Two descent hybrid conjugate gradient methods for optimization

Authors:
Li Zhang;Weijun Zhou
Affiliations:
College of Mathematics and Computational Science, Changsha University of Science and Technology, Changsha 410076, China;College of Mathematics and Computational Science, Changsha University of Science and Technology, Changsha 410076, China
Venue:
Journal of Computational and Applied Mathematics
Year:
2008

Citing 8
Cited 0

Practical methods of optimization; (2nd ed.)

Practical methods of optimization; (2nd ed.)
Efficient hybrid conjugate gradient techniques

Journal of Optimization Theory and Applications
Efficient generalized conjugate gradient algorithms, Part 1: theory

Journal of Optimization Theory and Applications
CUTE: constrained and unconstrained testing environment

ACM Transactions on Mathematical Software (TOMS)
A Nonlinear Conjugate Gradient Method with a Strong Global Convergence Property

SIAM Journal on Optimization
A New Conjugate Gradient Method with Guaranteed Descent and an Efficient Line Search

SIAM Journal on Optimization
Global convergence of a modified Fletcher–Reeves conjugate gradient method with Armijo-type line search

Numerische Mathematik
Scaled conjugate gradient algorithms for unconstrained optimization

Computational Optimization and Applications

Quantified Score

Hi-index	7.29

Visualization

Abstract

In this paper, we propose two new hybrid nonlinear conjugate gradient methods, which produce sufficient descent search direction at every iteration. This property depends neither on the line search used nor on the convexity of the objective function. Under suitable conditions, we prove that the proposed methods converge globally for general nonconvex functions. The numerical results show that both hybrid methods are efficient for the given test problems from the CUTE library.