A Modified Spectral Conjugate Gradient Method with Global Convergence

Parvaneh Faramarzi () and Keyvan Amini ()
Additional contact information
Parvaneh Faramarzi: Razi University
Keyvan Amini: Razi University

Journal of Optimization Theory and Applications, 2019, vol. 182, issue 2, No 11, 667-690

Abstract: Abstract In this paper, a modified version of the spectral conjugate gradient algorithm suggested by Jian, Chen, Jiang, Zeng and Yin is proposed. It is proved that the new method is globally convergent for general nonlinear functions, under some standard assumptions. Based on the modified secant condition and quasi-Newton directions, some new spectral parameters are introduced. It is shown that the search direction satisfies the sufficient descent property independent of the line search. Numerical experiments indicate a promising behavior of the new algorithm, especially for large-scale problems.

Keywords: Global convergence; Sufficient descent property; Unconstrained optimization; Spectral conjugate gradient; Modified secant condition; 90C30; 65K05 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://link.springer.com/10.1007/s10957-019-01527-6 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:joptap:v:182:y:2019:i:2:d:10.1007_s10957-019-01527-6

Ordering information: This journal article can be ordered from
http://www.springer. ... cs/journal/10957/PS2

DOI: 10.1007/s10957-019-01527-6

Access Statistics for this article

Journal of Optimization Theory and Applications is currently edited by Franco Giannessi and David G. Hull

More articles in Journal of Optimization Theory and Applications from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().