Noise-free optimization with Expected Improvement

import numpy as np
import tensorflow as tf

np.random.seed(1793)
tf.random.set_seed(1793)

Describe the problem

In this example, we look to find the minimum value of the two-dimensional Branin function over the hypercube \([0, 1]^2\). The Branin function is a popular toy function used in Bayesian optimization literature. Trieste provides a selection of toy functions in trieste.objectives package, where besides the functions we also provide their minimizers, minima and search space definitions.

Below we use a version of the Branin function scaled to the hypercube search space. For the Branin we use the predefined search space, but otherwise one would define the search space directly using a Box object (illustrated below as well). We also plot contours of the Branin over the search space.

from trieste.objectives import ScaledBranin
from trieste.experimental.plotting import plot_function_plotly
from trieste.space import Box

scaled_branin = ScaledBranin.objective
search_space = ScaledBranin.search_space  # predefined search space
search_space = Box([0, 0], [1, 1])  # define the search space directly

fig = plot_function_plotly(
    scaled_branin,
    search_space.lower,
    search_space.upper,
)
fig.show()