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<p>This work demonstrates the utility of gradients for the global optimization of certain differentiable functions with many suboptimal local minima. To this end, a principle for generating non-local quadratic approximants, and the associated search directions, from gradient information of multimodal objective functions is analyzed. Experiments measure the quality of non-local search directions as well as the performance of the principle embedded into a simplistic algorithm, of the covariance matrix adaptation evolution strategy (CMA-ES), and of a randomly reinitialized Broyden-Fletcher-Goldfarb-Shanno (BFGS) method.</p>

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A Principle for Global Optimization with Gradients

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月1日

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  • Nils Müller

摘要

This work demonstrates the utility of gradients for the global optimization of certain differentiable functions with many suboptimal local minima. To this end, a principle for generating non-local quadratic approximants, and the associated search directions, from gradient information of multimodal objective functions is analyzed. Experiments measure the quality of non-local search directions as well as the performance of the principle embedded into a simplistic algorithm, of the covariance matrix adaptation evolution strategy (CMA-ES), and of a randomly reinitialized Broyden-Fletcher-Goldfarb-Shanno (BFGS) method.