A Principle for Global Optimization with Gradients

Nils Müller ()
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Nils Müller: Max Planck Institute for Software Systems

Journal of Optimization Theory and Applications, 2026, vol. 208, issue 1, No 25, 23 pages

Abstract: Abstract 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.

Keywords: Global Optimization; Robust Optimization; Continuous Optimization; Mathematical Optimization; Simulation-Based Optimization; 90C26; 90C15; 90C53 (search for similar items in EconPapers)
Date: 2026
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DOI: 10.1007/s10957-025-02848-5

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