# Trieste documentation#

Trieste is a research toolbox built on TensorFlow, dedicated to Bayesian optimization, the process of finding the optimal values of an expensive, black-box objective function by employing probabilistic models over observations.

Without loss of generality, Trieste only supports minimizing the objective function. In the simplest case of an objective function with one-dimensional real output $$f: X \to \mathbb R$$, this is

$\mathop{\mathrm{argmin}}_{x \in X} f(x) \qquad .$

When the objective function has higher-dimensional output, we can still talk of finding the minima, though the optimal values will form a Pareto set rather than a single point. Trieste provides functionality for optimization of single-valued objective functions, and supports extension to the higher-dimensional case. It also supports optimization over constrained spaces, learning the constraints alongside the objective.

Trieste (pronounced tree-est) is named after the bathyscaphe Trieste, the first vehicle to take a crew to Challenger Deep in the Mariana Trench, the lowest point on the Earth’s surface: the literal global minimum.

## Installation#

To install Trieste, run

\$ pip install trieste


The library supports Python 3.7 onwards, and uses semantic versioning.

## Getting help#

• We welcome contributions. To submit a pull request, file a bug report, or make a feature request, see the contribution guidelines.

• For more open-ended questions, or for anything else, join the discussions on Trieste channels in Secondmind Labs’ community Slack workspace.