diff --git a/hw1-conjugate-gradient/optimizer.py b/hw1-conjugate-gradient/optimizer.py
index a22b22b1..5b9aa27e 100644
--- a/hw1-conjugate-gradient/optimizer.py
+++ b/hw1-conjugate-gradient/optimizer.py
@@ -1 +1,57 @@
 import numpy as np
+import matplotlib.pyplot as plt
+from mpl_toolkits.mplot3d import Axes3D
+import scipy.optimize
+
+
+def my_callback(xk):
+    mystring = str(xk[0]) + "\t" + str(xk[1]) + "\n"
+    with open("minimize_steps.txt", "a") as myfile:
+        myfile.write(mystring)
+
+def find_minimum(func):
+    result = scipy.optimize.minimize(func, x0 = np.array([4.0, 4.0]), method='BFGS', callback=my_callback)
+    return result.x
+
+# This function implements S(x) given in the exercise sheet
+def quadratic_function(x):
+
+    # create column vector of input array
+    x.reshape(-1, 1)
+
+    # create matrix and vector coefficients of S(x) given in exercise sheet
+    mat = np.array([[4, 0], [1, 3]])
+    vec = np.array([0, 1]).transpose()
+
+    # return S(x)
+    return (x.transpose() @ mat) @ x - x.transpose() @ vec
+
+
+find_minimum(quadratic_function)
+
+
+def plotting():
+    fig = plt.figure()
+    axe = fig.add_subplot(111, projection="3d")
+    num_of_points = 100
+    xmax = ymax = -10
+    xmin = ymin = 10
+    X, Y = np.meshgrid(np.linspace(xmin,xmax,num_of_points), np.linspace(ymin,ymax,num_of_points))
+    Z = np.zeros_like(X)
+    for i in range(0,num_of_points):
+        for j in range(0, num_of_points):
+            Z[i,j] = quadratic_function( np.array([X[i,j], Y[i,j]]) )
+    axe.plot_surface(X, Y, Z)
+    #axe.view_init(orientation=30)
+    #axe.contourf(X,Y,Z)
+
+    step = np.loadtxt("minimize_steps.txt")
+    step_value = np.zeros(step.shape[0])
+    for i in range(0, step.shape[0]):
+        step_value[i] = quadratic_function( np.array([step[i, 0], step[i, 1]]) )
+    axe.plot(step[:,0], step[:,1], step_value, 'r*--')
+    #axe.plot(step[:,0], step[:,1], 'r*--')
+
+    plt.show()
+
+plotting()