Convergence property of a class of unconstrained minimization methods with perturbations

Author

Li, Meixia ; Che, Haitao

Author_Institution

Sch. of Math. & Inf. Sci., Weifang Univ., Weifang, China

Volume

2

fYear

2009

fDate

8-9 Aug. 2009

Firstpage

85

Lastpage

88

Abstract

In this paper, we investigate a class of unconstrained minimization methods including Fletcher-Reeves (abbr. FR) conjugate gradient method with perturbations. Their stepsizes are determined by generalized Wolfe line search. We prove that these methods are globally convergent under mild conditions, and in doing so, we remove various boundedness conditions such as boundedness from blow of f, boundedness of level set, etc. At the end of this paper, numerical examples are given.

Keywords

conjugate gradient methods; convergence; minimisation; Fletcher-Reeves conjugate gradient method; convergence property; generalized Wolfe line search; perturbations; unconstrained minimization methods; Algorithm design and analysis; Communication system control; Convergence; Gradient methods; Information science; Level set; Mathematics; Minimization methods; Neural networks; Optimization methods; global convergence; perturbation; unconstrained optimization method;

fLanguage

English

Publisher

ieee

Conference_Titel

Computing, Communication, Control, and Management, 2009. CCCM 2009. ISECS International Colloquium on

Conference_Location

Sanya

Print_ISBN

978-1-4244-4247-8

Type

conf

DOI

10.1109/CCCM.2009.5267980

Filename

5267980