Original scientific paper

https://doi.org/10.3336/gm.52.2.12

An extended Dai-Liao conjugate gradient method with global convergence for nonconvex functions

Mohammad Reza Arazm ; Department of Mathematics, Faculty of Mathematics, Statistics and Computer Science, Semnan University, P.O. Box: 35195-363, Semnan, Iran
Saman Babaie-Kafaki ; Department of Mathematics, Faculty of Mathematics, Statistics and Computer Science, Semnan University, P.O. Box: 35195-363, Semnan, Iran
Reza Ghanbari ; Faculty of Mathematical Sciences, Ferdowsi University of Mashhad, P.O. Box: 9177948953, Mashhad, Iran

Abstract

Using an extension of some previously proposed modified secant equations in the Dai-Liao approach, a modified nonlinear conjugate gradient method is proposed. As interesting features, the method employs the objective function values in addition to the gradient information and satisfies the sufficient descent property with proper choices for its parameter. Global convergence of the method is established without convexity assumption on the objective function. Results of numerical comparisons are reported. They demonstrate efficiency of the proposed method in the sense of the Dolan-Moré performance profile.

Keywords

Unconstrained optimization; large-scale optimization; conjugate gradient method; sufficient descent property; nonconvexity; global convergence

Hrčak ID:

189339

URI

https://hrcak.srce.hr/189339

Publication date:

13.11.2017.

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