diff --git a/hw1-conjugate-gradient/conjugate_gradient.py b/hw1-conjugate-gradient/conjugate_gradient.py
index 0938075c..75363f83 100644
--- a/hw1-conjugate-gradient/conjugate_gradient.py
+++ b/hw1-conjugate-gradient/conjugate_gradient.py
@@ -1,57 +1,60 @@
 ### THIS PROGRAM RUNS THE CONJUGATE GRADIENT ALGORITHM
 
-### IMPORT LIBRARIES
+################## IMPORT LIBRARIES ################################
 
 import numpy as np
 import scipy as sp
 #import matplotlib.pyplt as plt
 
-### DEFINE FUNCTION : conjgrad
-### DOES : CG algorithm
+
+############# DEFINE FUNCTION : conjgrad ###########################
+################# DOES : CG algorithm ##############################
 
 def conjgrad(A, b, x, tolerance, max_iterations):
 
 	# Dimension of problem
 	dim = len(b)
 
 	# Convert input to column vectors
 	b.reshape(dim, -1)
 	x.reshape(dim, -1)
 	print(x.shape)
 
 	# Initialization
 	k = 0
 	r = b - np.einsum('ij,j', A, x) # b - A @ x
 	p = r
 	if (np.sqrt(np.sum(r**2)) < tolerance):
 		return (x, k)
 
 	# CG Algorithm
 	for i in range(0, max_iterations):
 		alpha  = np.einsum('j,j',r,r) / np.einsum('j,j',p,np.einsum('ij,j', A, p))
 		x      = x + alpha*p
 		r_next = r - alpha*np.einsum('ij,j', A, p)
 		beta   = np.einsum('j,j',r_next,r_next)/np.einsum('j,j',r,r)
 		r      = r_next
 		p      = r + beta*p
 		k      = k + 1
 		if (np.sqrt(np.sum(r**2)) < tolerance):
 			break
 
 	return (x,k) # want to return x-array that minimizes S(x)
 
-### INPUT ARGUMENTS
+
+################################ INPUT ARGUMENTS ###################################################
 
 #A = np.array([[2.0, 3.0, 5.0], [2.0, 3.0 , 6.0], [3.0, 6.0, 9.0]])
 #b = np.array([2.0, 3.0, 5.0])
 #x = np.array([10.0, 20.5, 0.0]).T # initial guess
 A = np.array([[4.0, 0], [1.0, 3.0]])
 b = np.array([0.0, 1.0])
 x = np.array([4, 4]) # initial guess
 max_iterations = 1000
 tolerance = 0.001
 
-### RUN, PRINT RESULTS
+
+############################## RUN, PRINT RESULTS ##################################################
 
 result, k = conjgrad(A, b, x, tolerance, max_iterations)
 print("Minimum found at position ", result, " after ", k, " iterations.")
diff --git a/hw1-conjugate-gradient/main.py b/hw1-conjugate-gradient/main.py
new file mode 100644
index 00000000..818f5747
--- /dev/null
+++ b/hw1-conjugate-gradient/main.py
@@ -0,0 +1,32 @@
+### IMPORT LIBRARIES
+
+import numpy as np
+import scipy as sp
+import argparse
+
+#import quadratic_function
+#import optimizer
+#import conjugate_gradient
+
+
+### DEFINE PARAMETERS / COEFFICIENTS
+
+A = np.array([[4.0, 0.0], [1.0, 3.0]])
+b = np.array([0.0, 1.0])
+
+
+### ARGPARSE EXAMPLE : TO DO
+
+parser = argparse.ArgumentParser(description='Process Minimization Schemes')
+#parser.add_argument("A", help="Get the matrix A coefficients",
+#                    type=string)
+
+parser.add_argument('A', metavar='N', type=float, nargs='+',
+                    help='input matrix')
+#parser.add_argument('b', metavar='--b', type=float, nargs='+',
+#                    help='input vector')
+
+args = parser.parse_args()
+print(args.A)
+print(args.b)
+