See https://github.com/JuliaLang/julia/issues/16113
For gradients, check out ForwardDiff. It'll give you really fast
calculations.
On Tuesday, September 13, 2016 at 4:29:59 AM UTC-7, MLicer wrote:
>
> Dear all,
>
> i am wondering if there exists Julia N-dimensional equivalents to Numpy
> vector field operators like gradient, divergence and curl,
Fast to implement, only moderately fast for execution; I switch to
ReverseDiffSource
Both ForwardDiff and ReverseDiff source solve a different problem (taking
the derivative of a user-supplied function f(x)). The Matlab and NumPy
gradient functions, instead, take an array (not a function) and compute
differences of adjacent elements of the array, returning a new array.
Thank you all!
On 14 September 2016 at 00:41, Steven G. Johnson
wrote:
> Both ForwardDiff and ReverseDiff source solve a different problem (taking
> the derivative of a user-supplied function f(x)). The Matlab and NumPy
> gradient functions, instead, take an array (not a function) and compute
>
