[_dq_quadprog_solver.py] Addressing bug when called via cpp.

Empty matrices are sent by the `cpp` side and these were not being processed correctly.

Author: mmmarinho

dqrobotics/solvers/_dq_quadprog_solver.py

@@ -62,6 +62,16 @@ def solve_quadratic_program(self, H, f, A, b, Aeq, beq):
          :param beq: the m x 1 value for the inequality constraints.
          :return: the optimal x
         """
+        # Calls from dqrobotics-cpp will trigger these conditions if empty matrices/vectors are arguments.
+        if A.shape == (0, 0):
+            A = None
+        if b.shape == 0:
+            b = None
+        if Aeq.shape == (0, 0):
+            Aeq = None
+        if beq.shape == 0:
+            beq = None
+        
         if A is None and b is None and Aeq is None and beq is None:
             raise ValueError("A, b, Aeq, beq, cannot all be None.")
         if (A is None and b is not None) or (b is None and A is not None):
@@ -73,12 +83,9 @@ def solve_quadratic_program(self, H, f, A, b, Aeq, beq):
         ## Aeq.x <= beq + delta
         ## Aeq.x >= beq - delta ==> -Aeq.x <= -beq + delta
         if Aeq is not None: # beq is None already checked by the ValueError
-            if Aeq.shape == (0, 0) or beq.shape == 0:
-                pass
-            else:
-                Aeq = np.vstack([Aeq, -Aeq])
-                beq = beq.reshape(-1)
-                beq = np.concatenate([beq + self.equality_constraints_tolerance, -beq + self.equality_constraints_tolerance])
+            Aeq = np.vstack([Aeq, -Aeq])
+            beq = beq.reshape(-1)
+            beq = np.concatenate([beq + self.equality_constraints_tolerance, -beq + self.equality_constraints_tolerance])
 
         # Use (A,b), (Aeq,beq), or both.
         if Aeq is None:
@@ -91,11 +98,6 @@ def solve_quadratic_program(self, H, f, A, b, Aeq, beq):
             A_internal = np.vstack([A, Aeq])
             b_internal = np.concatenate([b.reshape(-1), beq])
 
-        # Calls from DQRobotics CPP will trigger this condition
-        if A_internal.shape == (0, 0) or b_internal.shape == 0:
-            A_internal = np.zeros((1, H.shape[0]))
-            b_internal = np.zeros(1)
-
         (x, f, xu, iterations, lagrangian, iact) = quadprog.solve_qp(G=H,
                                                                      a=-f,
                                                                      C=-np.transpose(A_internal),