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[ascl:2102.015] ForwardDiff: Forward mode automatic differentiation for Julia

ForwardDiff implements methods to take derivatives, gradients, Jacobians, Hessians, and higher-order derivatives of native Julia functions (or any callable object, really) using forward mode automatic differentiation (AD).While performance can vary depending on the functions you evaluate, the algorithms implemented by ForwardDiff generally outperform non-AD algorithms in both speed and accuracy.

Code site:
https://github.com/JuliaDiff/ForwardDiff.jl
Used in:
https://ui.adsabs.harvard.edu/abs/2019MNRAS.484.3772T
Described in:
https://ui.adsabs.harvard.edu/abs/2016arXiv160707892R
Bibcode:
2021ascl.soft02015R

Views: 194

ascl:2102.015
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