Abstract This package implements a modified Constrained Adversarially Robust Bayesian Optimization (CARBO) sampler based on the paper Constrained robust Bayesian optimization of expensive noisyblack-box functions with guaranteed regret bounds. This sampler robustly optimizes a function along with inequality constraints that incurs a noise in its input. The algorithm details are described in the Others section.
APIs CARBOSampler(*, seed: int | None = None, independent_sampler: BaseSampler | None = None, n_startup_trials: int = 10, deterministic_objective: bool = False, constraints_func: Callable[[FrozenTrial], Sequence[float]] | None = None, rho: float = 1e3, beta: float = 4.
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.