2.7.4.1. Noisy optimization problem

Draws a figure explaining noisy vs non-noisy optimization

import numpy as np
import matplotlib.pyplot as plt

rng = np.random.default_rng(27446968)

x = np.linspace(-5, 5, 101)
x_ = np.linspace(-5, 5, 31)


def f(x):
    return -np.exp(-(x**2))


# A smooth function
plt.figure(1, figsize=(3, 2.5))
plt.clf()

plt.plot(x_, f(x_) + 0.2 * np.random.normal(size=31), linewidth=2)
plt.plot(x, f(x), linewidth=2)

plt.ylim(ymin=-1.3)
plt.axis("off")
plt.tight_layout()
plt.show()