diff --git a/hw1-conjugate-gradient/conjugate_gradient.py b/hw1-conjugate-gradient/conjugate_gradient.py
index 3490f337..1aea021b 100644
--- a/hw1-conjugate-gradient/conjugate_gradient.py
+++ b/hw1-conjugate-gradient/conjugate_gradient.py
@@ -1,26 +1,39 @@
 #THIS PROGRAM RUNS THE CONJUGATE GRADIENT ALGORITHM
 
 # Import Libraries
 import numpy as np
 import scipy as sp
 import matplotlib.pyplt as plt
 
 # Input Arguments
-A = [[2.0, 3.0, 5.0], [2.0, 3.0 , 6.0], [3.0, 6.0, 9.0]]
-b = [2.0, 3.0, 5.0]
+# 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])
 
-float tolerance = 0.0005
+A = np.array([[4.0, 0], [1.0, 3.0]])
+b = np.array([0.0, 1.0])
+
+max_iterations = 1000
+tolerance = 0.0001
 
 # Define the function conjgrad
 # DOES : CG algorithm
-def conjgrad(A, b, x, tolerance):
+def conjgrad(A, b, x, tolerance, max_iterations):
 	# Initialization
-	int k = 0
-	r = b - np.matmul(A, x)
+	k = 0
+	r = b - np.matmul(A, x) # b - A @ x
 	p = r
-	
-	while(np.sqrt(np.sum(r**2)>tolerance):
-		k=k+1
-	
-	return(r);
-	
+
+	for i in range(0, max_iterations):
+		alpha  =
+		x      =
+		r_next =
+		beta   =
+		p      =
+		k      = k + 1
+		r      = r_next
+		# Bertil continues
+
+		if (np.sqrt(np.sum(r**2)) > tolerance):
+			break
+
+	return x # want to return x-array that minimizes S(x)