STREGO: A Scalable Implementation of a Trust-Region Framework for Noisy Bayesian Optimization
TREGO, a trust-region Bayesian optimization (BO) method for expensive blackbox problems, has convergence guarantees, but only for exact function values. In high dimension, its performance tends to deteriorate. This paper asks whether a trust-region BO method can be extended to handle noisy evaluations while being competitive in high dimension. We first generalize TREGO into a trust-region framework for noisy BO and extend its convergence analysis to noisy function estimates that are sufficiently accurate with sufficiently high probability. We then propose STREGO, a scalable implementation of this framework. Its global phase selects a batch of candidates by solving a bi-objective problem o...
| Authors | Youssef Diouane, Issmail El Hallaoui, Meisseme Kadri et al. |
|---|---|
| Primary category | math.OC |
| Published | 9 October 2026 |
| Last updated at source | 9 October 2026 |
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