optimizer.py
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	# Import libraries
	import numpy as np
	import scipy.optimize

	### Global variables (only necessary for plotting of steps taken by optimizer)
	# Dimension of problem, default value = 2 will be overwritten by main_minimize
	DIM = 2
	# Initial guess to minimizer, default value = (4,4) will be overwritten by main_minimize
	STEPS = np.array([4, 4])
	# This array will contain the steps of the minimizer on its way to the minimum
	STEPS = STEPS.reshape(-1, DIM)


	### This function fills the STEPS array, and is used as input to scipy.optimize.minimize
	def my_callback(xk):
	global STEPS
	STEPS = np.vstack((STEPS, np.array(xk)))


	#### This function returns the minimum value of the function func
	# x: initial guess of the minimum
	# tolerance: termination criteria for algortihm
	# max_iterations: maximum number of iterations as termination criteria for algortihm
	# extra_args: tuple containing matrix A and b to be used if not default A and b used
	def find_minimum( func, x, tolerance, max_iterations, extra_args=() ):
	result = scipy.optimize.minimize(func, x0 = x, args=extra_args, method='CG', tol=tolerance, options={ "maxiter" : max_iterations}, callback=my_callback)
	return result.x