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Global Convergence of a Spectral Conjugate Gradient Method for Unconstrained Optimization

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  • Jinkui Liu
  • Youyi Jiang

Abstract

A new nonlinear spectral conjugate descent method for solving unconstrained optimization problems is proposed on the basis of the CD method and the spectral conjugate gradient method. For any line search, the new method satisfies the sufficient descent condition . Moreover, we prove that the new method is globally convergent under the strong Wolfe line search. The numerical results show that the new method is more effective for the given test problems from the CUTE test problem library (Bongartz et al., 1995) in contrast to the famous CD method, FR method, and PRP method.

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  • Jinkui Liu & Youyi Jiang, 2012. "Global Convergence of a Spectral Conjugate Gradient Method for Unconstrained Optimization," Abstract and Applied Analysis, Hindawi, vol. 2012, pages 1-12, July.
    • Handle: RePEc:hin:jnlaaa:758287
    DOI: 10.1155/2012/758287
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    Cited by:

    1. Siti Farhana Husin & Mustafa Mamat & Mohd Asrul Hery Ibrahim & Mohd Rivaie, 2020. "An Efficient Three-Term Iterative Method for Estimating Linear Approximation Models in Regression Analysis," Mathematics, MDPI, vol. 8(6), pages 1-12, June.

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