Firstly, I am minimizing a function which contains complex arrays and real arrays, with which there are a lot of operations and definitions which are pretty specific so I don’t know how to simplify it down into an example that I can post, and I can’t post the whole function for privacy so I understand if I cannot get much help on this:
My function seems to be working as expected. When I optimize it to find the minimum however, I get something like this, when I am looking for a small negative value (or really any value other than getting zero every single time)
12.126196 seconds (17.56 M allocations: 850.970 MiB, 4.54% gc time)
got 0.0 at [0.0752977, 0.120637, 0.0793536, -0.0835715, -0.17881, -0.421116, -0.0358142, -0.326447, 0.125564, 0.170543, 0.465881, 0.147937, 0.0309477, 0.04438, -0.22023, 0.109589, 0.483408, -0.132619, 0.235039, -0.0263986, -0.164046, 0.00920599, -0.0972819, 0.185368, -0.373212, -0.068289, -0.355533, 0.345202, -0.321877, -0.0296487, 0.0965427, -0.186317, 0.430853, 0.279356, -0.186008, 0.333952, -0.392721, -0.101361, -0.00896927, 0.149327, 0.306786, -0.215247, -0.106167, 0.367069, -0.381538, 0.0530803, -0.040668, 0.0265114, -0.446107, -0.164597, 0.0933045, 0.120752, 0.222864, -0.211655, -0.193802, 0.272776, -0.379973, 0.347921, 0.0671, 0.00366182, 0.0436334, 0.157879, 0.0223028, 0.311515, -0.0437446, 0.317073, 0.142629, -0.371994, -0.0134165, 0.329916, -0.149653, -0.483149, 0.646226, 0.0365353, 0.116795, -0.163173, 0.0900926, 0.388191, 0.211555, 0.191873, 0.0766339, -0.128054] after 1 iterations (returned FORCED_STOP)
Every time. I can delete huge parts of my function and I still get 0.0 after 1 or 2 iterations. I have tried every algorithm. I have tried zeroing out different sections of my function but nothing I do to the function ever has an effect on the result of the optimization, and my function works fine when I check it with other minimization vectors from old runs before this problem happened.
The Nlopt code I am running looks like this with Ef(x::Vector, grad::Vector) being my function:
D=5
Sigma=D
x0 = randn(5*D^2+2)/Sigma
opt2f = Opt(:LN_COBYLA, 5*D^2+2)
lower_bounds!(opt2f, -10.0)
upper_bounds!(opt2f, 10.0)
xtol_rel!(opt2f,1e-6)
min_objective!(opt2f, Ef)
@time (minf,minx,ret) = optimize(opt2f, x0)
numevals = opt2f.numevals # the number of function evaluations
println("got $minf at $minx after $numevals iterations (returned $ret)")
And furthermore I know that Nlopt is generally working because it accurately minimizes the example problem on the github and any other problem I pass to it, even a function which is very similar to the one I have now.
If anyone has any ideas, please help. This has been plaguing me for a month, and it could just be the function, in which case I don’t expect any help without a working example, but if something seems off or familiar to anyone about the Nlopt result, let me know.
Thanks