Matthias Miltenberger
Gurobi StaffGurobi Optimization Support Manager  Berlin, Germany
 Total activity 1096
 Last activity
 Member since
 Following 0 users
 Followed by 0 users
 Votes 46
 Subscriptions 464
Activity overview
Latest activity by Matthias Miltenberger
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...

Matthias Miltenberger commented,
Seems OK to me, except that the exponent should be exp(5x) to match your mathematical equation. Please note that you should also specify tight bounds to all your variables to improve the solver's ...

Matthias Miltenberger commented,
Hi Fabio, Please excuse my delayed response. From the code snippet you shared, it seems that you are not working with Gurobi environments explicitly. This means that for every single new Model() ca...

Matthias Miltenberger commented,
This guide should help you out regarding the division: How do I divide by a variable in Gurobi? – Gurobi Help Center In general, you often need to split up your mathematical constraint into smaller...

Matthias Miltenberger commented,
Hi Nico, Here's one way to do it: t = {}for i in range(n): t[i] = m.addVar() m.addConstr(t[i] == 0.5 * d[i]) m.addGenConstrExp(t[i], c[i], options="FuncNonlinear=1") You may want to pass...

Matthias Miltenberger commented,
Hi Alessandra, This should be fine. Just be aware that you cannot have dynamically changing values (e.g. the output of a function that is part of your problem) as model parameters. The upper bound ...