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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| Principais autores: | , , |
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| 格式: | Artigo |
| 語言: | Inglês |
| 出版: |
Public Library of Science (PLoS)
2025-01-01
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| 叢編: | PLoS ONE |
| 在線閱讀: | https://doi.org/10.1371/journal.pone.0321862 |
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