# Using \`bboptimize\` with a function containing \`JuMP\` calls
**URL:** https://discourse.julialang.org/t/using-bboptimize-with-a-function-containing-jump-calls/112846
**Category:** Optimization (Mathematical)
**Tags:** jump, optimization
**Created:** [April 11, 2024, 8:40pm UTC](https://discourse.julialang.org/t/using-bboptimize-with-a-function-containing-jump-calls/112846 "2024-04-11T20:40:40Z")
**Posts on this page:** 3
**Page:** 1
### Author:  [@Rick](https://discourse.julialang.org/u/Rick)
#### Post date: [April 11, 2024, 8:40pm UTC](https://discourse.julialang.org/t/using-bboptimize-with-a-function-containing-jump-calls/112846/1 "2024-04-11T20:40:40Z")
Hi everyone,
I am trying blackbox optimization with a function that contains JuMP functions. So, this is like a nested optimization where the “inner problem” is evaluated using JuMP functions and the “outer problem” is optimized using `bboptimize`. However, I am getting an error (see below).
To illustrate what I am doing, here is a minimal example.
- I define a function `estimate_objective` that is parameterized by `c`. This function tries to solve a well-behaved optimization problem.
- Then, I want to find the value of `c` that gives the smallest objective through `bboptimize`.
An obvious solution is to make `c` a variable in the JuMP call. But I do not want to do this in my problem because when it is fixed, the problem I have is convex (the problem I want to solve is more complicated, the function below is just an illustration). Solving the “inner problem” with a fixed `c` is also very fast.
```julia
using BlackBoxOptim, JuMP, NLopt
function estimate_objective(c)
model = Model(NLopt.Optimizer)
set_optimizer_attribute(model, "algorithm", :LD_MMA)
# Min x^2 + c s.t. x >= c
@variable(model, x .>= c)
@objective(model, Min, x^2 .+ c)
JuMP.optimize!(model)
return objective_value(model)
end
bboptimize(estimate_objective; SearchRange = [(-5.0, 5.0)])
```
The error I get is:
```julia
ERROR: At REPL[387]:7: `@variable(model, x[1:1] .>= c)`: Passing arrays as variable bounds without indexing them is not supported.
Instead of:
@variable(model, x[1:2] >= lb)
use
@variable(model, x[i=1:2] >= lb[i])
or
@variable(model, x[1:2])
set_lower_bound.(x, lb)
```
Any thoughts are very much appreciated!! I am also open to using other packages. Thank you!!
---
### Author:  [@odow](https://discourse.julialang.org/u/odow)
#### Post date: [April 11, 2024, 11:46pm UTC](https://discourse.julialang.org/t/using-bboptimize-with-a-function-containing-jump-calls/112846/2 "2024-04-11T23:46:36Z")
The input `c` to the function `estimate_objective` is a vector. JuMP does not support broadcasting lower bounds.
You need
```Julia
julia> using BlackBoxOptim, JuMP, NLopt
julia> function estimate_objective(c)
c_scalar = only(c)
model = Model(NLopt.Optimizer)
set_optimizer_attribute(model, "algorithm", :LD_MMA)
@variable(model, x >= c_scalar)
@objective(model, Min, x^2 + c_scalar)
JuMP.optimize!(model)
return objective_value(model)
end
estimate_objective (generic function with 1 method)
julia> bboptimize(estimate_objective; SearchRange = [(-5.0, 5.0)])
```
---
### Author:  [@Rick](https://discourse.julialang.org/u/Rick)
#### Post date: [April 14, 2024, 6:52am UTC](https://discourse.julialang.org/t/using-bboptimize-with-a-function-containing-jump-calls/112846/3 "2024-04-14T06:52:53Z")
Thanks, this works great!!