I’m trying to use the LD_MMA algoritmn in NLopt to fit my model.

I get Xtol reached after 2 iterations and the values of the variables don’t seem to change from the starting values. Actually when I leave out the xtol restriction and set maxeval to 200, the parameter value still didn’t change from the second iteration.

Do you know which can be the problem?

I tested all the functions I implemented and all of them work outside the NLopt optimization. For your reference, I attach the relevant part for the model fitting here:

```
observed_accuracy = zeros(3,2,4);
observed_accuracy[1,:,:] = [0.964 0.958 0.920 0.828;
0.971 0.965 0.936 0.852];
observed_accuracy[2,:,1:3] = [0.888 0.626 0.155;
0.806 0.789 0.221];
observed_accuracy[3,:,1:3] = [0.863 0.583 0.862;
0.786 0.768 0.938];
bdd = (
(.001, 20.000), # between,
(.001, 2.000), # within,
(.001, 5.000), # sensitivity,
(.000, 20.000), # noise,
(.001, 5.000), # response_scaling,
(.001, 30.000), # crit2_rep,
(.001, 30.000), # crit2_nrep,
(.001, 30.000), # crit3_rep,
(.001, 30.000), # crit3_nrep,
);
bdd_lower = [i[1] for i in bdd];
bdd_upper = [i[2] for i in bdd];
init_guess() = [rand(Uniform(bdd_lower[i],bdd_upper[i])) for i in 1:length(bdd)];
function myobjective(x::Vector, grad::Vector)
criterion = zeros(3,2);
between, within, sensitivity, noise,
response_scaling, criterion[2,1],criterion[2,2],
criterion[3,1], criterion[3,2] = x;
all_vars = [between, within, sensitivity, noise, response_scaling, criterion];
WSSD = calc_wssd(observed_accuracy,all_vars,nsim = 1000);
return(WSSD)
end
function myconstraint(x::Vector, grad::Vector)
return(x[2]-x[1])
end
function optim_all()
println("\n ************start optimization now****************")
opt = Opt(:LD_MMA, length(init_guess()));
opt.lower_bounds = bdd_lower;
opt.upper_bounds = bdd_upper;
opt.xtol_rel = 1e-32;
opt.maxtime = 60*10;
#opt.initial_step = .1;
#opt.maxeval = 200;
opt.min_objective = myobjective; # important!! import objective function
inequality_constraint!(opt, myconstraint, 1e-8);
time1=Dates.now();
(minf,minx,ret) = optimize(opt, init_guess());
println(Dates.now()-time1);
numevals = opt.numevals;
println("got $minf at $minx after $numevals iterations (returned $ret)");
return(minf, minx)
end
```

Thank you in advance for your support!

Mingjia