Matthias Miltenberger

Since Gurobi version 9.1, the Global Interpreter Lock (GIL) is released during the optimization process which takes place on a lower level. This enables user programs to execute other Python code i...

To create a relaxed version of your model that contains only continuous variables and removes SOS and general constraints, use:relax = model.relax()This creates a new Model object and does not modi...

Custom termination criteria can be set within callbacks in order to avoid calling optimize() several times. The following example uses the Python API to implement a "soft" time limit that is only v...

There are multiple ways to set a termination limit in multi-objective environments: You can set a global limit in the main Gurobi environment. This limit will then be inherited by each Model object...

Gurobi produces a log so you can track the progress of the optimization. A default log file is written only for command-line Gurobi (gurobi_cl). When you use one of the APIs to initiate the optimiz...

For users who cannot install the full Gurobi Package but still need the license tools grbgetkey and grbprobe and/or the token server grb_ts, we have collected those three tools for download here: W...

Estimating the time it takes to solve a MIP is very difficult. The nature of NP-hard problems is that they can take a very long time to solve or may not even be solvable in a reasonable amount of t...

Gurobi can compute an Irreducible Inconsistent Subset (IIS) of an infeasible model. An IIS is a minimal subset of constraints and variable bounds that, if isolated from the rest of the model, is st...

To return from multiple objectives to single-objective optimization, set the model attribute NumObj to 0, call update() on the model, and reset the primary objective function via setObjective(). To...

In multi-objective optimization, not all Model attributes are available. You can use callbacks to store these values (and others) and print them manually when the optimization is finished:def mycal...