Michel Soares

In your example you could not remove C2.Let's say your objective function is to maximize X + Y. The optimal solution of the original model would be in the intersection of C2 and C4. If you take out...

You could presolve a model and export it: reduced_model = model.presolve() reduced_model.write("reduced_model.lp") Based on this LP file your can get to a new model with fewer constraints.

Hi Paul, Generic algorithms for this reduction are exactly what pre-solve does and there many techniques behind this. Here is a paper on some of pre-solve techniques used in Gurobi.Specific ideas f...

Ok, let me explain a few things that can help and the challenges in having a result for this model in 30 seconds. 1. If the presolve takes the full 30 seconds, you will not have a solution within y...

Hi Dante, You should be able to access the best solution found when the optimization ends, the same way you would retrieve the solution when it finds the optimal solution. It is not clear if this i...

Hi Constantin, Indeed it seems to be related to numerical stability. Ideally, you would improve your model to avoid this, if possible. In terms of parameters, you can try increasing the value of Nu...

You can get the number of cuts added using callbacks. You can get the number of cuts added so far (MIP_CUTCNT) in a MIP node (MIPNODE and MIPNODE_NODCNT = 0): that should give you the number of cut...

Hi Saeedeh, The root node of a MIP is indeed the solution of the relaxed model. Indeed, there are many cuts that can be added in the root node to speed up finding a good integer solution. If you wa...

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.