Obtain gradient of function at end of iterations in Optim.jl

Is it possible to obtain the gradient of the objective function at the iterations limit? There is a field in the object returned that is named g_residual, that would give me the max norm of the gradient, but I am interested in obtaining the gradient vector. Is there a way to obtain that gradient?

Assuming you didn’t pass an analytic gradient, just use ForwardDiff:

using ForwardDiff
ForwardDiff.gradient(f, x)

It’s what Optim uses internally.

I was hoping to piggy back on the work done by Optim. The way you propose I would have to pay for precompilation. I guess if I had supplied the gradient, then I could that without penalty, maybe I should do that.

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Is recompilation really an issue?

julia> using Optim, ForwardDiff

julia> f(x) = (x[1] - 2)^2 + 3
f (generic function with 1 method)

julia> r = optimize(f, [1.0], BFGS())
 * Status: success

 * Candidate solution
    Final objective value:     3.000000e+00

 * Found with
    Algorithm:     BFGS

 * Convergence measures
    |x - x'|               = 1.00e+00 ≰ 0.0e+00
    |x - x'|/|x'|          = 5.00e-01 ≰ 0.0e+00
    |f(x) - f(x')|         = 1.00e+00 ≰ 0.0e+00
    |f(x) - f(x')|/|f(x')| = 3.33e-01 ≰ 0.0e+00
    |g(x)|                 = 1.83e-11 ≤ 1.0e-08

 * Work counters
    Seconds run:   1  (vs limit Inf)
    Iterations:    1
    f(x) calls:    3
    ∇f(x) calls:   3

julia> xstar = Optim.minimizer(r)
1-element Vector{Float64}:

julia> @time ForwardDiff.gradient(f, xstar)
  0.840569 seconds (2.92 M allocations: 176.158 MiB, 7.48% gc time, 99.96% compilation time)
1-element Vector{Float64}: