Abstract
In comparison to symbolic differentiation and numerical differencing, the chain rule based technique of automatic differentiation is shown to evaluate partial derivatives accurately and cheaply. In particular it is demonstrated that the reverse mode of automatic differentiation yields any gradient vector at no more than five times the cost of evaluating the underying scalar function. After developing the basic mathematics we describe several software implementations and briefly discuss the ramifications for optimization.
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CITATION STYLE
Jaulin, L., Kieffer, M., Didrit, O., & Walter, É. (2001). Automatic Differentiation. In Applied Interval Analysis (pp. 271–286). Springer London. https://doi.org/10.1007/978-1-4471-0249-6_9
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