15 lines
742 B
Markdown
15 lines
742 B
Markdown
# AutomaticDifferentiation
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Templated c++ Forward Automatic differentiation.
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There are two versions :
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- A scalar one,
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- a vectorized one.
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The class is a simple one, no expression templates are used. The class is however a template, meaning that any base numeric type can be used with it. It has successfully tested with boost::multiprecision::mpfr.
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## Scalar version
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The scalar one allows very easily to produce higher order derivatives.
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## Vector version
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The vectorized one is harder to make work with higher order derivatives, but allows the simultaneous computation of the full gradient, in a single function call, making it more efficient than backward automatic differentiation.
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It currently depends on Eigen for the vectorized part.
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