Hi guys, I am trying to implement a code in Julia (it’s my first in Julia) and I am having problems in computing the gradient of a function with vector arguments. I would like to compute the derivative in relation with only one of those vector arguments of my function. If you need further details…

Just use ForwardDiff.gradient : julia> using ForwardDiff julia> f(x) = sum(x) f (generic function with 1 method) julia> ForwardDiff.gradient(f, [1., 2.]) 2-element Array{Float64,1}: 1.0 1.0 Edit: hadn’t noticed [image] Ujku_KU: in relation with only one of those vector arguments of my func…

@nilshg Hi nilshg, and thank you for your advice. I was trying to create a minimal working code that represent generally what I would like to realize with my code, but in the mean time “tkoolen” has answered and he is in line with what I was experimenting with. Hence I will carry on where he has lef…

@tkoolen Hi tkoolen, and thank you very much for your answer. This part of your answer: ForwardDiff.gradient(x -> f(x, [3, 4]), [1, 2]) ia what I was very much interested, but when I tried to implement it I got the same error as the one when I implemented it myself as follows: julia> using Forwa…

It will be a lot easier to help you, if you posted the exact error message. Everything works fine on my machine, so I would first try ]up ForwardDiff to make sure, you are running the latest version. Also make sure you are running a fresh Julia session. If that doesn’t work, the output of versioninf…

I agree with simeonschaub. This code runs fine in a new Julia session. The fact that the tag is getfield(PreImageProblems, Symbol("##4#5") means that the code you posted cannot be what produced the error, regardless of ForwardDiff version.

@simeonschaub and @tkoolen Hi guys and thank you very much for your help. Maybe I wasn’t clear enough and I am sorry for that. As you guys affirm the code that tkoolen provided and the code I provided work in a new Julia session. But I am saying that when I try to implement it on my code (when I try…

It looks like you designed the PolynomialKernel constructor such that the element type of the first and second argument must be the same, namely T₃. But when you use ForwardDiff, the constructor is called with an array with element type ForwardDiff.Dual as the second argument. One way to fix this i…

@tkoolen Hi tkoolen and thank you very much for your help. I wasn’t aware of: struct Partials{N,V} <: AbstractVector{V} values::NTuple{N,V} end and struct Dual{T,V<:Real,N} <: Real value::V partials::Partials{N,V} end in ForwardDiff. It seems I have to generalize the PolynomialK…

@tkoolen Hi tkoolen, sorry for this delay in contacting you back, but just to confirm that your suggestion worked fine. Once more thank you very much. Cheers. Ergnoor

Gradient of a normal (NOT vector) function that has vector arguments

General Usage

nilshg July 22, 2019, 7:56am 2

Please refer to this topic and post an MWE - I don’t think your question can be answered at present without additional information.

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Gradient of a normal (NOT vector) function that has vector arguments

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