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Can Li
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Global Convergence of A Kind of Conjugate Gradient Method Can Li; Ling Fang; Xianglian Cao
Indonesian Journal of Electrical Engineering and Computer Science Vol 11, No 1: January 2013
Publisher : Institute of Advanced Engineering and Science

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Abstract

The conjugate gradient method is welcome method for solving optimization problems due to its simplicity and low storage. In this paper, we propose a kind of conjugate gradient method. The presented method possesses the sufficient descent property under the strong Wolfe line search. Under mild conditions, we prove that the method with strong Wolfe line search is globally convergent even if the objective function is nonconvex. At the end of this paper, we also present numerical experiment to show the efficiency of the proposed method. DOI: http://dx.doi.org/10.11591/telkomnika.v11i1.1796
A Modified Conjugate Gradient Method for Unconstrained Optimization Can Li
Indonesian Journal of Electrical Engineering and Computer Science Vol 11, No 11: November 2013
Publisher : Institute of Advanced Engineering and Science

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Abstract

Conjugate gradient methods are an important class of methods for solving unconstrained optimization problems, especially for large-scale problems. Recently, they have been much studied. In this paper, we further study the conjugate gradient method for unconstrained optimization. We focus our attention to the descent conjugate gradient method. This paper presents a modified conjugate gradient method. An interesting feature of the presented method is that the direction is always a descent direction for the objective function. Moreover, the property is independent of the line search used. Under mild conditions, we prove that the modified conjugate gradient method with Armijo-type line search is globally convergent. We also present some numerical results to show the efficiency of the proposed method. DOI:  http://dx.doi.org/10.11591/telkomnika.v11i11.2894 
Co-Authors Ling Fang Xianglian Cao