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Algorithm 984: ADiGator, a Toolbox for the Algorithmic Differentiation of Mathematical Functions in MATLAB Using Source Transformation via Operator Overloading

机译:算法984:ADiGator,用于通过运算符重载使用源变换在MATLAB中对数学函数进行算法微分的工具箱

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摘要

A toolbox called ADiGator is described for algorithmically differentiating mathematical functions in MATLAB. ADiGator performs source transformation via operator overloading using forward mode algorithmic differentiation and produces a file that can be evaluated to obtain the derivative of the original function at a numeric value of the input. A convenient by-product of the file generation is the sparsity pattern of the derivative function. Moreover, because both the input and output to the algorithm are source codes, the algorithm may be applied recursively to generate derivatives of any order. A key component of the algorithm is its ability to statically exploit derivative sparsity at the MATLAB operation level to improve runtime performance. The algorithm is applied to four different classes of example problems and is shown to produce runtime efficient derivative code. Due to the static nature of the approach, the algorithm is well suited and intended for use with problems requiring many repeated derivative computations.
机译:描述了一个名为ADiGator的工具箱,用于在算法上区分MATLAB中的数学函数。 ADiGator使用正向模式算法微分,通过运算符重载执行源转换,并生成一个文件,该文件可以进行评估以获得原始函数在输入数值上的导数。文件生成的一个方便的副产品是导数函数的稀疏模式。此外,由于算法的输入和输出都是源代码,因此可以递归应用该算法以生成任何阶数的导数。该算法的关键组成部分是它能够在MATLAB操作级别上静态利用导数稀疏性来提高运行时性能。该算法被应用于四个不同类别的示例问题,并被证明可以生成运行时有效的派生代码。由于该方法的静态性质,该算法非常适合用于需要多次重复导数计算的问题。

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