I’m trying to compute all third partial derivatives of a function using ForwardDiff, but cannot get it to be type stable and I don’t understand why. Here is a minimal example: using ForwardDiff using StaticArrays function tf( x ) return ( exp( 0.1*x[1] + 0.2*x[2] ) ) end function get_derivati…

yes, i found the same problem, but when needing the primal, first and second derivative. for what i understand, when using StaticArrays, the chunk is always the length of the SArray

related: [image] Allocation on ForwardDiff + DiffResults + StaticArrays General Usage Cf https://github.com/JuliaDiff/ForwardDiff.jl/pull/315 Something similar for Hessian may help resolve this.

Type stability for higher derivatives in ForwardDiff

General Usage

longemen3000 August 19, 2021, 10:07pm 3

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Type stability for higher derivatives in ForwardDiff

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