# TREGO: a Trust-Region Framework for Efficient Global Optimization

Source: [TREGO: a Trust-Region Framework for Efficient Global Optimization](<https://www.sota2.com/research/paper/68fa59301e209131ce9b4df7>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T15:46:18.067Z

Authors: Youssef Diouane, Victor Picheny, Rodolphe Le Riche, Alexandre Scotto Di Perrotolo

Published: 2021-01-18T00:14:40.000Z

## 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.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2101.06808>)

[PDF](<https://arxiv.org/pdf/2101.06808.pdf>)