A Conjugate Gradient Method with Global Convergence for Large-Scale Unconstrained Optimization Problems | Zendy

Shengwei Yao | Zendy; Xiwen Lu | Zendy; Zengxin Wei | Zendy

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

Author(s) -

Shengwei Yao,

Xiwen Lu,

Zengxin Wei

Publication year - 2013

Publication title -

journal of applied mathematics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.307

H-Index - 43

eISSN - 1687-0042

pISSN - 1110-757X

DOI - 10.1155/2013/730454

Subject(s) - conjugate gradient method , nonlinear conjugate gradient method , conjugate residual method , derivation of the conjugate gradient method , gradient descent , convergence (economics) , gradient method , line search , mathematics , scale (ratio) , mathematical optimization , conjugate , descent (aeronautics) , biconjugate gradient method , computer science , mathematical analysis , artificial neural network , artificial intelligence , physics , radius , computer security , quantum mechanics , meteorology , economics , economic growth

The conjugate gradient (CG) method has played a special role in solving large-scale nonlinearoptimization problems due to the simplicity of their very low memory requirements. This paperproposes 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. Ournumerical results show that the proposed method is very efficient for the test problems

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