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...
Gorde:
| Egile Nagusiak: | , , |
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| Formatua: | Artigo |
| Hizkuntza: | Inglês |
| Argitaratua: |
Public Library of Science (PLoS)
2025-01-01
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| Saila: | PLoS ONE |
| Sarrera elektronikoa: | https://doi.org/10.1371/journal.pone.0321862 |
| Etiketak: |
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