optimizer.py
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	import scipy.optimize
	import numpy as np

	def optimizer(f, method, x0, tol):
	"""

	Find the minimizer of a scalar function of 2 variables.


	Parameters
	----------
	f : scalar function of a vector of 2 variables.

	method : strig
	e.g. "CG" for Conjigate gradient, "BFGS" for quasi-Newton method of Broyden,
	Fletcher, Goldfarb, and Shanno

	x0 : ndarray
	array of 2 elements with the initial guess of the minimizer.

	tol : float
	exit tollerance for the method e.g. 10^-8


	Returns
	-------
	minimizer : ndarray
	The solution of the optimization

	resultINFO: OptimizeResult object
	Important attributes are:
	x: ndarray - The solution of the optimization.
	success: bool - Whether or not the optimizer exited successfully.
	message: str - Description of the cause of the termination.
	nit: int - Number of iterations performed by the optimizer.

	points : numpy ndarray
	2-D array of shape (number of iterations + 1, 2) containing the successive approximations
	for the solution obtained with the iterations

	"""

	points = [[x0[0], x0[1]]]

	# Define function to store iteration points when calling scipy.optimize.minimize
	def store_iterations(x):
	points.append([x[0], x[1]])

	# Compute optimizer and store iterations by using callback
	resultINFO = scipy.optimize.minimize(f, x0,
	method=method,
	callback=store_iterations,
	options={'gtol': tol})
	minimizer = resultINFO.x
	return minimizer, resultINFO, np.asarray(points)