Hello,
I am using NLopt to solve an optimization problem with non-linear constraints. I provide the gradients for both the optimization function and the gradient function. Both functions provide the correct values of the gradients as I compared them to previous Matlab results. The optimization stops after one iteration providing the initial point and the corresponding value of the optimization function and giving the message :FORCED_STOP. I tried the examples in the Documentation and they worked finely. Here is the code that I am using.
After running the code I get:
got -0.6640272512608545 after 1 iterations (returned FORCED_STOP)

begin
# M, k, xf, psi, a and R2 are all previously defined Float64 except xf::Vector[1,2]
al=minimum(a)* ones(M,1)
xu=R2* ones(M,1)
yu=R2* ones(M,1)
au=maximum(a)* ones(M,1)
lb =reshape([xl yl al]',M*3,1)
ub =reshape([xu yu au]',M*3,1)
function f(x, grad)
Pres=FP_Multi_Freq_Multi_IncAngle_Radius_Position(x,M,k,xf,psi) # The optimization fn that returns two values: Float64 and a vector of gradients [1x3M] (number of variables to be optimized)
if length(grad) > 0
grad[:]=vec(Pres[2])
end
return Pres[1]
end
function myConst(x, grad)
C=nlcon_grad_Radius_Position(x, R2, M, delta)
if length(grad) > 0
grad[:]=C[2]
end
return C[1]
end
opt = Opt(:LD_SLSQP, M*3)
opt.lower_bounds = lb[:,1]
opt.upper_bounds=ub[:,1]
opt.xtol_abs = 1e-11
inequality_constraint!(opt, myConst)
opt.min_objective = f
opt.maxtime=10
(minf,minx,ret) = optimize(opt, vec(xx)) #xx is a previously defined initial vector
numevals = opt.numevals # the number of function evaluations
println("got $minf at $minx after $numevals iterations (returned $ret)")
end

I can’t run your code because I don’t have the data or the functions. But typically the FORCED_STOP is because there is a different error in your code. What happens if you call

I get this when I call the code.
1×1275 adjoint(::Vector{Float64}) with eltype Float64:
-0.00763178 -0.00462144 -0.00469838 … -0.000935341 -0.00198357 -0.00633272
These values are the constraint values that I get in Matlab.

I get this when I call the code.
1×1275 adjoint(::Vector{Float64}) with eltype Float64:
-0.00763178 -0.00462144 -0.00469838 … -0.000935341 -0.00198357 -0.00633272
These values are the constraint values that I get in Matlab.

function myConst(result, x, grad)
C=nlcon_grad_Radius_Position(x, R2, M, delta)
println(length(grad))
println(1)
if length(grad) > 0
println(2)
grad=C[2]
println(size(grad))
println(grad)
println(3)
end
result=C[1]
end

I used printlp() to track the flow of the code and in this way the code did not even call the constraint function and I got the exact same result as previous.

It worked. I needed to add the value of tol in inequality_constraint!(opt::Opt, c, tol::AbstractVector) like this:

inequality_constraint!(opt, myConst,vec(1e-8*ones(sum(1:M),1)))
The key was in @odow 's suggestion of using the constraint function syntax for vector output.