Lennart Lahrs

Hi Koustav, Unfortunately, I was not able to reproduce the error. Can you please share which gurobipy and Python version you are using? Did you already add any other code to the code snippet above?...

Hi Koustav, I hope the code snippet below helps. Without additional constraints, the variance will always be zero. import gurobipy as gpmodel = gp.Model("Minimize Variance")n = 10x = model.addVars(...

Hi Kim, We recommend placing the license file in one of the following directories: /opt/gurobi, /opt/gurobi1001 (for 10.0.1), or your home directory. If you place your license file in one of those ...

Hi Kim, Can you please try the below command, from within your active environment? This installs the gurobipy extension from the public PyPI server. python -m pip install gurobipy Best regards,Lennart

Hi Jordi,I was able to remove the infeasibility by relaxing the below equality constraints to larger or equal to constraints. >= 1, name="VisitOnceFrom_{}".format(i))>= 1, name="VisitOnceTo_{}".fo...

Hi Kim, To solve your problem, please try the following: make sure your environment is active, call pip uninstall gurobipy, or create a new environment, move to the directory of your Gurobi instal...

Hello Sebastián, The Method and Crossover parameters only affect the algorithm for the initial root relaxation of your MIP. What follows when solving a MIP is the Branch and Bound algorithm. The al...

Hi Shaojie,You can express this type of objective using auxiliary variables. Please find below some comments and a code example that I hope will help you with your problem. Gurobi can handle bilin...

Hi Gabriel, Sequentially solving your model for different objectives rather than solving your model for one combined objective can have a strong influence on model performance and solving behavior....

Hi Frederico, Please find below a suggestion of how to access objective values and the Xn attribute of Gurobi model variables for multiple solutions in Pyomo. import pyomo.environ as pem = pe.Concr...