diff --git a/Homework1/3D_representration.pdf b/Homework1/3D_representration.pdf
deleted file mode 100644
index 0845f3a..0000000
Binary files a/Homework1/3D_representration.pdf and /dev/null differ
diff --git a/Homework1/post_processing.py b/Homework1/post_processing.py
index 0675a56..bfc3f2b 100644
--- a/Homework1/post_processing.py
+++ b/Homework1/post_processing.py
@@ -1,68 +1,68 @@
 #!/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(1) # 3D plot
     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.savefig('3D_representration.pdf', bbox_inches = 'tight')
+    plt.savefig('3D_representation.pdf', bbox_inches = 'tight')
 
     fig = plt.figure(2) # 2D contour plot
     ax = fig.add_subplot(111)
     ax.contour(xx, yy, S_post, colors = 'k', levels = 20)
     contour = ax.contourf(xx, yy, S_post, cmap = 'Greys', levels = 20)
     ax.plot(X_int[:,0], X_int[:,1], 'r--o')
     ax.set_xlabel('$x$', fontsize = 14)
     ax.set_ylabel('$y$', fontsize = 14)
     ax.set_title('Minimization method: %s' % method, fontsize = 14)
     fig.colorbar(contour, ax=ax)
     plt.savefig('2D_projection.pdf', bbox_inches = 'tight')
 
     plt.show()
 
     return 0