Advanced Search

Query:

Fill in the form and click »Search«...

Format:

Display: entries per page entries

Zbl 1142.65050
Zhang, Li; Zhou, Weijun
Two descent hybrid conjugate gradient methods for optimization. (English)
[J] J. Comput. Appl. Math. 216, No. 1, 251-264 (2008). ISSN 0377-0427

The aim of the paper is to study convergence and computational properties of two new descent hybrid conjugate gradient methods for nonlinear optimization problems consisting in the global minimization of a continuously differentiable function of $n$ variables over $\bbfR^n$. The methods require no restarts and produce a sufficient descent search direction in each iteration. No convexity assumptions are required. The obtained results hold for functions with bounded level sets and bounded Lipschitz continuous gradients. The numerical results presented at the end of the paper show a good efficiency of the proposed methods.
[Karel Zimmermann (Praha)]

MSC 2000:: *65K05 Mathematical programming (numerical methods)
90C30 Nonlinear programming

Keywords: conjugate gradient method; descent direction; global convergence; global optimization; nonlinear optimization; numerical results

Comment on this Item PDF MathML XML ASCII DVI PS BibTeX Online Ordering Article Journal

	Scientific prize winners of the ICM 2010
	Overhang
	Lie groups, physics and geometry. An introduction for physicists, engineers and chemists.

Advanced Search

Zentralblatt MATH Berlin [Germany]