TREGO: a trust-region framework for efficient global optimization

Youssef Diouane (), Victor Picheny (), Rodolophe Le Riche () and Alexandre Scotto Di Perrotolo ()
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Youssef Diouane: Polytechnique Montréal
Victor Picheny: Secondmind
Rodolophe Le Riche: CNRS LIMOS, Mines St-Etienne and UCA
Alexandre Scotto Di Perrotolo: Université de Toulouse

Journal of Global Optimization, 2023, vol. 86, issue 1, No 1, 23 pages

Abstract: 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.

Keywords: Non-linear optimization; Black-box optimization; Gaussian processes; Bayesian optimization; Trust-region (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10898-022-01245-w

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