NLopt not optimising

Hi
I am rather new to Julia and I am experimenting with NLopt. I ran the tests on github and they work fine but then I tried my own objective and constraints.
return x[1]
end
f=zeros(2)
f[1]=x[2]^2-1+x[3]
f[2]=-10x[2]^2+0.1x[3]
z=-f+w*x[1]
return z
end

I then followed the same procedure followed in the test examples but with a derivative free optimiser.
opt = Opt(:LN_COBYLA, 3)
opt.lower_bounds = [0, -Inf.-Inf]
opt.upper_bounds = [1,Inf,Inf]
opt.xtol_rel = 1e-4
opt.min_objective = ps
opt.inequality_constraint = (x,g) → ps_con(x,g,[1,1])
(minf,minx,ret) = optimize(opt, [1,1,1])

No error occurs but the optimiser does not do anything and exits with either FORCED_STOP or XTOL_REACHED at the first iteration.

Note that calling the objective and constraint functions individually with random inputs does not produce any error.

What am I doing wrong?
Mx

Try to call the function and constraint with the values you supplied to see if there is an error in them.

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See the vector valued constraints section. These (in)equality constraints expect first argument to be returned vector.

already done that and the function outputs the expected value with no error

ok I can try, this is the part I was not sure about, in fact
I thought the the syntax I was using use equivalent to declaring a vector constraint but I will try to follow the example you suggested

I tested these new functions
end
return x[1]
end
x[2]
end
z[1]=-(x[2]^2-1+x[3])+w[1]x[1]
z[2]=-(-10
x[2]^2+0.1*x[3])+w[2]*x[1]
end
and inserted the string
inequality_constraint!(opt, (z,x,g) → ps_con(z,x,g,[1,1]), [1e-8,1e-8]::AbstractVector)
(minf,minx,ret) = optimize(opt, [1,1, 1])
but now I get:
(1.0, [1.0, 1.0, 1.0], :FORCED_STOP)

1 Like

1 Like

Yes much better, thanks!

NLopt is pretty aggressive about swallowing errors in my experience. The fact that you have `:FORCED_STOP` and just get your inits back leads me to believe that there is an error happening somewhere. I haven’t looked at your code in detail here and am not familiar with the constraint interface in NLopt, but just in the interest of a quick response, you’re almost certainly experiencing an error that NLopt is trying to elegantly handle without making everything crash.

yeh I suspected that but I tested each function individually with no error and given that I started programming in Julia on Saturday I am not that skilled yet
any advice on where to look for the bug would be precious

It seems `grad` should be 3x2 as documentation says instead of 2x3 if it ever asked by optimization.

``````julia> function ps(x::Vector,grad::Vector)
end
return x[1]
end
ps (generic function with 1 method)

julia> using NLopt

end
z[1]=-(x[2]^2-1+x[3])+w[1]x[1]
z[2]=-(-10x[2]^2+0.1*x[3])+w[2]*x[1]
end
ps_con (generic function with 1 method)

julia> opt=Opt(:LD_MMA,3)
Opt(LD_MMA, 3)

julia> opt.min_objective=ps
ps (generic function with 1 method)

julia> opt.lower_bounds=[0,-Inf,-Inf];

julia> opt.upper_bounds=[1,Inf,Inf];

julia> inequality_constraint!(opt, (z,x,g) -> ps_con(z,x,g,[1,1]), [1e-8,1e-8])

julia> (minf,minx,ret) = optimize(opt, Float64[1,1, 1])

^C(0.0, [0.0, -0.01634360284914689, 2.11468681873786], :FORCED_STOP)

julia> opt.maxeval = 1000
1000

julia> (minf,minx,ret) = optimize(opt, Float64[1,1, 1])
(0.0, [0.0, -0.01634360284914689, 2.11468681873786], :MAXEVAL_REACHED)
``````

It seems without a stopping criteria it never stops that’s why stopped first try.

great, thank you, I included a tolerance on the x at convergence and now it returns a solution, so just to be clear you did the following:
``question: if the solver does not need gradients does it matter how I define the gradient? I guess no``