模
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英文摘要：
  Two of the most important areas in computational finance: Greeks and, respectively, calibration, are based on efficient and accurate computation of a large number of sensitivities. This paper gives an overview of adjoint and automatic differentiation (AD), also known as algorithmic differentiation, techniques to calculate these sensitivities. When compared to finite difference approximation, this approach can potentially reduce the computational cost by several orders of magnitude, with sensitivities accurate up to machine precision. Examples and a literature survey are also provided.
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PDF链接：
https://arxiv.org/pdf/1107.1831