Eli Towle

Hi Jose, Based on that inequality, it seems like you want a single lazy constraint that does sum over all of the \( i \) indices inside of a quicksum() function. Basically, you would just move your...

Hi Jose, You probably get this message from your quicksum() terms: gp.quicksum(1-valsy[i]) The argument provided to quicksum() should be iterable (like a list), not a single value like \( \texttt{1...

Hi Noortje, There unfortunately isn't a way to increase the log precision like this. However, you can retrieve these lower and upper bounds programmatically with the \(\texttt{MIPNODE_OBJBST}\) and...

Hi Tianhao, Is the only difference between the models that you add some continuous variables that aren't used in the constraints or objective? If so, does this still occur with Gurobi 9.0.2? Thanks...

Hi Amogh, Could you post a small, self-contained code example that shows the two approaches producing different solutions? That would make it easier to tell what's going on. Thanks! Eli

Hi, The issue is that the \(\texttt{x}\) variable is only defined in the local scope of the \(\texttt{createmodel()}\) function. This means that you won't be able to access it outside of this funct...

Hi Valeria, Yes, this means that the difference between the LHS and RHS of constraint p23[86] is ~1.45e-10. This is well within the default feasibility tolerance used by Gurobi. This is the maximum...

Hi Carlo, You can retrieve the solution value of a variable by using GRBVar.get() to query the GRB.DoubleAttr.X attribute. With binary variables, it's good practice to check if the value is actuall...

This issue was resolved by upgrading from Gurobi 9.0.0 to Gurobi 9.0.2. Gurobi 9.0.0 was the first version to support non-convex MIQCPs, and this MIQCP-related bug has since been fixed. Eli

Hi Jongeun, Gurobi searches for globally optimal solutions to MIQCPs. We'll reach out to you through our online support portal to investigate this. The absolute gap is the absolute difference betwe...