ForwardDiff.jl \ Optim.jl and recompilation for any vector size

Specific Domains Optimization (Mathematical)
3

@dpsanders , yep!

code:

using ForwardDiff
function f(x::Vector{T}) where T
    x2 = x .* x
    return log(sum(x2))
end
v1 = [1., 2.]
v2 = [1., 2., 3.]
@time ForwardDiff.hessian(f, v1)
@time ForwardDiff.hessian(f, v1)
@time ForwardDiff.hessian(f, v2)
@time ForwardDiff.hessian(f, v2)

out:

julia> @time ForwardDiff.hessian(f, v1)
  2.000931 seconds (4.09 M allocations: 232.179 MiB, 99.94% compilation time)
2×2 Matrix{Float64}:
  0.24  -0.32
 -0.32  -0.24

julia> 

julia> @time ForwardDiff.hessian(f, v1)
  0.000030 seconds (10 allocations: 1.359 KiB)
2×2 Matrix{Float64}:
  0.24  -0.32
 -0.32  -0.24

julia> 

julia> @time ForwardDiff.hessian(f, v2)
  2.122246 seconds (4.02 M allocations: 224.059 MiB, 7.06% gc time, 99.97% compilation time)
3×3 Matrix{Float64}:
  0.122449   -0.0408163  -0.0612245
 -0.0408163   0.0612245  -0.122449
 -0.0612245  -0.122449   -0.0408163

julia> 

julia> @time ForwardDiff.hessian(f, v2)
  0.000030 seconds (10 allocations: 2.828 KiB)
3×3 Matrix{Float64}:
  0.122449   -0.0408163  -0.0612245
 -0.0408163   0.0612245  -0.122449
 -0.0612245  -0.122449   -0.0408163

julia>

first call f(x) with v2 - 2.122246 seconds