Learning Low-Dimensional Embeddings for Black-Box Optimization
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866916984013193216 |
|---|---|
| author | Busetto, Riccardo Mejari, Manas Forgione, Marco Bemporad, Alberto Piga, Dario |
| author_facet | Busetto, Riccardo Mejari, Manas Forgione, Marco Bemporad, Alberto Piga, Dario |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_01112 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learning Low-Dimensional Embeddings for Black-Box Optimization Busetto, Riccardo Mejari, Manas Forgione, Marco Bemporad, Alberto Piga, Dario Systems and Control Machine Learning 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. |
| title | Learning Low-Dimensional Embeddings for Black-Box Optimization |
| topic | Systems and Control Machine Learning |
| url | https://arxiv.org/abs/2505.01112 |