This is the Linux app named ForwardDiff.jl whose latest release can be downloaded as v0.10.39sourcecode.tar.gz. It can be run online in the free hosting provider OnWorks for workstations.
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SCREENSHOTS
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ForwardDiff.jl
DESCRIPTION
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 (such as finite-differencing) in both speed and accuracy. Functions like f which map a vector to a scalar are the best case for reverse-mode automatic differentiation, but ForwardDiff may still be a good choice if x is not too large, as it is much simpler. The best case for forward-mode differentiation is a function that maps a scalar to a vector.
Features
- Forward Mode Automatic Differentiation for Julia
- 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
- Functions like f which map a vector to a scalar are the best case for reverse-mode automatic differentiation
- ForwardDiff may still be a good choice if x is not too large, as it is much simpler
Programming Language
Julia
Categories
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