I'm trying to get the constraint matrix from a Gurobi model in C++. In Python, one can get this by simply calling a method that will populate a scipy object (see this post). However, in a C++ fashion, one may expect three vectors (column, row, value) for the non-zero elements in the matrix.

One could technically iterate through the result of

GRBConstr* getConstrs() const;

however, I am not sure how efficient this method would be. Has anyone tried and found an efficient way of doing so?


One can also get the non-zero coefficients for a given variable with

GRBColumn getCol(GRBVar v)

EDIT 2: The first (possibly inefficient) idea is the following. Assume the object presolved stores the presolved model.

 auto         vars = presolved.getVars();
 //Getting the vars and iterating through them
 for (int v = 0; v < nVariables; ++v) {
 //Get the column of the v-th var
 auto varCol = presolved.getCol(vars[v]);

 if (varCol.size() > 0) { 
 //For any constraint in the number of constraints of the presolved model
    for (int c = 0; c < nconstr; ++c) {
      auto val = varCol.getCoeff(c);
      if (val > 1e-6 || val < -1e-6)
         // Fill the matrix A with the value
         A.at(c, v) = val;


  • $\begingroup$ If you have a large problem, you probably want to avoid dense matrices (I'm guessing A in A.at[c, v] is stored as a dense array). If you choose to work with a sparse matrix, I would expect the following to be decent: get the total number of non-zero coeffs, then populate a sparse column-wise matrix by iterating over the columns of the model. $\endgroup$
    – mtanneau
    Apr 13 '21 at 1:04
  • $\begingroup$ @mtanneau Yes, that would be the idea. I also missed to say that the column should already haveonly non-zero coefficients. If that is true, the solution works. And no, not necessarily A is dense, but as of my example, the loop is iterating over every (possibly zero) value $\endgroup$
    – gdragotto
    Apr 13 '21 at 13:19

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