TREGO: a Trust-Region Framework for Efficient Global Optimization
Abstract
Efficient Global Optimization (EGO) is the canonical form of Bayesian optimization that has been successfully applied to solve global optimization of expensive-to-evaluate black-box problems. However, EGO struggles to scale with dimension, and offers limited theoretical guarantees. In this work, a trust-region framework for EGO (TREGO) is proposed and analyzed. TREGO alternates between regular EGO steps and local steps within a trust region. By following a classical scheme for the trust region (based on a sufficient decrease condition), the proposed algorithm enjoys global convergence properties, while departing from EGO only for a subset of optimization steps. Using extensive numerical experiments based on the well-known COCO {bound constrained problems}, we first analyze the sensitivity of TREGO to its own parameters, then show that the resulting algorithm is consistently outperforming EGO and getting competitive with other state-of-the-art black-box optimization methods.
Cite
@article{arxiv.2101.06808,
title = {TREGO: a Trust-Region Framework for Efficient Global Optimization},
author = {Youssef Diouane and Victor Picheny and Rodolphe Le Riche and Alexandre Scotto Di Perrotolo},
journal= {arXiv preprint arXiv:2101.06808},
year = {2022}
}