What is the best way to add the following constraints in JuMP to minimize computation time? I need to solve similar models many times.

```
@constraint(model, [i = 1:n, j = 1:m], [t[i, j], 1, x[i, j]] in MOI.ExponentialCone())
```

Another way for example is to do for loops:

```
for j = 1:m
for i = 1:n
@constraint(model, [t[i, j], 1, x[i, j]] in MOI.ExponentialCone())
end
end
```

How to do this without loops? For example, with vectors? Will vectorized codes be faster when `n`

or `m`

is large?

As a related question, I remember I have seen cases where adding constraints with vectorized codes can be much faster than simply doing for loops, but I also heard that for loops are actually considered to be more efficient than vectorized codes. I’m a bit confused about in general which way to follow.

Thanks.