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Article Dans Une Revue Journal of Global Optimization Année : 2022

TREGO: a Trust-Region Framework for Efficient Global Optimization

Résumé

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, we propose and analyze a trust-region-like EGO method (TREGO). 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), we demonstrate that our algorithm enjoys strong global convergence properties, while departing from EGO only for a subset of optimization steps. Using extensive numerical experiments based on the well-known COCO benchmark, 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 global optimization methods. The method is available both in the R package DiceOptim 1 and python library trieste 2 .
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Dates et versions

hal-03450072 , version 1 (25-11-2021)
hal-03450072 , version 2 (11-10-2022)

Identifiants

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Youssef Diouane, Victor Picheny, Rodolphe Le Riche, Alexandre Scotto Di Perrotolo. TREGO: a Trust-Region Framework for Efficient Global Optimization. Journal of Global Optimization, 2022, ⟨10.1007/s10898-022-01245-w⟩. ⟨hal-03450072v2⟩
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