Abstract This sampler optimizes the objective function given input perturbations. For example, most industrial productions have their own tolerance, and any differences within this tolerance are considered acceptable. For this reason, even if we transfer the optimized result of a design simulation to its production, it is mostly impossible to reproduce the precise simulated design setup. This necessitates accounting for noisy input so that the deployed setup remains sufficiently performant even in the presence of arbitrary noise.
Abstract TuRBOSampler implements Bayesian optimization with trust regions. It places local trust regions around the current best solutions and fits Gaussian Process (GP) models within those regions. Operating within adaptive local regions reduces high-dimensional sample complexity, yielding accurate fits with fewer trials.
Please refer to the paper, Scalable Global Optimization via Local Bayesian Optimization for more information.
APIs TuRBOSampler(*, n_startup_trials: int = 4, n_trust_region: int = 5, success_tolerance: int = 3, failure_tolerance: int = 5, seed: int | None = None, independent_sampler: BaseSampler | None = None, deterministic_objective: bool = False, warn_independent_sampling: bool = True) n_startup_trials: Number of initial trials PER TRUST REGION.