I have a simple optimizing code that I would like to add a constraint to. I think it may fall under nonlinear optimization (so potentially `NLopt.jl`

) but I have no idea.

The function is defined as the sum of squared differences.

```
f(p) = sum((data_vector .- (p[2])*x_values.^p[1] ).^2
optz = optimize(f, p0; autodiff = :forward)
println(Optim.minimizer(optz))
```

so I am trying to estimate `p[1]`

and `p[2]`

here. I would like to add a constraint to `p[2]`

such that `p[2] < 1`

.

It seems easy enough, but I am not sure how to really do it. Do I need “boxed constraints”?