diff --git a/hw1-conjugate-gradient/main.py b/hw1-conjugate-gradient/main.py
deleted file mode 100644
index 818f5747..00000000
--- a/hw1-conjugate-gradient/main.py
+++ /dev/null
@@ -1,32 +0,0 @@
-### IMPORT LIBRARIES
-
-import numpy as np
-import scipy as sp
-import argparse
-
-#import quadratic_function
-#import optimizer
-#import conjugate_gradient
-
-
-### DEFINE PARAMETERS / COEFFICIENTS
-
-A = np.array([[4.0, 0.0], [1.0, 3.0]])
-b = np.array([0.0, 1.0])
-
-
-### ARGPARSE EXAMPLE : TO DO
-
-parser = argparse.ArgumentParser(description='Process Minimization Schemes')
-#parser.add_argument("A", help="Get the matrix A coefficients",
-#                    type=string)
-
-parser.add_argument('A', metavar='N', type=float, nargs='+',
-                    help='input matrix')
-#parser.add_argument('b', metavar='--b', type=float, nargs='+',
-#                    help='input vector')
-
-args = parser.parse_args()
-print(args.A)
-print(args.b)
-
diff --git a/hw1-conjugate-gradient/main_minimize.py b/hw1-conjugate-gradient/main_minimize.py
index 1fd03c2f..9dda10c7 100644
--- a/hw1-conjugate-gradient/main_minimize.py
+++ b/hw1-conjugate-gradient/main_minimize.py
@@ -1,53 +1,49 @@
 ### IMPORT LIBRARIES
-
 import numpy as np
 import scipy as sp
 import argparse
 
 #import quadratic_function
 #import optimizer
 from conjugate_gradient import conjgrad
 
 ### DEFINE PARAMETERS / COEFFICIENTS
-
 A = np.array([[4.0, 0.0], [1.0, 3.0]])
 b = np.array([0.0, 1.0]).T
 x = np.array([4, 4]).T # initial guess
 max_iterations = 1000
 tolerance = 0.001
 
-### ARGPARSE
-### DOES : SET VIA INTERFACE the Coefficients and Optimization Method
-
+### DEFINE DICTIONARY TO SET THE OPTIMIZATION METHOD TO RUN
 FUNCTION_MAP = {'CG' : conjgrad,
-                'minimizer' : min}
+                'BFGS' : min}   ## To Do : BFGS matching, CallBack, Plots Option Argspace
+
+### ARGPARSE
+### DOES : SET(via interface) Matrix Coefficients + Optimization Method
 
 parser = argparse.ArgumentParser(description='Process Minimization Schemes')
 parser.add_argument('-i',action='append', nargs='+',help='Input both : -i matrix -i vector')
-parser.add_argument('-method', choices=FUNCTION_MAP.keys())
-#parser.add_argument('--method', dest='minimize', action='store_const',
-#			const=min, default=conjgrad,
-#			help='minimize the system (default: use CG algorithm)')
-
+parser.add_argument('-method', choices=FUNCTION_MAP.keys(),help='Choose method of minimization (CG or BFGS)')
 args = parser.parse_args()
 
 A = np.array([float(k) for k in args.i[0]])
 b = np.array([float(k) for k in args.i[1]])
 
-dim = int(np.sqrt(len(A))) # Get the problem dimension
-
+dim = len(b) # int(np.sqrt(len(A))) # Get dimension for reshape
 A = np.reshape(A,(dim,dim))
-b = np.reshape(b,(1,dim))
+b.reshape(dim, -1)
 
-### PRINT SET UP
-
-print("Matrix A: ",A)
+### PRINT INITIAL STATE 
+print("Matrix A: ",A.shape)
 print("Vector b: ",b)
+print("x0: ",x)
+print("tolerance: ",tolerance)
+print("max_iterations: ",max_iterations)
 
+### RUN THE OPTIMIZATION
 func = FUNCTION_MAP[args.method]
-[results, k] = func(A, b, x, tolerance, max_iterations)
-
-#args.method(A, b, x, tolerance, max_iterations)
+[result, k] = func(A, b, x, tolerance, max_iterations)
 
-print("Result :",results," ", k)
+### PRINT RESULTS
+print("Results :",result," ", k)