A Conjugate Gradient Method with Global Convergence for Large‐Scale Unconstrained Optimization Problems

Shengwei Yao, Xiwen Lu and Zengxin Wei

Journal of Applied Mathematics, 2013, vol. 2013, issue 1

Abstract: The conjugate gradient (CG) method has played a special role in solving large‐scale nonlinear optimization problems due to the simplicity of their very low memory requirements. This paper proposes a conjugate gradient method which is similar to Dai‐Liao conjugate gradient method (Dai and Liao, 2001) but has stronger convergence properties. The given method possesses the sufficient descent condition, and is globally convergent under strong Wolfe‐Powell (SWP) line search for general function. Our numerical results show that the proposed method is very efficient for the test problems.

Date: 2013
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https://doi.org/10.1155/2013/730454

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Persistent link: https://EconPapers.repec.org/RePEc:wly:jnljam:v:2013:y:2013:i:1:n:730454

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