Trends in Consensus-based optimization
Abstract
In this chapter we give an overview of the consensus-based global optimization algorithm and its recent variants. We recall the formulation and analytical results of the original model, then we discuss variants using component-wise independent or common noise. In combination with mini-batch approaches those variants were tailored for machine learning applications. Moreover, it turns out that the analytical estimates are dimension independent, which is useful for high-dimensional problems. We discuss the relationship of consensus-based optimization with particle swarm optimization, a method widely used in the engineering community. Then we survey a variant of consensus-based optimization that is proposed for global optimization problems constrained to hyper-surfaces. We conclude the chapter with remarks on applications, preprints and open problems.
Cite
@article{arxiv.2104.01383,
title = {Trends in Consensus-based optimization},
author = {Claudia Totzeck},
journal= {arXiv preprint arXiv:2104.01383},
year = {2021}
}