Michel Soares

Hi Laaziz, First make sure you are using Gurobi 11: there were a lot of changes for non-linear models and it seems to have an impact on your models. Your first model (opt-model9) is feasible for Gu...

Hi Laaziz, A .lp model should be fine. No need for the python code.  

Hi Nima, From the logs, looking only at possible parameter changes, I would suggest trying Presolve = 2 and MIPFocus = 1. Also, updating your Gurobi to 11.0 might improve your results as well.

Hi Laaziz, I cannot see how that would be possible, I believe it would take a in-depth look to understand it. Can you share the logs and the LP file?

MIQCP is a convex optimization problem with integer variables. All convex optimization and integer programming optimization theory still holds and a lot of the technique Gurobi's uses is directly i...

Hi, Indeed there are additional steps involved in equal constraints. However, I cannot see how it would be better to replace it by two inequalities, it should be worse because it increases the numb...

As far as I understand, Aggregate and AggFill will mostly remove variables. These are sometimes referred to as "Substitute implied free variables". Disabling Aggregate increases your model solve ti...

Integer models are solved through branch and cut combined with several heuristics. What you described makes part of the algorithm, having this gap between a relaxed solution and the best integer fe...

Hi, I am not sure if it would help, but it seems like you do not need Bp[edge], you can use direction_AB and direction_BA instead. It should remove some variables and make your model more dense, wh...

Hi, You can get the presolved model this way: reduced_model = model.presolve() reduced_model.write("reduced_model.lp") You may be able to directly modify the reduced_model directly as you wish, I h...