I'm trying to use a binary integer linear program to assign members of my staff to different shift. I have a 16x9 matrix of preferences for my staff in a csv (16 staff members, 9 slots to fill) and I used the following code to try and assign them:
weights = pd.read_csv("holiday_green day.csv", index_col= 0)
weights = weights.to_numpy().astype(float)
selection = cvx.Variable((9,16), boolean = True)
row_sum_vector = np.ones((16,1)).astype(float)
result_constraint = np.ones((9,1)).astype(float) * 2
objective = cvx.Minimize(cvx.trace(weights @ assignments))
prob = cvx.Problem(objective, [assignments @ row_sum_vector == result_constraint])
prob.solve()
When I try running this, I get the error TypeError: G must be a 'd' matrix and I don't know where to start debugging. I looked at this post, but it wasn't helpful. Can someone help me figure out what G is and what it means by 'd' matrix? Its my first time actually using CVXPY and I'm very lost.
Full Stack Trace:
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-23-d07ad22cbc25> in <module>()
6 objective = cvx.Minimize(cvx.atoms.affine.trace.trace(weights @ assignments))
7 prob = cvx.Problem(objective, [assignments @ row_sum_vector == result_constraint])
----> 8 prob.solve()
3 frames
/usr/local/lib/python3.7/dist-packages/cvxpy/problems/problem.py in solve(self, *args, **kwargs)
288 else:
289 solve_func = Problem._solve
--> 290 return solve_func(self, *args, **kwargs)
291
292 @classmethod
/usr/local/lib/python3.7/dist-packages/cvxpy/problems/problem.py in _solve(self, solver, warm_start, verbose, parallel, gp, qcp, **kwargs)
570 self._intermediate_problem)
571 solution = self._solving_chain.solve_via_data(
--> 572 self, data, warm_start, verbose, kwargs)
573 full_chain = self._solving_chain.prepend(self._intermediate_chain)
574 inverse_data = self._intermediate_inverse_data + solving_inverse_data
/usr/local/lib/python3.7/dist-packages/cvxpy/reductions/solvers/solving_chain.py in solve_via_data(self, problem, data, warm_start, verbose, solver_opts)
194 """
195 return self.solver.solve_via_data(data, warm_start, verbose,
--> 196 solver_opts, problem._solver_cache)
/usr/local/lib/python3.7/dist-packages/cvxpy/reductions/solvers/conic_solvers/glpk_mi_conif.py in solve_via_data(self, data, warm_start, verbose, solver_opts, solver_cache)
73 data[s.B],
74 set(int(i) for i in data[s.INT_IDX]),
---> 75 set(int(i) for i in data[s.BOOL_IDX]))
76 results_dict = {}
77 results_dict["status"] = results_tup[0]
TypeError: G must be a 'd' matrix
Edit: Tried casting all numpy arrays as float like they suggested in a different post. It didn't work.
