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A Modified Non-Monotone BFGS Method for Non-Convex Unconstrained Optimization

Author

Listed:
  • Liying Liu

    (College of Mathematics Science, Liaocheng University, 1 Hunan Avenue, Liaocheng, Shandong 252059, P. R. China)

  • Shengwei Yao

    (School of Science, East China University of Science and Technology, Shanghai 200237, P. R. China;
    Department of Mathematics and Statistics, Guangxi University of Finance and Economics, 100 Mingxiu Avenue, Nanning, Guangxi 530003, P. R. China)

  • Zengxin Wei

    (College of Mathematics and Information Science, Guangxi University, 10 Daxue Avenue, Nanning, Guangxi 530004, P. R. China)

Abstract

In this paper, a modified non-monotone BFGS (MNBFGS) method for non-convex unconstrained optimization is proposed. Under some mild conditions, the global convergence of the given method is established, when the objective function is non-convex. Preliminary numerical comparisons, which show the proposed method is competitive, are also reported.

Suggested Citation

  • Liying Liu & Shengwei Yao & Zengxin Wei, 2014. "A Modified Non-Monotone BFGS Method for Non-Convex Unconstrained Optimization," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 31(05), pages 1-15.
    • Handle: RePEc:wsi:apjorx:v:31:y:2014:i:05:n:s021759591450033x
    DOI: 10.1142/S021759591450033X
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    Cited by:

    1. S. Bojari & M. R. Eslahchi, 2020. "Global convergence of a family of modified BFGS methods under a modified weak-Wolfe–Powell line search for nonconvex functions," 4OR, Springer, vol. 18(2), pages 219-244, June.

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