ForwardDiff.jl
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JuliaDiff
Forward Mode Automatic Differentiation for Julia
Hello, I noticed at a certain point my code started to crash because of some NaNs introduced by Forward diff when performing derivatives of the eigenvectors of matrix. Here was...
The Hessian of a function f is a symmetric matrix if f is smooth enough. Is there a way of having this information made available? I guess not just by...
Is there a reason why types like `Dual` and `Tag` or functions like `value` and `derivative` are left undocumented?
Closes #610. ## Proposal I propose adding a `value_and_derivative` function for easy single-pass computation of value and derivative that has the same signature as `derivative` but returns a plain tuple....
Adds support for taking derivatives of functions with `Unitful` units on their input and/or output by means of an extension (weakdep). The extension works by ensuring that `Dual` always wraps...
In Julia v1.10, I used Aqua to detect method ambiguities in my package https://github.com/impICNF/ContinuousNormalizingFlows.jl/issues/356 and some of its report were related to this package: ```julia Ambiguity #7 ==(x::ForwardDiff.Dual{Tx}, y::Real) where...
Add code for forming nested duals in the docs so folks don't make the mistakes I made in https://github.com/JuliaDiff/ForwardDiff.jl/issues/671
This is motivated by https://github.com/SciML/juliatorch/issues/12. It is proof of concept implementation to see if folks here think this would be reasonable to include in ForwardDiff or if it would be...
I was trying to calculate the Hessian of a simple function such as ```julia using ForwardDiff function busPQ(Ybus, Vθ) n = length(Vθ) V=Vθ[1:Int(n/2)] θ=Vθ[Int(n/2)+1:n] Vbus=V.*exp.(im .*θ) # Vbus=V.*(cos.(θ)+im .*sin.(θ)) #...
Trying to solve https://github.com/JuliaDiff/ForwardDiff.jl/issues/380. Does not fix the "show" ambiguity. A bit naively done, please review and advise if there's anything that needs to be done differently.
