A sequential adaptive regularisation using cubics algorithm for solving nonlinear equality constrained optimization

The adaptive regularisation algorithm using cubics (ARC) is initially proposed for unconstrained optimization. ARC has excellent convergence properties and complexity. In this paper, we extend ARC to solve nonlinear equality constrained optimization and propose a sequential adaptive regularisation u...

Full description

Saved in:

Bibliographic Details
Published in:	Computational optimization and applications Vol. 84; no. 3; pp. 1005 - 1033
Main Authors:	Pei, Yonggang, Song, Shaofang, Zhu, Detong
Format:	Journal Article
Language:	English
Published:	New York Springer US 01.04.2023 Springer Nature B.V
Subjects:	Adaptive algorithms Algorithms Convergence Convex and Discrete Geometry Iterative methods Management Science Mathematics Mathematics and Statistics Operations Research Operations Research/Decision Theory Optimization Penalty function Quadratic programming Reduction Regularization Statistics Global convergence 65K05 90C30 Adaptive regularization with cubics Nonlinear optimization 65K10 49M37 Constrained optimization
ISSN:	0926-6003, 1573-2894
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!