Two extensions of the Dai-Liao method with sufficient descent property based on a penalization scheme
type: Item
instance of: scholarly article
title: Two extensions of the Dai-Liao method with sufficient descent property based on a penalization scheme
zbMATH Open document ID: 1398.65136
author: Masoud Fatemi
author: Saman Babaie-Kafaki
published in: Bulletin of Computational Applied Mathematics
publication date: 2018-08-07T00:00:00Z
full work available at URL: http://drive.google.com/open
review text: Summary: To achieve the good features of the linear conjugate gradient algorithm in a recent extension of the Dai-Liao method, two adaptive choices for parameter of the extended method are proposed based on a penalization approach. It is shown that the suggested parameters guarantee the sufficient descent property independent to the line search and the objective function convexity. Furthermore, they ensure the global convergence of the related algorithm for uniformly convex objective functions. Using a set of unconstrained optimization test problems from the CUTEr library, effectiveness of the suggested choices are numerically compared with two other recently proposed adaptive choices. Results of comparisons show that one of the proposed choices is computationally promising in the sense of the Dolan-Moré performance profile.
Mathematics Subject Classification ID: 65K10
Mathematics Subject Classification ID: 90C30
zbMATH DE Number: 6914254
zbMATH Keywords: unconstrained optimization
zbMATH Keywords: conjugate gradient method
zbMATH Keywords: sufficient descent property
zbMATH Keywords: penalty method
zbMATH Keywords: line search
describes a project that uses: CUTEr
describes a project that uses: SifDec
describes a project that uses: CG_DESCENT
MaRDI profile type: Publication
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description: scientific article; zbMATH DE number 6914254