diff --git a/hw1-conjugate-gradient/conjugate_gradient.py b/hw1-conjugate-gradient/conjugate_gradient.py
index 75a02559..d4035c1b 100644
--- a/hw1-conjugate-gradient/conjugate_gradient.py
+++ b/hw1-conjugate-gradient/conjugate_gradient.py
@@ -1,56 +1,56 @@
 ### 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 = 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([0, 0]) # initial guess
+x = np.array([4, 4]) # initial guess
 max_iterations = 1000
 tolerance = 0.001
 
 
 ### 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
 	if (np.sqrt(np.sum(r**2)) < tolerance):
 		return (x, k)
 	p = r
 
     # 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)
 
 result, k = conjgrad(A, b, x, tolerance, max_iterations)
 print("Minimum found at position ", result, " after ", k, " iterations.")