# NLopt :FORCED\_STOP when objective function has wrong signature (missing gradient arg)
**URL:** https://discourse.julialang.org/t/nlopt-forced-stop-when-objective-function-has-wrong-signature-missing-gradient-arg/82742
**Category:** Optimization (Mathematical)
**Tags:** nlopt
**Created:** [June 14, 2022, 12:03pm UTC](https://discourse.julialang.org/t/nlopt-forced-stop-when-objective-function-has-wrong-signature-missing-gradient-arg/82742 "2022-06-14T12:03:02Z")
**Posts on this page:** 3
**Page:** 1
### Author:  [@pierre-haessig](https://discourse.julialang.org/u/pierre-haessig)
#### Post date: [June 14, 2022, 12:03pm UTC](https://discourse.julialang.org/t/nlopt-forced-stop-when-objective-function-has-wrong-signature-missing-gradient-arg/82742/1 "2022-06-14T12:03:02Z")
Hello,
I just faced a small usability issue using [NLopt.jl](https://github.com/JuliaOpt/NLopt.jl/). I believe the issue is common enough (e.g. forum post from Oct 2020 [https://discourse.julialang.org/t/nlopt-forced-stop](https://discourse.julialang.org/t/nlopt-forced-stop)) that it may suggest a better error catching.
To give a bit of background, I had some prior experience with NLopt last year, then a long pause and then yesterday I was studying a fresh optimization problem from scratch (not starting from a working code template). So I was partly a NLopt newcomer. And I get stuck with a `:FORCED_STOP` return code.
I took me some hours of instrumenting my cost functions (with prints, logging of parameters values in a global array…) to find the silly cause of the error. Here is a simplified version of my problem which shows the issue: a seemingly innocent simple unconstrained **derivative-free** optimization problem:
```julia
using NLopt
function obj_badsig(x)
J = sum(x.^2)
return J
end
opt = Opt(:LN_NELDERMEAD, nx)
x0 = [1., 1., 1.]
xmax = [2., 2., 2.]
opt.lower_bounds = -xmax
opt.upper_bounds = xmax
opt.min_objective = obj_badsig
opt.xtol_rel = 1e-4
(minf,minx,ret) = optimize(opt, x0)
```
This yields `(0.0, [1.0, 1.0, 1.0], :FORCED_STOP)`. And in fact the objective function is never called.
And the solution is to reread the [NLopt.jl](https://github.com/JuliaOpt/NLopt.jl/) README with better eyes: a gradient parameter is required, even when working with gradient-free algorithms:
```julia
function obj(x::Vector, grad::Vector)
J = sum(x.^2)
return J
end
```
which works.
So my question is: is there a better way for NLopt.jl to catch wrong signatures of objective (and constraint) function to report a more informative error message than `:FORCED_STOP` for absent-minded users like me?
---
### Author:  [@pierre-haessig](https://discourse.julialang.org/u/pierre-haessig)
#### Post date: [June 21, 2022, 11:41am UTC](https://discourse.julialang.org/t/nlopt-forced-stop-when-objective-function-has-wrong-signature-missing-gradient-arg/82742/2 "2022-06-21T11:41:10Z")
Hello @stevengj, do you believe I should open an issue on this topic of uncatched wrong signature? Or is it just a matter of requiring users to read `NLopt.jl`’s doc more carefully?
---
### Author:  [@odow](https://discourse.julialang.org/u/odow)
#### Post date: [June 21, 2022, 9:00pm UTC](https://discourse.julialang.org/t/nlopt-forced-stop-when-objective-function-has-wrong-signature-missing-gradient-arg/82742/3 "2022-06-21T21:00:01Z")
Related:
- [Exception thrown by objective function is not rethrown by nlopt · Issue #156 · JuliaOpt/NLopt.jl · GitHub](https://github.com/JuliaOpt/NLopt.jl/issues/156)
- [Fix #156, exceptions from FORCED\_STOP not rethrown by ikirill · Pull Request #157 · JuliaOpt/NLopt.jl · GitHub](https://github.com/JuliaOpt/NLopt.jl/pull/157)
Nlopt catches all errors and throws FORCED\_STOP. The right fix would be to communicate what error caused the FORCED\_STOP.