Marika Karbstein

Hi Josh, Using gurobipy the default lower bounds for the variables are 0, see Model.addMVar().If you change the lower bound for the D variables to -Infinity, the model becomes feasible. Cheers,Marika

Maybe you could start with rather lax bounds and then try to improve them similarly as it is done in this Python example gc_pwl_func.py

As already noted exponential and logarithmic functions are handled via piecewise-linear approximation. All pieces have equal width and this could lead to large approximation errors if, for example,...

Could you please explain a bit more about what exactly is not working? Do you see any errors? Or are you surprised about the result? If so, what is the result and what do you think should be correct?

Please have a look at which type of constraints you can add to a Gurobi model. You cannot use math.log or math.exp.If you want to use exponential expressions on a decision variable, you need to add...

Your code is not directly reproducible but so far I cannot see anything suspicious. Could you explain why you think that your code does not find a maximum weighted matching? If you know an optimal ...

Hi Marco, It looks a bit to me like you need to structure your project and try to 'isolate' the optimization problem from data processing and everything else you want to do. What needs to be optimi...

Hi Marco, model.addGenConstrPow(x, y, 0.5) models the function/equation y = sqrt(x). Whichever value x will take, the value of y is then the square root of x. model.addGenConstrPow(x, x, 0.5) model...

Hi Marco, You cannot use math.sqrt or power on Gurobi variables. This needs to be modeled with general constraints or function constraints. In python a power function can be added with Model.addGen...

Hi Tomáš,Your interpretation is right. In the MIP and MIPSOL callback, the processing of the provided solution is delayed until the next possible point in the solution process, while in the MIPNODE...