How to use NLopt in Julia with equality_constraint

I'm struggling to amend the Julia-specific tutorial on NLopt to meet my needs and would be grateful if someone could explain what I'm doing wrong or failing to understand.

I wish to:

• Minimise the value of some objective function myfunc(x); where
• x must lie in the unit hypercube (just 2 dimensions in the example below); and
• the sum of the elements of x must be one.

Below I make myfunc very simple - the square of the distance from x to [2.0, 0.0] so that the obvious correct solution to the problem is x = [1.0,0.0] for which myfunc(x) = 1.0. I have also added println statements so that I can see what the solver is doing.

testNLopt = function()

    origin = [2.0,0.0]
    n = length(origin)

    #Returns square of the distance between x and "origin", and amends grad in-place
    myfunc = function(x::Vector{Float64}, grad::Vector{Float64})
        if length(grad) > 0 
            grad = 2 .* (x .- origin)
        end
        xOut = sum((x .- origin).^2)
        println("myfunc: x = $x; myfunc(x) = $xOut; ∂myfunc/∂x = $grad")
        return(xOut)
    end

    #Constrain the sums of the x's to be 1...
    sumconstraint =function(x::Vector{Float64}, grad::Vector{Float64})
        if length(grad) > 0
            grad = ones(length(x)) 
        end
        xOut = sum(x) - 1
        println("sumconstraint: x = $x; constraint = $xOut; ∂constraint/∂x = $grad")
        return(xOut)
    end

    opt = Opt(:LD_SLSQP,n)

    lower_bounds!(opt, zeros(n))
    upper_bounds!(opt,ones(n))
    equality_constraint!(opt,sumconstraint,0)
    #xtol_rel!(opt,1e-4)
    xtol_abs!(opt,1e-8)

    min_objective!(opt, myfunc)
    maxeval!(opt,20)#to ensure code always terminates, remove this line when code working correctly?
    optimize(opt,ones(n)./n)
end

I have read this similar question and documentation here and here, but still can't figure out what's wrong. Worryingly, each time I run testNLopt I see different behaviour, as in this screenshot including occasions when the solver uselessly evaluates myfunc([NaN,NaN]) many times.

You aren't actually writing to the grad parameters in-place, as you write in the comments;

grad = 2 .* (x .- origin)

just overrides the local variable, not the array contents -- and I guess that's why you see these df/dx = [NaN, NaN] everywhere. The simplest way to fix that would be with broadcasting assignment (note the dot):

grad .= 2 .* (x .- origin)

and so on. You can read about that behaviour here and here.