Modified Armijo's Rule. Faster convergence, but I'm not sure if it is correct

Assignment 1/assignment1.py

@@ -6,7 +6,7 @@ b = np.asmatrix(np.loadtxt('b.txt'))
 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
+m = 1
 
 # Make a guess for x vector
 #x = np.asmatrix(np.zeros(np.size(b)))
@@ -14,8 +14,9 @@ m = 0
 #x=np.matrix(np.random.rand(5,1))
 x = np.transpose(np.asmatrix(np.ones(np.size(b)))*0.1)
 
-alpha0 = 1000.0
+alpha0 = 1.0
 count = 0
+
 # Define f(x)
 def f(Q,b,c,x): 
 	return np.transpose(x)*Q*x + np.transpose(b)*x + c
@@ -32,7 +33,7 @@ def armijo(alpha0,Q,b,c,D,m):
 	s = 1.0
 	sigma = 1.0
 	beta = 1.0/2
-	while f(Q,b,c,x+np.transpose(beta**m*alpha*D)) > f(Q,b,c,x) + sigma*beta**m*alpha*(np.transpose(gradf(Q,b,x)))*np.transpose(D):
+	while f(Q,b,c,x+np.transpose(beta**m*s*D)) < f(Q,b,c,x) + sigma*beta**m*s*(np.transpose(gradf(Q,b,x)))*np.transpose(D):
 		m+=1
 		#print('m is ', m)
 		alpha = beta**m*s
@@ -60,7 +61,7 @@ def grad_opt(epsilon,x,count,alpha):
 			
 		#print('norm of gradf(x) is ', np.linalg.norm(gradf(Q,b,x)))
 		x -= alpha*gradf(Q,b,x)
-		alpha = alpha0
+		
 				
 	print '\nDone at ', count,'th iteration'
 	print 'x* is ', x