MF-SCBO : Multi-fidelity Scalable Constrained Bayesian Optimization
Abstract
Many real-world optimization problems rely on expensive simulations or experiments, making the efficient use of available data essential. Multi-fidelity optimization of high-dimensional black-box functions subject to black-box constraints is increasingly relevant as the cost of objective evaluations continues to rise in applications such as machine learning, engineering, and control. To our knowledge, no existing method simultaneously addresses high-dimensionality, black-box constraints, an arbitrary number of fidelity levels, and non-nested sampling. In this work, we extend the Scalable Constrained Bayesian Optimization method to the multi-fidelity setting, resulting in the MF-SCBO method. The proposed approach is evaluated on standard benchmark functions as well as challenging problems. The experimental results demonstrate that MF-SCBO generally achieves better convergence than both the single-fidelity SCBO and the other multi-fidelity method considered in this high-dimensional and constrained settings.
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BibTeX
@misc{palazzolo2026mfscbo,
title = {{MF-SCBO : Multi-fidelity Scalable Constrained Bayesian Optimization}},
author = {Lucas Palazzolo and Mickaël Binois and Laëtitia Giraldi},
howpublished = {HAL},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.29941},
eprint = {2609.29941},
archivePrefix = {arXiv},
url = {https://hal.science/hal-05763985v1},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - MF-SCBO : Multi-fidelity Scalable Constrained Bayesian Optimization
AU - Lucas Palazzolo
AU - Mickaël Binois
AU - Laëtitia Giraldi
PY - 2026
DA - 2026/09/24
PB - HAL
DO - 10.48550/arxiv.2609.29941
UR - https://hal.science/hal-05763985v1
AB - Many real-world optimization problems rely on expensive simulations or experiments, making the efficient use of available data essential. Multi-fidelity optimization of high-dimensional black-box functions subject to black-box constraints is increasingly relevant as the cost of objective evaluations continues to rise in applications such as machine learning, engineering, and control. To our knowledge, no existing method simultaneously addresses high-dimensionality, black-box constraints, an arbitrary number of fidelity levels, and non-nested sampling. In this work, we extend the Scalable Constrained Bayesian Optimization method to the multi-fidelity setting, resulting in the MF-SCBO method. The proposed approach is evaluated on standard benchmark functions as well as challenging problems. The experimental results demonstrate that MF-SCBO generally achieves better convergence than both the single-fidelity SCBO and the other multi-fidelity method considered in this high-dimensional and constrained settings.
ER -