diff --git a/README.md b/README.md
index d0cf3024..b4d7adb0 100644
--- a/README.md
+++ b/README.md
@@ -1,31 +1,33 @@
 # SP4E Homeworks
 Students:  
 * Lars B.
 * Bertil T.
 
 In this repository, we will keep all homeworks for SP4E
 
 ## Homework 1
-See folder named *hw1-conjugate-gradient*  
+See folder named *hw1-conjugate-gradient*.
 
 #### Requirements
 * Numpy
 * Scipy
 * Matplotlib
 * Argparse
 
 The code as been tested with Python 3.
 
 #### Usage
 Run the *main_minimize.py* with the following arguments:
 * -A < matrix elements in row major order >
 * -b < vector elements >
 * -x0 < initial guess elements >
 * -method < CG-scipy or CG-ours >
 * -plot < True or False >
 
 #### Example
 
-The following command will run the program with the example values given in Exercise 1:  
+The following command will run the program with the coefficients given in Exercise 1:  
 
-python main_minimize.py -A 4 0 1 3 -b 0 1 -method CG-scipy -plot True -x0 4 4
+python main_minimize.py -A 8 0 2 6 -b 0 1 -x0 4 4 -method CG-scipy -plot True   
+
+NB: Note the scaling of S(x) with 1/2 between Ex 1 and 2.
diff --git a/hw1-conjugate-gradient/plotting.py b/hw1-conjugate-gradient/plotting.py
index 48507426..7f9d6f6a 100644
--- a/hw1-conjugate-gradient/plotting.py
+++ b/hw1-conjugate-gradient/plotting.py
@@ -1,102 +1,102 @@
 import numpy as np
 import sys
 import matplotlib.pyplot as plt
 from matplotlib import cm
 from mpl_toolkits.mplot3d import Axes3D
 
 from quadratic_function import quadratic_function
 
 # This function plots the S(x) surface and includes the path taken by the minimizer
 def plotting(dim, steps, x0, *args):
     # If we are just dealing with scalars
     if dim == 1:
         # Create figure
         fig = plt.figure(figsize=(14,8))
         axe = fig.add_subplot(111)
 
         # Discretization of plot
         num_of_points = 100
 
         # Upper and lower bound of plot
         xmin = min(x0[0], -5)
         xmax = max(x0[0], +5)
 
         # Discretized x
         X = np.linspace(xmin,xmax,num_of_points)
 
         # Initialize S(x)
         Z = np.zeros_like(X)
 
         # Fill S(x)
         for i in range(0,num_of_points):
                 Z[i] = quadratic_function( np.array([ X[i] ]), *args )
 
         # Make the plot of S(x)
         sp = axe.plot(X, Z, 'b-')
 
         # Initialize step values
         step_value = np.zeros(steps.shape[0])
 
         # Fill step values
         for i in range(0, steps.shape[0]):
             step_value[i] = quadratic_function( np.array([ steps[i, 0] ]), *args )
 
         # Plot the steps taken in the same plot
         axe.plot(steps[:,0], step_value, 'r*--')
 
         # Set label and save plot
         axe.set_xlabel('x', fontsize=15)
         axe.set_ylabel('S(x)', fontsize=15)
         plt.savefig("plot_1d.png")
 
     # If \bf{x} = (x, y)
     elif dim == 2:
         # Create figure
         fig = plt.figure(figsize=(14,8))
         axe = fig.add_subplot(111, projection="3d")
 
         # Discretization of plot
         num_of_points = 100
 
         # Upper and lower bound of plot
         xmin = min(x0[0], -5)
         xmax = max(x0[0], +5)
         ymin = min(x0[1], -5)
         ymax = max(x0[1], +5)
 
         # Discretized x on a mesh
         X, Y = np.meshgrid(np.linspace(xmin,xmax,num_of_points), np.linspace(ymin,ymax,num_of_points))
 
         # Initialize S(x,y)
         Z = np.zeros_like(X)
 
         # Fill S(x, y)
         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]]), *args )
 
         # Make the plot of S(x)
         sp = axe.plot_surface(X, Y, Z, cmap=cm.coolwarm)
         axe.view_init(60, 100)
         fig.colorbar(sp)
 
         # Initialize step values
         step_value = np.zeros(steps.shape[0])
 
         # Fill step values
         for i in range(0, steps.shape[0]):
             step_value[i] = quadratic_function( np.array([steps[i, 0], steps[i, 1]]), *args )
 
         # Plot the steps taken in the same plot
         axe.plot(steps[:,0], steps[:,1], step_value, 'r*--')
 
         # Set label and save plot
         axe.set_xlabel('x', fontsize=15)
         axe.set_ylabel('y', fontsize=15)
         axe.set_zlabel('       S(x,y)', fontsize=15)
         axe.zaxis.set_rotate_label(False)
         plt.savefig("plot_2d.png")
 
-    # If higher dimensions    
+    # If higher dimensions
     else:
         print("Cannot make a high dimensional plot")
diff --git a/hw1-conjugate-gradient/quadratic_function.py b/hw1-conjugate-gradient/quadratic_function.py
index e3b5b4d8..fba92c05 100644
--- a/hw1-conjugate-gradient/quadratic_function.py
+++ b/hw1-conjugate-gradient/quadratic_function.py
@@ -1,26 +1,31 @@
 import numpy as np
 
 # This function implements S(x) as given in the exercise sheet
+
 # If matrix A or vector b are not specified through "args", the function assumes
 # that A and b are those given in Exercise 1
-# Not that you CANNOT only specify A or b, both must be specified such that
+
+# Note that you CANNOT only specify A or b, both must be specified such that
 # args[0] = A and args[1] = b
+
+# Note also that the function is implemented with the factor 1/2 as in Exercise 2
+# Hence the scaling of matrix A with 2 in order to reproduce the case in Exercise 1
 def quadratic_function(x, *args):
 
     if len(args) == 0:
-        A = np.array([[4, 0], [1, 3]])
+        A = np.array([[4.0, 0], [1, 3]]) * 2.0
         b = np.array([0, 1])
     else:
         A = args[0]
         b = args[1]
 
     # reshape into column vectors
     x.reshape(-1, 1)
     b.reshape(-1, 1)
 
     # return S(x)
-    return ((x.transpose() @ A) @ x) - (x.transpose() @ b)
+    return 0.5*((x.transpose() @ A) @ x) - (x.transpose() @ b)
 
 # TESTING AND VALIDATING FUNCTION
 #print(quadratic_function(np.array([1,2]) ))
 #print(quadratic_function(np.array([1,2]), np.array([[4, 0], [1, 3]]), np.array([0, 1]) ))