A Fast Gradient and Function Sampling Method for Finite Max-Functions
Optimization and Control
2019-04-03 v2
Abstract
This paper tackles the unconstrained minimization of a class of nonsmooth and nonconvex functions that can be written as finite max-functions. A gradient and function-based sampling method is proposed which, under special circumstances, either moves superlinearly to a minimizer of the problem of interest or superlinearly improves the optimality certificate. Global and local convergence analysis are presented, as well as illustrative examples that corroborate and elucidate the obtained theoretical results.
Cite
@article{arxiv.1708.07473,
title = {A Fast Gradient and Function Sampling Method for Finite Max-Functions},
author = {Elias S. Helou and Sandra A. Santos and Lucas E. A. Simões},
journal= {arXiv preprint arXiv:1708.07473},
year = {2019}
}