[examples/julia] partial revert

examples/julia/overview-simple.jl

@@ -19,32 +19,33 @@ function generate_mixed_qp(n, seed=1)
 
     P = spa.random(
         n, n, density=0.85, data_rvs=np.random.randn, format="csc"
-        ).toarray(order="C")
+        ).toarray()
     P = (P + np.transpose(P)) / 2.0
 
     s = np.max(np.absolute(np.linalg.eigvals(P)))
     P += (np.abs(s) + 1e-02) * spa.eye(n)
-    P = spa.coo_matrix(P).toarray(order="C")
+    P = spa.coo_matrix(P)
     q = np.random.randn(n)
-    A = spa.random(m, n, density=0.85, data_rvs=np.random.randn, format="csc").toarray(order="C")
+    A = spa.random(m, n, density=0.85, data_rvs=np.random.randn, format="csc")
     v = np.random.randn(n)  # Fictitious solution
-    delta = np.random.rand(m)  # To get inequality
     u = A * v
     l = -1.0e20 * np.ones(m)
 
-    return P, q, get(A, slice(0, n_eq)), get(u, slice(0, n_eq)), get(A, slice(n_in, m)), get(u, slice(n_in, m, )), l[n_in+1:end]
+    return P.toarray("C"), q, get(A, slice(0, n_eq)), get(u, slice(0, n_eq)), get(A, slice(n_in, m)), get(u, slice(n_in, m, )), l[n_in+1:end]
 end
 
 # generate a qp problem
 n = 10
 H, g, A, b, C, u, l = generate_mixed_qp(n)
-n_eq = A.shape[1]
-n_in = C.shape[1]
+A = A.toarray("C")
+C = C.toarray("C")
+n_eq = size(A, 1)
+n_in = size(C, 1)
 
 # solve it
 qp = proxsuite.proxqp.dense.QP(n, n_eq, n_in)
 qp.settings.eps_abs = EPS
-qp.init(H, g, A.toarray(), b, C.toarray(), l, u)
+qp.init(H, g, A, b, C, l, u)
 qp.solve()
 
 x_res = qp.results.x