Hello,

I would like to do nonlinear optimization with inequality constraint by `NLopt`

, in which my objective function is not defined when the constraint is not satisfied. I’m using `LN_COBYLA`

because I cannot compute the derivative.

However, the solver sometimes try to evaluate the objective function outside of the constraint and, as a consequence, the solver fails.

The minimal example below shows the problem.

I know I can impose a bounds constraint for this simple example but not in general.

Is there any way to search values only inside of the constraint? (What I can come up with is to let the objective function return huge value when undefined, although I don’t think it’s a beautiful solution.)

Thank you.

=======================

Example: \min x_1+x_2 subject to x_1\geq0 and x_2\geq0 but x_1+x_2 is undefined for x_1<0 or x_2<0

```
using NLopt
function obj(x, grad)
if any(x .<0)
println("negative element")
println(x)
return error("undefined")
else
return x[1]+x[2]
end
end
function myconstraint(result, x, grad)
result .= -x
end
opt = Opt(:LN_COBYLA, 2)
min_objective!(opt, obj)
inequality_constraint!(opt, myconstraint, [0, 0])
optimize(opt, [0.001, 0.001])
```

return

```
undefined
[-0.000320083, 0.000507602]
(0.000585786437626905, [0.000292893, 0.000292893], :FORCED_STOP)
```