EGORSE couples random and supervised linear embeddings inside Bayesian optimization, with a constraint-based feasible-domain formulation, and shows faster CPU convergence than several high-dimensional BO baselines on benchmark problems up to 600 variables.
An Efficient Application of Bayesian Optimization to an Industrial MDO Framework for Aircraft Design
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High-Dimensional Bayesian Optimization Using Both Random and Supervised Embeddings
EGORSE couples random and supervised linear embeddings inside Bayesian optimization, with a constraint-based feasible-domain formulation, and shows faster CPU convergence than several high-dimensional BO baselines on benchmark problems up to 600 variables.