An adaptive augmented Lagrangian method for large-scale constrained optimization
type: Item
instance of: scholarly article
title: An adaptive augmented Lagrangian method for large-scale constrained optimization
zbMATH Open document ID: 1323.49015
author: Frank E. Curtis
author: Hao Jiang
author: Daniel P. Robinson
published in: Mathematical Programming. Series A. Series B
publication date: 2015-08-31T00:00:00Z
review text: An adaptive augmented Lagrangian algorithm is proposed for large-scale constrained optimization problems by updating the penalty parameters and/or Lagrange multipliers within the trust-region framework. The well-posedness and global convergence of the method are proved. The effectiveness of the adaptive penalty parameter updating strategy is illustrated in terms of the numbers of iterations and gradient evaluations on several test problems.
reviewed by: Bülent Karasözen
Mathematics Subject Classification ID: 49M05
Mathematics Subject Classification ID: 49M29
Mathematics Subject Classification ID: 49M37
Mathematics Subject Classification ID: 65K05
Mathematics Subject Classification ID: 65K10
Mathematics Subject Classification ID: 90C06
Mathematics Subject Classification ID: 90C30
Mathematics Subject Classification ID: 93B40
zbMATH DE Number: 6477212
zbMATH Keywords: nonconvex optimization
zbMATH Keywords: large-scale optimization
zbMATH Keywords: augmented Lagrangian method
zbMATH Keywords: matrix-free methods
zbMATH Keywords: steering methods
describes a project that uses: CUTEr
describes a project that uses: CPLEX
describes a project that uses: COPS
describes a project that uses: RecPF
describes a project that uses: LANCELOT
describes a project that uses: SifDec
describes a project that uses: SNOPT
describes a project that uses: PENNON
MaRDI profile type: Publication
full work available at URL: s10107-014-0784-y
OpenAlex ID: W2028066028
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DOI: 10.1007/S10107-014-0784-Y
description: scientific article; zbMATH DE number 6477212