I would like to use JuMP for nonlinear constrained optimization. Specifically I want to be able to pass my objective function into it from outside. How do I do that? What I tried is below.

I am not sure if this is a JuMP-specific thing or just a Julia passing functions thing. Thanks

```
f(x) = x^2 + 3x + 4
using JuMP
import Ipopt
function test_obj(obj_func)
model = Model(Ipopt.Optimizer)
@variable(model, x, start = -1.0)
@NLobjective(model, Min, obj_func)
@NLconstraint(model, x >= 5)
optimize!(model)
println("""
termination_status = $(termination_status(model))
\n x = $(value(x))
""")
return
end
test_obj(f)
> Unexpected object #22 (of type var"#22#23"{typeof(f)} in nonlinear expression.
```

Based on this question I also tried:

```
model = Model(Ipopt.Optimizer)
@variable(model, x, start = -1.0)
@NLobjective(model, Min, f(x))
# @NLconstraint(model, sqrt(y-π) - b_1 >= sqrt(y-π-x_2) )
# @NLconstraint(model, π == p_2*(c-x_2) )
optimize!(model)
> Unrecognized function "f" used in nonlinear expression.
```