If have a general square matrix[[1,1,0],[1,1,0],[0,0,0]].(the element is on a binary field--Galois Fields) I want to find the rank of this matrix(like the function:np.linalg.matrix_rank). How can describe this process use gurobi?Appreciate your help. Due to the elements in the matrix are related to the vars in the gurobi model.I can't use the function of Numpy directly.
I tried asking a question in the Gurobi community and got a small model that worked.link But when I embedded it in my original big model. The model becomes a multi-objective optimization problem, and the original objective function will affect the optimization process of rank finding. The rank of a matrix should be a property of certainty. However, in my big model, in order to also optimize the original goal, the rank of the matrix is not optimal (that is, it becomes wrong). Even though I adjusted the priority of the multi-target, I still couldn't get the correct rank. I wonder if there is a solution that doesn't use objective function.Appreciate your help!