diff --git a/Homework1/__pycache__/post_processing.cpython-37.pyc b/Homework1/__pycache__/post_processing.cpython-37.pyc
new file mode 100644
index 0000000..bcf0011
Binary files /dev/null and b/Homework1/__pycache__/post_processing.cpython-37.pyc differ
diff --git a/Homework1/optimizer.py b/Homework1/optimizer.py
index 9316ea2..ec701b8 100644
--- a/Homework1/optimizer.py
+++ b/Homework1/optimizer.py
@@ -1,76 +1,53 @@
 #!/usr/bin/env python3
 # -*- coding: utf-8 -*-
 """
 Created on Thu Oct 10 10:19:11 2019
 Authors: Omid Ashtari and Armand Sieber
 Description:    Scripts intended to minimize a quadratic function using scipy.optimize built-in function.
                 The function to minimize is defined as S(X)= X'AX - X'B, with X = [x, y], X' the transform of X
                 A is a 2x2 matrix and B 2X1 vector
 """
 
 import numpy as np
 import sys
 from scipy.optimize import minimize
+import post_processing
 
-import matplotlib.pyplot as plt
-from mpl_toolkits.mplot3d import Axes3D
-
-def objective(x, *args): #objective function to be minimized
+def objective(X, *args): #objective function to be minimized
     A = args[0]
     B = args[1].T
-    S = np.dot(x.T, np.dot(A, x)) - np.dot(x.T, B)
+    S = np.dot(X.T, np.dot(A, X)) - np.dot(X.T, B)
     return S
 
-def reporter(x_r):
-    global x_int
-    x_int = np.vstack([x_int, x_r])
+def reporter(X_r):
+    global X_int
+    X_int = np.vstack([X_int, X_r])
     return 0
 
 # Import minimization method
 if len(sys.argv) < 2:
     print('Solver method missing. Default method implemented --> CG')
     method = 'CG'
 else:
     method = sys.argv[1]
 
 available_methods = ('Nelder-Mead', 'Powell', 'CG', 'BFGS', 'L-BFGS-B', 'TNC', 'SLSQP')
 
 if method in available_methods: # test availibility of user inputs
     print('Minimzation performed with the %s solver' % method)
 else:
     sys.exit('Minimization method missing or not available!')
 
 # Parameters of the function to minimize
 A = np.array([[4, 0], [1, 3]])
 B = np.array([0, 1])
 
 # Minimization process
 X0 = np.array([3, 2]) #initial guess
-x_int = X0 #stores solution at each step
+X_int = X0 #stores solution at each step
 solution = minimize(objective, X0, args = (A, B), method = method, callback = reporter,options = {'maxiter':100})
 print(solution)
-print(x_int)
+print(X_int)
 
 # Post-processing
-X_domain = np.linspace(-1.2*X0[0], 1.2*X0[0], 100)
-Y_domain = np.linspace(-1.2*X0[0], 1.2*X0[0], 100)
-xx, yy = np.meshgrid(X_domain, Y_domain)
-S_post = np.array([objective(np.array([x , y]), A, B)
-                for x, y in zip(np.ravel(xx), np.ravel(yy))]) # Value of S for 3D plot (whole X-Y domain)
-S_post = S_post.reshape(xx.shape)
-
-S_int = np.array([objective(np.array([x , y]), A, B)
-                for x, y in zip(x_int[:,0], x_int[:,1])]) # Value of S at intermediate minimization steps
-
-
-fig = plt.figure()
-ax = plt.axes(projection = '3d')
-ax.plot3D(x_int[:,0], x_int[:,1], S_int, 'r--o', alpha = 1.0, zorder = 10)
-surface = ax.plot_surface(xx, yy, S_post, cmap = 'Greys', edgecolor = 'none', alpha = 1.0, zorder = 0)
-ax.set_xlabel('$x$', fontsize = 14)
-ax.set_ylabel('$y$', fontsize = 14)
-ax.set_zlabel('$S$', fontsize = 14)
-ax.view_init(elev = 60.0, azim = 130.0)
-ax.set_title('Minimization method: %s' % method, fontsize = 14, pad = 32)
-#fig.colorbar(surface, shrink = 0.5, aspect = 5)
-plt.show()
+post_processing.plot_results(X_int, A, B, method)
diff --git a/Homework1/post_processing.py b/Homework1/post_processing.py
new file mode 100644
index 0000000..07695fc
--- /dev/null
+++ b/Homework1/post_processing.py
@@ -0,0 +1,55 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Mon Oct 14 15:19:11 2019
+Authors: Omid Ashtari and Armand Sieber
+Description:    Scripts intended to display minimization proplems results
+"""
+
+import numpy as np
+
+import matplotlib.pyplot as plt
+from mpl_toolkits.mplot3d import Axes3D
+
+def objective(X, *args): #objective function to be minimized
+    A = args[0]
+    B = args[1].T
+    S = np.dot(X.T, np.dot(A, X)) - np.dot(X.T, B)
+    return S
+
+def plot_results(X_int, A, B, method):
+
+    # Pre-processing the data to be dispalyed
+    ##########################################
+
+    # Set domain size and coordinates according to intermediate solution of minimization problem
+    x1 = np.max(np.abs(X_int[:, 0]))
+    x2 = np.max(np.abs(X_int[:, 1]))
+    X_domain = np.linspace(-1.5*x1, 1.5*x1, 100)
+    Y_domain = np.linspace(-1.5*x2, 1.5*x2, 100)
+    xx, yy = np.meshgrid(X_domain, Y_domain)
+
+    # Compute S for the computational domain (S_post) and intermediate minimization solution (S_int)
+    S_post = np.array([objective(np.array([x , y]), A, B)
+                    for x, y in zip(np.ravel(xx), np.ravel(yy))]) # Value of S for 3D plot (whole X-Y domain)
+    S_post = S_post.reshape(xx.shape)
+
+    S_int = np.array([objective(np.array([x , y]), A, B)
+                    for x, y in zip(X_int[:,0], X_int[:,1])]) # Value of S at intermediate minimization steps
+
+    # Dispalying the results
+    ########################
+
+    fig = plt.figure()
+    ax = plt.axes(projection = '3d')
+    ax.plot3D(X_int[:,0], X_int[:,1], S_int, 'r--o', alpha = 1.0, zorder = 10)
+    surface = ax.plot_surface(xx, yy, S_post, cmap = 'Greys', edgecolor = 'none', alpha = 1.0, zorder = 0) # zorder 'best' solution found to plot S_int over S_post
+                                                                                                            # --> alternative using mayavi
+    ax.set_xlabel('$x$', fontsize = 14)
+    ax.set_ylabel('$y$', fontsize = 14)
+    ax.set_zlabel('$S$', fontsize = 14)
+    ax.view_init(elev = 60.0, azim = 130.0)
+    ax.set_title('Minimization method: %s' % method, fontsize = 14, pad = 32)
+    plt.show()
+
+    return 0