Silke Horn

Hi, You are correct that you cannot access the X attribute before running the optimization. Instead, you can use indicator constraints to model something like this. This article contains additional...

Hi, I cannot reproduce this so far. Could you post a complete log? Are you using any non-default parameter settings? Silke

Hi Ritesh, From the log snippet you posted above, it is impossible to predict how difficult it is to solve the model, but many of our users are solving models of similar sizes. 115 GB of memory sou...

Hi Ritesh, Please have a look at this article. (The short answer is that Gurobi does not support GPUs.) - Silke

Hi Koen, I am happy to hear that the NoRel heuristic is working so well for you. Unfortunately, it is not possible to change parameter settings in a callback. See this article for more information....

Hi, Thanks for posting your code. This looks as though some of the parentheses may be misplaced. Your objective expression is pretty long and convoluted. Maybe different formatting can help break t...

Hi, Could you please clarify what type your variables have? For example, which ones are Gurobi model variables, and which are constants? Or, better yet, provide a self-contained code example so we ...

Yes, exactly! Of course, you can also iterate over an array of constraints (e.g. one returned by addConstrs) to set the Lazy attribute. Silke

Hi, You can find C++ attribute examples here. C++ is not as elegant as Python, and you need to include the type of the attribute in the identifier. The Lazy attribute is integer-valued, so you woul...

Hi, For me, the most natural way to implement those constraints would actually be the addConstrs method: x = model.addVars(10, 20, name="x")model.addConstrs(x[i,j] + x[i_tilde,j] <= 1 for i in rang...