Optimization outcome for the Rosenbrock function.

<div><p>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. The...

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Bibliographic Details
Main Author:	Shikun Chen (14625352) (author)
Other Authors:	Zebin Huang (438392) (author), Wenlong Zheng (14140) (author)
Published:	2025
Subjects:	Medicine Biotechnology Plant Biology Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified reducing computation time random forests excel problem complexity increases predict outputs based output quality according based ensemble algorithms achieved superior performance model &# 8217 method achieves solutions guide slsqp optimization superior prediction accuracy model accuracy prediction tasks prediction precision varying dimensions traditional methods test cases successfully applied rastrigin functions practical solution optimization surrogates optimization efficiency neural networks key transition identifying values fixed parameters extensive testing engineering applications constraint conditions commonly used approach bridges adjustable variables
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Optimization outcome for the Rosenbrock function.

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