#*******************  EE556 - Mathematics of Data  ************************
# Function:  [x, info] = GD(fx, gradf, stepsize, x0, maxit)
# Purpose:   Implementation of the gradient descent algorithm.
# Input:     fx         - function handle to compute the objective
#            gradf      - function handle to compute the gradient
#            stepsize   - strictly positive stepsize parameter
#            x0         - initial point
#            maxit      - number of iterations
# Output:    x          - final estimate.
#            obj        - vector containing the objective values over
#                         iterations.
#*************************** LIONS@EPFL ***********************************

def GD(fx, gradf, stepsize, x0, maxit):



	return x, obj
#**************************************************************************
# END OF THE IMPLEMENTATION.
#**************************************************************************