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arXiv · 2302.05278

A clustering heuristic to improve a derivative-free algorithm for nonsmooth optimization

Manlio Gaudioso·Giampaolo Liuzzi·Stefano Lucidi
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Abstract

In this paper we propose an heuristic to improve the performances of the recently proposed derivative-free method for nonsmooth optimization CS-DFN. The heuristic is based on a clustering-type technique to compute a direction { which relies on an estimate of Clarke's generalized gradient} of the objective function. As such, this direction (as it is shown by the numerical experiments) is a good descent direction for the objective function. We report some numerical results and comparison with the original CS-DFN method to show the utility of the proposed improvement on a set of well-known test problems.

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Manlio Gaudioso, Giampaolo Liuzzi, Stefano Lucidi. 2023-02-10. A clustering heuristic to improve a derivative-free algorithm for nonsmooth optimization. https://arxiv.org/abs/2302.05278

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