optimizer.py
7024392a5c62master

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)