Exploiting Separability in Multi-Scale Grey-Box Bayesian Optimization

Abstract:We consider grey-box optimization problems where the decision variables naturally partition into black-box variables (as arguments to an expensive black-box function) and white-box variables, governed by a set of explicit, closed-form equations that also depend on the output of the black-box function. We exploit this separability through a bilevel reformulation: an outer Bayesian optimization (BO) to optimize the scalar objective as a function of black-box variables alone, while an inner problem solves the white-box subproblem via global optimization. The Gaussian process surrogate used in BO is therefore defined rather than and white-box constraints are satisfied exactly whenever the inner optimizer converges to a feasible point---without penalty functions, chance constraints, or moment approximations. On a suite of 13 benchmark problems, bilevel BO achieves lower regret, with fewer iterations and wall clock time. This advantage is robust to initialization set size, exploration parameters, and inner-solver choice.

Machine Learning (cs.LG)

arXiv:2608.03045 [cs.LG]

(or arXiv:2608.03045v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2608.03045 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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