On the convergence of curvilinear search algorithms in unconstrained optimization
type: Item
instance of: scholarly article
title: On the convergence of curvilinear search algorithms in unconstrained optimization
zbMATH Open document ID: 0568.90081
DOI: 10.1016/0167-6377(85)90048-3
author: Jorge Amaya
published in: Operations Research Letters
publication date: 1985-01-01T00:00:00Z
review text: The purpose of this paper is to unify the conditions under which curvilinear algorithms for unconstrained optimization converge. Particularly, two gradient path approximation algorithms and a trust region curvilinear algorithm are examined in this context.
Mathematics Subject Classification ID: 90C30
Mathematics Subject Classification ID: 49M37
Mathematics Subject Classification ID: 65K05
zbMATH DE Number: 3908180
zbMATH Keywords: curvilinear algorithms
zbMATH Keywords: unconstrained optimization
zbMATH Keywords: gradient path approximation algorithms
zbMATH Keywords: trust region curvilinear algorithm
MaRDI profile type: Publication
full work available at URL: 0167-6377(85)90048-3
OpenAlex ID: W1985169854
cites work: Minimization of functions having Lipschitz continuous first partial derivatives
cites work: A Newton-type curvilinear search method for optimization
cites work: Curvilinear path and trust region in unconstrained optimization: A convergence analysis
cites work: Quasi-Newton Methods, Motivation and Theory
cites work: Unconstrained Optimization by Approximation of the Gradient Path
cites work: A new arc algorithm for unconstrained optimization
description: scientific article; zbMATH DE number 3908180