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

arXiv: https://arxiv.org/abs/2111.14098 · HTML: https://mlsanity.com/paper/2111.14098/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Nov 2021.

## Built on by (most cited first, 1 of 1)

- [A Trust-Region Algorithm for Noisy Equality Constrained Optimization](https://mlsanity.com/paper/2411.02665/) (2024)

## Abstract (arXiv)

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.