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

Author

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  • Shengwei Yao
  • Xiwen Lu
  • Zengxin Wei

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.

Suggested Citation

  • Shengwei Yao & Xiwen Lu & Zengxin Wei, 2013. "A Conjugate Gradient Method with Global Convergence for Large-Scale Unconstrained Optimization Problems," Journal of Applied Mathematics, Hindawi, vol. 2013, pages 1-9, November.
    • Handle: RePEc:hin:jnljam:730454
    DOI: 10.1155/2013/730454
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