I'm currently working on a model that has a large number of variables (around 200k), and I don't know what the proper way to handle such a big problem is.
One suggestion I got is to use lazy constraints in order to limit the number of constraints, so that the model can be lighter, but I'm not sure I understand how they should be used.
Lazy constraints are constraints that get checked against a solution that satisfies all normal constraints, and in case they get violated, they get added as new constraints.
The problem with my model is that it can generate many solutions that have the same score, so I fear that a very large number of solutions would have to be tested against these lazy constraints, which they'd violate, resulting in many iterations of this process, and in each of these iterations many violations would be found (meaning that a very large number of constraints would be added anyway).
So, how do I use lazy constraints? Do I set as lazy only the constraints that rarely get violated, while keeping the others as normal? Or should I make a weak model, strenghtening it with lazy constraints?