A scaled conjugate gradient method for solving monotone nonlinear equations with convex constraints
type: Item
instance of: scholarly article
title: A scaled conjugate gradient method for solving monotone nonlinear equations with convex constraints
zbMATH Open document ID: 1397.90365
DOI: 10.1155/2013/286486
author: Sheng Wang
author: Hongbo Guan
published in: Journal of Applied Mathematics
publication date: 2018-10-10T00:00:00Z
review text: Summary: Based on the Scaled conjugate gradient (SCALCG) method presented by \textit{N. Andrei} [Appl. Math. Lett. 20, No. 6, 645--650 (2007; Zbl 1116.90114)] and the projection method presented by Solodov and Svaiter, we propose a SCALCG method for solving monotone nonlinear equations with convex constraints. SCALCG method can be regarded as a combination of conjugate gradient method and Newton-type method for solving unconstrained optimization problems. So, it has the advantages of the both methods. It is suitable for solving large-scale problems. So, it can be applied to solving large-scale monotone nonlinear equations with convex constraints. Under reasonable conditions, we prove its global convergence. We also do some numerical experiments show that the proposed method is efficient and promising.
Mathematics Subject Classification ID: 90C30
Mathematics Subject Classification ID: 90C53
Mathematics Subject Classification ID: 65K05
Mathematics Subject Classification ID: 65H10
zbMATH DE Number: 6950598
Wikidata QID: Q59002598
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2013/286486
OpenAlex ID: W2076248199
DBLP publication ID: journals/jam/WangG13a
description: scientific article; zbMATH DE number 6950598