A clustering heuristic to improve a derivative-free algorithm for nonsmooth optimization
Optimization and Control
2023-02-13 v1
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.
Cite
@article{arxiv.2302.05278,
title = {A clustering heuristic to improve a derivative-free algorithm for nonsmooth optimization},
author = {Manlio Gaudioso and Giampaolo Liuzzi and Stefano Lucidi},
journal= {arXiv preprint arXiv:2302.05278},
year = {2023}
}