When gradient-based methods are impractical, black-box optimization (BBO) provides a valuable alternative. However, BBO often struggles with high-dimensional problems and limited trial budgets. In this work, we propose a novel approach based on meta-learning to pre-compute a reduced-dimensional manifold where optimal points lie for a specific class of optimization problems. When optimizing a new problem instance sampled from the class, black-box optimization is carried out in the reduced-dimensional space, effectively reducing the effort required for finding near-optimal solutions.
View on arXiv@article{busetto2025_2505.01112,
title={ Learning Low-Dimensional Embeddings for Black-Box Optimization },
author={ Riccardo Busetto and Manas Mejari and Marco Forgione and Alberto Bemporad and Dario Piga },
journal={arXiv preprint arXiv:2505.01112},
year={ 2025 }
}