Maliheh Aramon

Hi,  The Model.addGenConstrPow() general constraint API in Gurobi can be used to represent constraints in the form of \(y = x^a\) where \(y\) and \(x\) are instances of Gurobi Var objects and \(a\)...

Hi WI,  Please see the snippet edited above. Specifically, below needs to change from: xmin = gp.min_(Var)xmax = gp.max_(Var) to: model.addConstr(xmin == gp.min_(Var), name="min") model.addConstr(x...

Hi WI,  The error means that the subtraction is not supported for the general expressions, i.e., we cannot subtract \(x_{\small\mbox{min}}\) from \(x_{\small\mbox{max}}\). We likely need additional...

Hi Jia,  The following should get you started. Gurobi has an interface for modelling piecewise-linear objective functions. The linked section explains in detail how to define piecewise-linear fun...

Hi WI,  Since \(\texttt{Var_min}\) and \(\texttt{Var_max}\) are variables in your model, normalizing the decision variables as \((\texttt{Var[i]} - \texttt{Var_min})/(\texttt{Var_max} - \texttt{Var...

Hi Maedeh,  The simplest approach would be to manipulate the variable string names to retrieve the indices \(i\) and \(j\). You can modify the assignment variable names, \(\texttt{assignment[i,j]}\...

Hi Natalie,  How did you identify that the xx values in the condition xj>=xixj>=xi are for the coordinates and not the xkixik or xkjxjk boolean variables? I ask because I had assumed that the jj f...

Hi Natalie,  Based on the paper (page 4), it seems to me that each fruit \(i\) has known \((x_i, y_i)\) coordinate values . You just need to ensure that the second constraint is implemented for the...

Hi Jacob,  The callback routines in Gurobi take two arguments: \(\texttt{where}\) and \(\texttt{what}\). The former argument tells the Gurobi Optimizer from where the callback should be called and ...

Hi Fikri, Yes, you are right. Almost all modern computers use double precision (64 bits) nowadays. The parameter Quad is specific to the simplex algorithm and the warning message "switch to quad pr...