Work

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

Year: 2021

Type: preprint

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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2111.14098

Authors: Gould, N. I. M., Toint, Ph. L.

Institutions: Rutherford Appleton Laboratory, Science and Technology Facilities Council, University of Namur

Language: English

Topic: Advanced Optimization Algorithms Research

Subfield: Numerical Analysis

Field: Mathematics

Domain: Physical Sciences

Open Access status: green

Funder: Engineering and Physical Sciences Research Council

Award: Least Squares: Fit for the Future

OpenAlex ID: W3214830776

Cites: 9

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