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A new descent memory gradient method and its global convergence

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Abstract

In this article, a new descent memory gradient method without restarts is proposed for solving large scale unconstrained optimization problems. The method has the following attractive properties: 1) The search direction is always a sufficiently descent direction at every iteration without the line search used; 2) The search direction always satisfies the angle property, which is independent of the convexity of the objective function. Under mild conditions, the authors prove that the proposed method has global convergence, and its convergence rate is also investigated. The numerical results show that the new descent memory method is efficient for the given test problems.

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Authors and Affiliations

  1. Department of Mathematics and Information Science, Zaozhuang University, Zaozhuang, 277160, China

    Min Sun

  2. School of Management, Qufu Normal University, Rizhao, 276826, China

    Qingguo Bai

Authors
  1. Min Sun
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  2. Qingguo Bai
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Corresponding author

Correspondence to Min Sun.

Additional information

This research is supported by the National Science Foundation of China under Grant No. 70971076 and the Foundation of Shandong Provincial Education Department under Grant No. J10LA59.

This paper was recommended for publication by Editor Shouyang WANG.

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Sun, M., Bai, Q. A new descent memory gradient method and its global convergence. J Syst Sci Complex 24, 784–794 (2011). https://doi.org/10.1007/s11424-011-8150-0

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  • DOI: https://doi.org/10.1007/s11424-011-8150-0

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