Automatic differentiation

<p>In <a href="/facts/Mathematics/pxTouaz4">mathematics</a> and <a href="/facts/Computer_algebra/hjun1KAX">computer algebra</a>, automatic differentiation (auto-differentiation, autodiff, or AD), also called algorithmic differentiation, computational differentiation, and differentiation arithmetic is a set of techniques to evaluate the <a href="/facts/Partial_derivative/JWHsBkAu">partial derivative</a> of a function specified by a computer program. Automatic differentiation is a subtle and central tool to automatize the simultaneous computation of the numerical values of arbitrarily complex functions and their derivatives with no need for the symbolic representation of the derivative, only the function rule or an algorithm thereof is required. Auto-differentiation is thus neither numeric nor symbolic, nor is it a combination of both. It is also preferable to ordinary numerical methods: In contrast to the more traditional numerical methods based on finite differences, auto-differentiation is 'in theory' exact, and in comparison to symbolic algorithms, it is computationally inexpensive.
</p><p>Automatic differentiation exploits the fact that every computer calculation, no matter how complicated, executes a sequence of elementary arithmetic operations (addition, subtraction, multiplication, division, etc.) and elementary functions (<a href="/facts/Exponential_function/ewlYxvR2">exp</a>, <a href="/facts/Natural_logarithm/PnreKEzI">log</a>, <a href="/facts/Sine/gQ6LXK8z">sin</a>, <a href="/facts/Cosine/czej7ur0">cos</a>, etc.). By applying the <a href="/facts/Chain_rule/aMLYxP0x">chain rule</a> repeatedly to these operations, partial derivatives of arbitrary order can be computed automatically, accurately to working precision, and using at most a small constant factor of more arithmetic operations than the original program.
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Automatic differentiation open-in-new

Automatic differentiation