An adaptive regularization algorithm for unconstrained optimization with inexact function and derivatives values
Optimization and Control
2021-11-30 v1
Abstract
An adaptive regularization algorithm for unconstrained nonconvex optimization is proposed that is capable of handling inexact objective-function and derivative values, and also of providing approximate minimizer of arbitrary order. In comparison with a similar algorithm proposed in Cartis, Gould, Toint (2021), its distinguishing feature is that it is based on controlling the relative error between the model and objective values. A sharp evaluation complexity complexity bound is derived for the new algorithm.
Cite
@article{arxiv.2111.14098,
title = {An adaptive regularization algorithm for unconstrained optimization with inexact function and derivatives values},
author = {N. I. M. Gould and Ph. L. Toint},
journal= {arXiv preprint arXiv:2111.14098},
year = {2021}
}