Matthias Miltenberger
Gurobi StaffGurobi Optimization Support Manager  Berlin, Germany
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Latest activity by Matthias Miltenberger
Matthias Miltenberger commented,
Please see here for objectivewise termination criteria: How do I set termination criteria in multiobjective environments? – Gurobi Help Center Cheers,Matthias

Matthias Miltenberger commented,
Hi Florian, Here's the relevant part from our documentation: Objectives are numbered 0 through NumObj1. The order of the objectives is arbitrary, but you must provide a unique index for each one (...

Matthias Miltenberger commented,
In that case, you should check what the list \(\texttt{p}\) actually contains. It seems, there are incompatible data types present. Try this code: for n in range(N): for s in range(S): pr...

Matthias Miltenberger commented,
Hi Ludwig, it seems that "beet" is not a variable, so you also cannot use it to construct the constraint "beet <= 1". You may have to add another call to addVariable() to do something with those be...

Matthias Miltenberger commented,
Interesting! Could you please share the model? It might be some kind of numerical issue, but without more information, this is very hard to say.

Matthias Miltenberger commented,
Yes, you should be able to get the dual values of a MIP problem using the fixedModel approach: How do I retrieve the (dual) Pi values for a MIP problem? – Gurobi Help Center

Matthias Miltenberger commented,
Your question is unclear. The optimal solution to the small LP file is (values of a_MN): [[0. 1. 1.] [ 1. 0. 0.]] You want to maximize \(\texttt{min_avg_res}\), so it will be set to 6, satisfy...

Matthias Miltenberger commented,
The code works fine. You must have done something wrong with defining the variables. Here is a minimal working snippet that uses exactly your formulation, except for a correct way to define the x v...

Matthias Miltenberger commented,
You can use the method Model.relax() to create the LP relaxation of your model. This can then be saved as a file.

Matthias Miltenberger commented,
When modeling nonlinear problems, you should always make sure that the variables that taking part in nonlinear constraints have predefined bounds that are as tight as possible. This reduces the dom...