Michel Soares

Hi, You can fix the values of all integer variables by iterating through the result and adding constraints to these variables. Example: variable X takes value 0, therefore you add a constraint that...

Hi Yuzhou, Besides the material Riley provided, there a few other points that are really important in MIP, which might be worth looking at: - Branching rules: Achterberg, Tobias, Thorsten Koch, and...

Hi Yuzhou, I would suggest you start by studying the Simplex Algorithm followed by Interior Point Method, for LPs. For MIP/IP you should start by studying the Branch and Cut algorithm. You can star...

Hi Beatriz, The slack variable here is a new variable that means the difference between the supply/demand proportion of region I and J. Add constraint: (S_i is variable for the supply of region i, ...

Hi Beatriz, I am not sure if I understood the problem correctly, but here is an idea: Add constraint: (S_i is variable for the supply of region i, D_i is the constant for the demand of region i)S_i...

The coefficient statistics, by default, are always shown for the original model, before any scaling. Please try running this to see if the changes after presolving, which should include your scalin...

Hi, Take a look at addMConstr, in which you can add a matrix constraint. It should speed up your model building.

Model.relax is useful in very specific cases, it is not common to be used, I can think of one case: There are some MIP models which might take too long to get to answer, requiring sophisticated us...

Hi Ariel, The first thing Gurobi will do is solving the relaxed model (meaning that all integer/binary constraints are ignored). Afterwards, it will use heuristics and branch and bound to find inte...

Hi David, It seems like your problem is indeed proving optimality. There are many reasons why this may happen, depending on your model. If you are not familiar with the topic of Weak MIP Formulatio...