Notebooks

The repository contains executable studies rather than embedding notebook outputs in the documentation build:

• Search examples: grid search and Bayesian optimization.

• Bayesian optimization design study: acquisition functions and their hyperparameters.

• High-dimensional behavior: Bayesian versus random search from 10 to 1,000 dimensions.

• Performance comparison: comparisons with other optimization libraries and batch sizes.

• RL hyperparameters: tuning Rejax reinforcement-learning runs.

• Bayesian optimization visualization: GP posterior and acquisition behavior.

Install the optional dependencies before running them:

uv pip install -e ".[notebooks]"