Module optimizer 

Bayesian Optimizer – the main driver for Bayesian optimization.

Orchestrates the GP surrogate, acquisition function, and sampling strategy into a full sequential/batch optimization loop.

Features

• Configurable surrogate (GP with any kernel)
• Pluggable acquisition functions (EI, PI, UCB, KG, Thompson, batch variants)
• Initial design via Latin Hypercube, Sobol, Halton, or random sampling
• Sequential and batch optimization loops
• Multi-objective Bayesian optimization via ParEGO scalarization
• Constraint handling via augmented acquisition
• Warm-starting from previous evaluations

Structs

BayesianOptResult

Result of Bayesian optimization.

BayesianOptimizer

The Bayesian optimizer.

BayesianOptimizerConfig

Configuration for the Bayesian optimizer.

Constraint

A constraint for constrained Bayesian optimization.

Observation

A single evaluated observation.

Functions

optimize

Run Bayesian optimization on a function.