diff --git a/exercise_2.py b/exercise_2.py
index fcfefd2..d8dea88 100644
--- a/exercise_2.py
+++ b/exercise_2.py
@@ -1,74 +1,79 @@
-# EXERCISE 2, PART 2 and 3.
+"""
+EXERCISE 2 - part 2 and 3
+Compare the Conjugate Gradient that has been implemented with the minimizer provided by SCIPY
+
+"""
 
 import argparse
 import numpy as np
 
 from conjugate_gradient import conjugate_gradient
 from optimizer import optimizer
 import plot
 
 # Initiate and describe parser
 parser = argparse.ArgumentParser(description='Find the minimizer of symmetric definite systems of linear equations by using the conjugate gradient method')
 
 # Define type of arguments to be received
 
 # Receive a list of elements to build matrix A and vector b
 parser.add_argument('elements', metavar='N', type=float, nargs='+',
                     help='enter 6 numbers: first 4 for matrix A in the order (1,1) (1,2) (2,1) (2,2), and last 2 for vector b. NOTE: matrix A in exercise 2 is equal to 2 * matrix of size 2x2 defined in exercise 1')
 
 # Receive the type of minimizer
 parser.add_argument('--method', default = 'scipy',
                     help='choose method to perform minimization, options are: "ourCG" or "scipy", if you do not define method then by default is "scipy"')
 
 # Receive instruction whether plor or not
 parser.add_argument('--plot', default = 'not',
                     help='indicate if you want to plot, options are: "yes" or "not", if you do not indicate any option then by default is "not"')
 
 # Get arguments
 args = parser.parse_args()
 
 # Build matrix A and vector b
 A = np.array([[args.elements[0], args.elements[1]], [args.elements[2], args.elements[3]]])
 b = np.array([args.elements[4], args.elements[5]])
 
 # NOTE: matrix A here (in exercise 2) is equal to 2 * matrix os size 2x2 defined in exercise 1
 
 # Construct the quadratic function S(x,y)
 def Sx(xy):
     A = np.array([[args.elements[0], args.elements[1]], [args.elements[2], args.elements[3]]])/2
     b = np.array([args.elements[4], args.elements[5]])
     x = np.asarray(xy[0])
     y = np.asarray(xy[1])
     xy = np.array([x, y])
     res = xy.T @ A @ xy - xy.T @ b
     return res
 
 # Initial guess, tolerance and maximum number of iterations for our conjugate gradient method
 x0 = np.array([2, 1])
 tol = 1e-9
 maxIter = 100;
 
 # Call corresponding optimizer function
 if args.method == 'ourCG':
     res = conjugate_gradient(A,b,x0,tol,maxIter)
     minimizer = res[0]
     points = np.asarray([res[1],res[2]]).transpose()
     method="CG"
 else:
     method = "BFGS"
     minimizer, resultINFO, points = optimizer(Sx, method, x0, tol)
 
 
 # Plot (in case)    
 if args.plot == 'yes':
     fig = plot.plotminimizer(Sx, points,method)
 else:
     print('not plot requested')
     
 # Print result
+print('_________________________________________________________')
 print('The minimizer is: x = ',  minimizer[0], ', y = ',  minimizer[1], 'and S(x,y) = ',
       Sx(minimizer))
 # Exact solution
 exact = np.linalg.inv(A)@b
 print('The exact solution is: x = ', exact[0], 'and y = ', exact[1], 
       'and S(x,y) = ',Sx([exact[0], exact[1]]))
\ No newline at end of file