We demonstrate the flexibility and ease of use of C++ algorithmic differentiation (AD) tools based on overloading to numerical patterns (kernels) arising in computational finance. While adjoint methods and AD have been known in the finance literature for some time, there are few tools capable of handling and integrating with the C++ codes found in production. Adjoint methods are also known to be very powerful but to potentially have infeasible memory requirements. We present several techniques for dealing with this problem and demonstrate them on numerical kernels which occur frequently in finance. We build the discussion around our own AD tool dco/c++ which is designed to handle arbitrary C++ codes and to be highly flexible, however the sketched concepts can certainly be transferred to other AD solutions including in-house tools. An archive of the source code for the numerical kernels as well as all the AD solutions discussed can be downloaded from an accompanying website. This includes documentation for the code and dco/c++. Trial licences for dco/c++ are available from NAG.

中文翻译：

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计算金融中典型数值模式的伴随算法微分工具支持

我们展示了基于对计算金融中出现的数值模式（内核）的重载的 C++ 算法微分 (AD) 工具的灵活性和易用性。虽然伴随方法和 AD 在金融文献中已经有一段时间了，但很少有工具能够处理和集成生产中发现的 C++ 代码。众所周知，伴随方法非常强大，但可能具有不可行的内存要求。我们提出了几种处理这个问题的技术，并在金融中经常出现的数值核上演示它们。我们围绕我们自己的 AD 工具 dco/c++ 展开讨论，dco/c++ 旨在处理任意 C++ 代码并具有高度的灵活性，但是草拟的概念当然可以转移到其他 AD 解决方案，包括内部工具。数值内核的源代码存档以及所有讨论的 AD 解决方案都可以从随附的网站下载。这包括代码和 dco/c++ 的文档。dco/c++ 的试用许可证可从 NAG 获得。