Riley Clement

Hi Alberto, You are correct.  You can verify this by adding the following code for (int i = 0; i < model.getCol(x).size(); i++) { cout << "Coeff term:" << i << ": " << model.getCol(x).getCoeff(i...

Hi Zheren, It's not quite clear to me what you mean by the value of "a". Consider the following minimum reproducible example import guropbipym = gp.Model()n=1M=5x = m.addVar(vtype=gp.GRB.BINARY)a =...

Hi Mohammed, You are creating quadratic expressions (QuadExpr objects) trot1 and trot2.  When you use them in a constraint you will be creating a quadratic constraint and they needed to be added wi...

Hi Xuan, You can just make your delta values a coefficient to this special variable in each constraint. Eg let's call the special variable D. Then for the i-th constraint add delta[i]*D to the RHS....

Hi Xuan, If the delta is the same for all constraints, then a good alternative would be to define a gurobipy variable called "delta", which you add to the RHS of every constraint when defining your...

Hi Madhushini, We might need to see what your dataframes look like in order for someone to provide an appropriate answer. - Riley

Hi Weihang, The elements in your "product array" are gurobipy.MLinExpr.  The initial arguments to gurobipy.min_ need to be variables.  You can fix this by introducing auxiliary variables like so: #...

Here is another way I have been using that may be worth mentioning, for anyone that stumbles across this in the future. # define model m here# define iterable of fixed_vars (containing variables yo...

No worries Daniel, perhaps a simple metaheuristic such as simulated annealing would be better in this case as the objective function can be a "black box".  You may find a handy python framework if ...

Hi Jingwei,Perhaps Model.getCol can help here? If that doesn't work for you case then my advice would be to just set a "_col" attribute on your variables which you use to store the corresponding co...