diff --git a/exercises/week4/conjugate_gradient/sources-solution/optimizer.py b/exercises/week4/conjugate_gradient/sources-solution/optimizer.py
index 15e7324..d986ec9 100644
--- a/exercises/week4/conjugate_gradient/sources-solution/optimizer.py
+++ b/exercises/week4/conjugate_gradient/sources-solution/optimizer.py
@@ -1,42 +1,44 @@
 #!/usr/bin/env python3
 
 import matplotlib.pyplot as plt
 import numpy as np
 import scipy.optimize
 from mpl_toolkits.mplot3d import Axes3D
 
 
 def plot(func, iterations, title):
     'Plotting function for plane and iterations'
 
     x = np.linspace(-3, 3, 100)
     y = np.linspace(-3, 3, 100)
     x, y = np.meshgrid(x, y)
 
     X = np.stack([x.flatten(), y.flatten()], axis=1)
     z = func(X).reshape((100, 100))
 
     xy = np.array(iterations)
     zs = func(xy)
 
     fig = plt.figure()
     ax = Axes3D(fig)
     ax.plot_surface(x, y, z, linewidth=0, antialiased=True,
                     cmap=plt.cm.viridis, alpha=0.2)
     ax.contour(x, y, z, 10, colors="k", linestyles="solid")
     ax.plot(xy[:, 0], xy[:, 1], zs, 'ro--')
     ax.grid(False)
     ax.set_title(title)
 
     plt.xlabel('$x$')
     plt.ylabel('$y$')
     plt.show()
 
 
 def solve(fun, x0, tol='1e-8', callback=None, **kwargs):
     'Minimize function with BFGS'
 
-    res = scipy.optimize(fun, x0, method='BFGS',
-                         callback=callback,
-                         tol=tol)
+    res = scipy.optimize.minimize(
+        fun, x0, method='BFGS',
+        callback=callback,
+        tol=tol)
+
     return res.x