Added part c. Done!

Assignment 1/assignment1.py

 import numpy as np
+import numpy
+from scipy.optimize import minimize
 
 # Import text files 
 Q = np.asmatrix(np.loadtxt('Q.txt'))
@@ -6,10 +8,17 @@ b = np.asmatrix(np.loadtxt('b.txt'))
 c = np.asmatrix(np.loadtxt('c.txt'))
 b = np.transpose(b) #make b a column vector
 m = 1
+x_c = np.transpose(np.asmatrix(np.zeros(np.size(b)))*1)
 
 # Define f(x)
-def f(Q,b,c,x): 
-	return np.transpose(x)*Q*x + np.transpose(b)*x + c
+def f(Q,b,c,x):	
+	if np.shape(x) == (5L,1L):
+		ans = np.transpose(x)*Q*x + np.transpose(b)*x + c
+	else:
+		
+		x = np.transpose(np.asmatrix(x))
+		ans =  np.transpose(x)*Q*x + np.transpose(b)*x + c
+	return ans
 	
 # Define gradient f(x)
 def gradf(Q,b,x):
@@ -63,6 +72,17 @@ def partb(Q,b,x):
 	print f(Q,b,c,x)
 	print 'f(x*) is', f(Q,b,c,ans)
 	return 0
+
+class partcfun:
+	def __init__(self,arg1):
+		self.arg1 = arg1
+	def __call__(self,arg1):
+		return f(Q,b,c,arg1)
+
+def partc(x_0c):	
+	cans = minimize(partcfun(x_c),x_0c)
+	print 'x is', cans
+	return 0
 	
 def run(epsilon):
 	# Make a guess for x vector
@@ -76,6 +96,9 @@ def run(epsilon):
 	grad_opt(epsilon,x,count,alpha0)
 	print '******** Part B ********'
 	partb(Q,b,x)
+	print '******** Part C ********'
+	x_0c = np.transpose(np.asmatrix(np.ones(np.size(b)))*1)
+	partc(x_0c)
 	return 0 
 	
 run(0.00001)
\ No newline at end of file