A Principle for Global Optimization with Gradients

Nils Müller

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arXiv · 2308.09556

A Principle for Global Optimization with Gradients

Nils Müller

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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 search directions from non-local quadratic approximants based on gradients of the objective function is analyzed. Experiments measure the quality of non-local search directions as well as the performance of a proposed simplistic algorithm, of the covariance matrix adaptation evolution strategy (CMA-ES), and of a randomly reinitialized Broyden-Fletcher-Goldfarb-Shanno (BFGS) method.

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Nils Müller. 2023-08-18. A Principle for Global Optimization with Gradients. https://arxiv.org/abs/2308.09556

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