optimizer.py
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	import matplotlib.pyplot as plt
	import numpy as np

	from mpl_toolkits.mplot3d import Axes3D
	from scipy import optimize


	def S(x):
	x_1, x_2 = x
	return 2 * x_1*2 + 1.5 x_2*2 + x_1 x_2 - x_1 - 2 * x_2 + 6


	def getIterationSteps(x):
	x_1, x_2 = x
	iterationPoints.append([x_1, x_2, S(x)])


	def getMinimum(fun, x0, **kwargs):
	return optimize.minimize(S, x0, **kwargs)


	if __name__ == "__main__":
	fig = plt.figure()
	ax = Axes3D(fig)
	X_1, X_2 = np.meshgrid(np.linspace(-3, 3, 100), np.linspace(3, -3, 100))
	f = np.array(
	[S((x_1, x_2)) for x_1, x_2 in zip(np.ravel(X_1), np.ravel(X_2))])
	F = f.reshape(X_1.shape)
	ax.plot_surface(X_1, X_2, F, cmap='viridis')
	x0 = (1, 3)
	iterationPoints = []
	print(getMinimum(S, x0, tol=1e-9, callback=getIterationSteps))
	iterationPoints = np.array(iterationPoints)
	ax.plot(iterationPoints[:, 0], iterationPoints[:, 1],
	iterationPoints[:, 2], 'ro-')
	ax.set_title("Minimisation from scipy")
	ax.set_xlabel("x")
	ax.set_ylabel("y")