Optimization of non-smooth functions via differentiable surrogates.
Mathematical optimization is fundamental across many scientific and engineering applications. While data-driven models like gradient boosting and random forests excel at prediction tasks, they often lack mathematical regularity, being non-differentiable or even discontinuous. These models are common...
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| Główni autorzy: | , , |
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| Format: | Artigo |
| Język: | Inglês |
| Wydane: |
Public Library of Science (PLoS)
2025-01-01
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| Seria: | PLoS ONE |
| Dostęp online: | https://doi.org/10.1371/journal.pone.0321862 |
| Etykiety: |
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