In the JuMP docs it shows how to efficiently set start values using one model:

```
x = all_variables(model)
x_solution = value.(x)
set_start_value.(x, x_solution)
```

However, I am looking for an efficient way to do this with two models, where the first model (with a solution) includes a subset of the variables in a second model.

Here is my very slow attempt:

```
# ... build model1
optimize!(model1)
x1 = all_variables(model1);
x1_solution = value.(x1);
d = Dict(string(n)=>v for (n,v) in zip(x1, x1_solution))
model2 = JuMP.Model()
# ... create second model that has more variables than model1
# and also shares variable names with model1
x2 = all_variables(model2);
for (n1,v) in d # this loop is very slow, like one variable/second
x2_index = findfirst(n2 -> string(n2) == n1, x2)
if !(isnothing(x2_index))
set_start_value(x2[x2_index], v)
@info("Set start value for $(x2[x2_index])")
end
end
```

I also question if I am doing this correctly?