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Nonlinear Conjugate Gradient Methods with Sufficient Descent Condition for Large-Scale Unconstrained Optimization

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  • Jianguo Zhang
  • Yunhai Xiao
  • Zengxin Wei

Abstract

Two nonlinear conjugate gradient-type methods for solving unconstrained optimization problems are proposed. An attractive property of the methods, is that, without any line search, the generated directions always descend. Under some mild conditions, global convergence results for both methods are established. Preliminary numerical results show that these proposed methods are promising, and competitive with the well-known PRP method.

Suggested Citation

  • Jianguo Zhang & Yunhai Xiao & Zengxin Wei, 2009. "Nonlinear Conjugate Gradient Methods with Sufficient Descent Condition for Large-Scale Unconstrained Optimization," Mathematical Problems in Engineering, Hindawi, vol. 2009, pages 1-16, July.
    • Handle: RePEc:hin:jnlmpe:243290
    DOI: 10.1155/2009/243290
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    Cited by:

    1. Zhi Yang & Xiaochuan Luo & Pengbo Liu & Jinwei Qiao & Ming Liu, 2022. "Estimation of the Total Heat Exchange Factor for the Reheating Furnace Based on the First-Optimize-Then-Discretize Approach and an Improved Hybrid Conjugate Gradient Algorithm," Mathematics, MDPI, vol. 10(21), pages 1-20, November.

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