A New Hybrid Three-Term Conjugate Gradient Algorithm for Large-Scale Unconstrained Problems

Three-term conjugate gradient methods have attracted much attention for large-scale unconstrained problems in recent years, since they have attractive practical factors such as simple computation, low memory requirement, better descent property and strong global convergence property. In this paper,...

Full description

Saved in:
Bibliographic Details
Published in:Mathematics (Basel) 2021-06, Vol.9 (12), p.1353
Main Authors: Tian, Qi, Wang, Xiaoliang, Pang, Liping, Zhang, Mingkun, Meng, Fanyun
Format: Article
Language:English
Subjects:
acceleration strategy
Algorithms
Approximation
conjugate condition
Conjugates
Convergence
Convex analysis
Convexity
global convergence
Mathematical analysis
Methods
Motivation
secant equation
sufficient descent property
three-term conjugate gradient method
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Three-term conjugate gradient methods have attracted much attention for large-scale unconstrained problems in recent years, since they have attractive practical factors such as simple computation, low memory requirement, better descent property and strong global convergence property. In this paper, a hybrid three-term conjugate gradient algorithm is proposed and it owns a sufficient descent property, independent of any line search technique. Under some mild conditions, the proposed method is globally convergent for uniformly convex objective functions. Meanwhile, by using the modified secant equation, the proposed method is also global convergence without convexity assumption on the objective function. Numerical results also indicate that the proposed algorithm is more efficient and reliable than the other methods for the testing problems.
ISSN:2227-7390
2227-7390
DOI:10.3390/math9121353