Two New Conjugate Gradient Methods for Unconstrained Optimization
Joint Authors
Liu, Meixing
Ma, Guodong
Yin, Jianghua
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-04-22
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
The conjugate gradient method is very effective in solving large-scale unconstrained optimal problems.
In this paper, on the basis of the conjugate parameter of the conjugate descent (CD) method and the second inequality in the strong Wolfe line search, two new conjugate parameters are devised.
Using the strong Wolfe line search to obtain the step lengths, two modified conjugate gradient methods are proposed for general unconstrained optimization.
Under the standard assumptions, the two presented methods are proved to be sufficient descent and globally convergent.
Finally, preliminary numerical results are reported to show that the proposed methods are promising.
American Psychological Association (APA)
Liu, Meixing& Ma, Guodong& Yin, Jianghua. 2020. Two New Conjugate Gradient Methods for Unconstrained Optimization. Complexity،Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1145791
Modern Language Association (MLA)
Liu, Meixing…[et al.]. Two New Conjugate Gradient Methods for Unconstrained Optimization. Complexity No. 2020 (2020), pp.1-13.
https://search.emarefa.net/detail/BIM-1145791
American Medical Association (AMA)
Liu, Meixing& Ma, Guodong& Yin, Jianghua. Two New Conjugate Gradient Methods for Unconstrained Optimization. Complexity. 2020. Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1145791
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1145791