diff --git a/Assignment 1/assignment1.py b/Assignment 1/assignment1.py
index 7938956970908526ef8687a03dc700e8b9da7891..5c132534b2bbe77e2dccee56ca60dbb6b584c109 100644
--- a/Assignment 1/assignment1.py	
+++ b/Assignment 1/assignment1.py	
@@ -7,6 +7,7 @@ c = np.asmatrix(np.loadtxt('c.txt'))
 b = np.transpose(b) #make b a column vector
 D = np.asmatrix(np.ones(np.size(b)))
 m = 0
+
 # Make a guess for x vector
 #x = np.asmatrix(np.zeros(np.size(b)))
 #x = np.transpose(x) #make column vector 
@@ -55,16 +56,16 @@ def grad_opt(epsilon,x,count,alpha):
 		#print('alpha0 is ', alpha)
 		count += 1
 		if count%1000==0:
-			print('f(x) is ', f(Q,b,c,x))
+			print 'f(x) is ', f(Q,b,c,x)
 			
 		#print('norm of gradf(x) is ', np.linalg.norm(gradf(Q,b,x)))
 		x -= alpha*gradf(Q,b,x)
 		alpha = alpha0
 				
-	print('Done')
-	print('x* is ', x)
-	print('f(x*) is ', f(Q,b,c,x))
-	print('epsilon is ', epsilon)
+	print '\nDone at ', count,'th iteration'
+	print 'x* is ', x
+	print 'f(x*) is ', f(Q,b,c,x)
+	print 'epsilon is ', epsilon
 	return 0
 
 
@@ -74,11 +75,7 @@ def run(epsilon):
 	grad_opt(epsilon,xstart,countstart,alpha0)
 	return 0 
 	
-run(0.889)
-
+run(0.8)
 
-		
-		
-#def simpgrad_opt(e)