Which one is more efficient in term of computational time? (I’m looking forward to read a general answer, but below is a simple example to describe the situation)

Suppose you have a large network (over 1K nodes and 10K edges). If we want to create a varibles such as `x[i,j,p]`

representing a person’s connections (their nodes that create edges); is it better to do (1) or (2) ?

y_sets = [(i,r,d) for d in drivers for r in riders for i in Acc_rd[r,d]]

```
# 1 defining very general and then in constraints imposing/checking a condition#
using Graphs, SimpleWeightedGraphs
g = SimpleWeightedGraph(start_node, end_node, W) # W are strength of connections
m = Model();
N = 1:25
P = 1:10
@variable(m, x[N,N, P] >= 0, Bin); #This way, in term of conncetions, some of the combinations are invalid
@constraint(m, [i in N, p in P], sum(x[i,j,p] for j in N, p in P) >= 1 if has_edge(g, i, j))
```

```
# 2 defining via imposing conditions
using Graphs, SimpleWeightedGraphs
g = SimpleWeightedGraph(start_node, end_node, W) # W are strength of connections
m = Model();
N = 1:25
P = 1:10
x_conditioned = [(i,j,p) for i in N for j in N for p in P if has_edge(g, i, j)]
@variable(model, x[x_conditioned]>= 0, Bin)
@constraint(m, [i in N, p in P], sum(x[i,j,p] for j in N, p in P) >= 1)
```

I was wondering if we put limits on for array indexing of a variable `x`

is efficientor it makes no difference. Because having used tuples for array indexing makes writing constraint difficult