# NLopt :FORCED\_STOP

**URL:** <https://discourse.julialang.org/t/nlopt-forced-stop/47747>\
**Category:** Optimization (Mathematical)\
**Created:** [October 4, 2020, 8:24pm UTC](https://discourse.julialang.org/t/nlopt-forced-stop/47747 "2020-10-04T20:24:31Z")\
**Posts on this page:** 3\
**Page:** 1

<div class="post-metadata">

**Author:** ![danicaratelli](https://avatars.discourse-cdn.com/v4/letter/d/90db22/32.png) [@danicaratelli](https://discourse.julialang.org/u/danicaratelli)\
**Post date:** [October 4, 2020, 8:24pm UTC](https://discourse.julialang.org/t/nlopt-forced-stop/47747/1 "2020-10-04T20:24:31Z")

</div>

Hello,

I am trying to get `NLopt` to work but it just stops immediately and gives :FORCED\_STOP. I tried the following but it looks like the algorithm isn’t even going into `_ff`. Any thoughts? Thank you!

```julia
function ff(x)
    return sum(x.^2)
    #return sum(abs.(SSresid(mod_pars,x[1],x[2],x[3])));
end
function _ff(x)
    try ff(x)
    catch 
        println("error")
    end
end
opt = Opt(:LN_SBPLX, 3);
opt.xtol_rel = 1e-1;
opt.min_objective = _ff;
(minf,minx,ret) = NLopt.optimize(opt, [1,25,0.1])

```

---

<div class="post-metadata">

**Author:** ![odow](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/odow/32/28685_2.png) [@odow](https://discourse.julialang.org/u/odow)\
**Post date:** [October 4, 2020, 11:43pm UTC](https://discourse.julialang.org/t/nlopt-forced-stop/47747/2 "2020-10-04T23:43:11Z")

</div>

Read the tutorial: [https://github.com/JuliaOpt/NLopt.jl#tutorial](https://github.com/JuliaOpt/NLopt.jl#tutorial). You need to provide the gradient:

```julia
using ForwardDiff
using NLopt

opt = NLopt.Opt(:LN_SBPLX, 3);
f(x) = sum(x.^2)
function my_obj(x, g)
    if length(g) > 0
        ForwardDiff.gradient!(g, f, x)
    end
    return f(x)
end
opt.min_objective = my_obj
sol = NLopt.optimize(opt, [1.0, 25.0, 0.1])

```

You may be interested in [https://github.com/JuliaNLSolvers/Optim.jl](https://github.com/JuliaNLSolvers/Optim.jl)

---

<div class="post-metadata">

**Author:** ![Nick\_Matzke](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/nick_matzke/32/34568_2.png) [@Nick\_Matzke](https://discourse.julialang.org/u/Nick_Matzke)\
**Post date:** [March 15, 2022, 8:57am UTC](https://discourse.julialang.org/t/nlopt-forced-stop/47747/3 "2022-03-15T08:57:19Z")

</div>

Giving some additional details – may be helpful to other newbies like me:

I had similar errors and this page was one of the few that popped up. So it may be helpful to post my solution.

I was trying to use NLopt’s LN\_BOBYQA to do a gradient free parameter search within box constraints.

I eventually figured it out but it was very obscure. One key thing that is an easy-to-miss requirement is that any function passed to NLopt has to take 2 inputs: (1) the parameter vector, and (2) a gradient function that modifies the input (e.g. g! rather than g).

_However_, the gradient function is pointless in a gradient-free search. But it still has to be there to get NLopt to work. Here was how I did it - YMMV but this worked for me:

```julia
# (Note: I have a pre-existing function, func_to_optimize, that takes 
# input parameter vector x, as well as a variety of constant inputs. 
# This could be anything, it just has to return a value depending on 
# the input parameters in x. (e.g. a -1*log-likelihood, so that by minimizing
# you conduct a Maximum Likelihood search).

# Reduce func_to_optimize() to a single-input function that just takes x
func = x -> func_to_optimize(x, parnames, inputs, p_Ds_v5; returnval="bgb_lnL")

# Create func2, which adds a dummy_gradient! input to make NLopt happy, but then just runs func()
function func2(pars, dummy_gradient!)
	return func(pars)
end # END function func2(pars, dummy_gradient!)

# Define starting guesses at the parameters; check that both func and func2 return log-likelihoods:
pars = [0.9, 0.9]
func(pars)
func2(pars, [])

# Limits on the parameter guesses
lower = [0.0, 0.0]
upper = [5.0, 5.0]

#######################################################
# Set up the optimization
#######################################################
using NLopt
opt = NLopt.Opt(:LN_BOBYQA, length(pars))
ndims(opt)
opt.algorithm
algorithm_name(opt::Opt)
opt.min_objective = func2

# Set the lower & upper bounds on the parameters
opt.lower_bounds = lower::Union{AbstractVector,Real}
opt.upper_bounds = upper::Union{AbstractVector,Real}
opt.lower_bounds
opt.upper_bounds
opt.ftol_abs = 0.00001 # tolerance on log-likelihood

# Run the optimization
(optf,optx,ret) = NLopt.optimize!(opt, pars)

```