An adaptive regularization algorithm for unconstrained optimization with inexact function and derivatives values

N. I. M. Gould; Ph. L. Toint

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arXiv · 2111.14098

An adaptive regularization algorithm for unconstrained optimization with inexact function and derivatives values

N. I. M. Gould·Ph. L. Toint

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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.

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BibTeX RIS

N. I. M. Gould, Ph. L. Toint. 2021-11-28. An adaptive regularization algorithm for unconstrained optimization with inexact function and derivatives values. https://arxiv.org/abs/2111.14098

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