A Principle for Global Optimization with Gradients

• URL: http://arxiv.org/abs/2308.09556v1
• Date: Fri, 18 Aug 2023 13:39:29 GMT
• Title: A Principle for Global Optimization with Gradients
• Authors: Nils M\"uller
• Abstract summary: This work demonstrates the utility of gradients for the global optimization of certain differentiable functions with many suboptimal local minima. Experiments measure the quality of non-local search directions as well as the performance of a proposed simplistic algorithm.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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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