plot.py
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	import numpy as np
	import matplotlib.pyplot as plt
	from matplotlib import cm
	from mpl_toolkits.mplot3d import Axes3D


	def plotminimizer(f,points,method):
	"""

	Plot the function f and the successive approximations of the minimizer


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

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

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

	"""

	fig = plt.figure(1)
	ax = fig.gca(projection='3d')

	# Remove grid lines (dotted lines inside plot)
	ax.grid(False)




	# evaluating the functions at the points
	fp = np.zeros(points.shape[0])
	i = 0
	for p in points:
	fp[i] = f(p)
	i = i + 1

	# plotting points and lines for each point pair
	ax.plot(points[:,0], points[:,1], fp, 'ro--', linewidth=2, markersize=5)


	# Plot the surface
	X = np.linspace(-3, 3, 50)
	Y = np.linspace(-3, 3, 50)
	X, Y = np.meshgrid(X, Y)
	Z = np.zeros((X.shape[0], X.shape[1]))
	for i in range(X.shape[0]):
	for j in range(X.shape[1]):
	Z[i, j] = f(np.array([X[i, j], Y[i, j]]))

	ax.plot_surface(X, Y, Z, cmap=cm.coolwarm,linewidth=0, antialiased=False, alpha=0.2)


	# Plot the contour lines
	ax.contour(X, Y, Z, colors="black")


	# Add title and axis names
	plt.title('Convergence of '+method+' method')
	plt.xlabel('x')
	plt.ylabel('y')

	plt.show()