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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Bibliographic Details
Published in:	PloS one Vol. 20; no. 5; p. e0321862
Main Authors:	Chen, Shikun, Huang, Zebin, Zheng, Wenlong
Format:	Journal Article
Language:	English
Published:	United States Public Library of Science 30.05.2025 Public Library of Science (PLoS)
Subjects:	Accuracy Algorithms Analysis Approximation Benchmarks Constraints Cost control Efficiency Energy consumption Genetic algorithms Heuristic Machine learning Mathematical analysis Mathematical functions Mathematical models Mathematical optimization Medical informatics Methods Models, Theoretical Neural networks Neural Networks, Computer Optimization Parameters Predictions Simulation Variables China
ISSN:	1932-6203, 1932-6203
Online Access:	Get full text
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