TREGO: a trust-region framework for efficient global optimization
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 t...
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| Published in | Journal of global optimization Vol. 86; no. 1; pp. 1 - 23 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Springer US
01.05.2023
Springer Springer Nature B.V Springer Verlag |
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| Online Access | Get full text |
| ISSN | 0925-5001 1573-2916 1573-2916 |
| DOI | 10.1007/s10898-022-01245-w |
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| 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. |
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| AbstractList | 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. 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 . |
| Audience | Academic |
| Author | Perrotolo, Alexandre Scotto Di Diouane, Youssef Riche, Rodolophe Le Picheny, Victor |
| Author_xml | – sequence: 1 givenname: Youssef orcidid: 0000-0002-6609-7330 surname: Diouane fullname: Diouane, Youssef email: youssef.diouane@polymtl.ca organization: Department of Mathematics and Industrial Engineering, Polytechnique Montréal – sequence: 2 givenname: Victor surname: Picheny fullname: Picheny, Victor organization: Secondmind – sequence: 3 givenname: Rodolophe Le orcidid: 0000-0002-3518-2110 surname: Riche fullname: Riche, Rodolophe Le organization: CNRS LIMOS, Mines St-Etienne and UCA – sequence: 4 givenname: Alexandre Scotto Di surname: Perrotolo fullname: Perrotolo, Alexandre Scotto Di organization: ISAE-SUPAERO, Université de Toulouse |
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| Keywords | Trust-region Bayesian optimization Non-linear optimization Black-box optimization Gaussian processes trust-region nonlinear optimization |
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| Title | TREGO: a trust-region framework for efficient global optimization |
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