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Spectral Scaling BFGS Method

Author

Listed:
  • W. Y. Cheng

    (Dongguan University of Technology)

  • D. H. Li

    (South China Normal University)

Abstract

In this paper, we scale the quasiNewton equation and propose a spectral scaling BFGS method. The method has a good selfcorrecting property and can improve the behavior of the BFGS method. Compared with the standard BFGS method, the single-step convergence rate of the spectral scaling BFGS method will not be inferior to that of the steepest descent method when minimizing an n-dimensional quadratic function. In addition, when the method with exact line search is applied to minimize an n-dimensional strictly convex function, it terminates within n steps. Under appropriate conditions, we show that the spectral scaling BFGS method with Wolfe line search is globally and R-linear convergent for uniformly convex optimization problems. The reported numerical results show that the spectral scaling BFGS method outperforms the standard BFGS method.

Suggested Citation

  • W. Y. Cheng & D. H. Li, 2010. "Spectral Scaling BFGS Method," Journal of Optimization Theory and Applications, Springer, vol. 146(2), pages 305-319, August.
    • Handle: RePEc:spr:joptap:v:146:y:2010:i:2:d:10.1007_s10957-010-9652-y
    DOI: 10.1007/s10957-010-9652-y
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    References listed on IDEAS

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    1. M. Al-Baali, 1998. "Numerical Experience with a Class of Self-Scaling Quasi-Newton Algorithms," Journal of Optimization Theory and Applications, Springer, vol. 96(3), pages 533-553, March.
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    Cited by:

    1. Shummin Nakayama & Yasushi Narushima & Hiroshi Yabe, 2021. "Inexact proximal memoryless quasi-Newton methods based on the Broyden family for minimizing composite functions," Computational Optimization and Applications, Springer, vol. 79(1), pages 127-154, May.
    2. Yasushi Narushima & Shummin Nakayama & Masashi Takemura & Hiroshi Yabe, 2023. "Memoryless Quasi-Newton Methods Based on the Spectral-Scaling Broyden Family for Riemannian Optimization," Journal of Optimization Theory and Applications, Springer, vol. 197(2), pages 639-664, May.
    3. Hiroyuki Sakai & Hideaki Iiduka, 2024. "Modified Memoryless Spectral-Scaling Broyden Family on Riemannian Manifolds," Journal of Optimization Theory and Applications, Springer, vol. 202(2), pages 834-853, August.
    4. Neculai Andrei, 2018. "A Double-Parameter Scaling Broyden–Fletcher–Goldfarb–Shanno Method Based on Minimizing the Measure Function of Byrd and Nocedal for Unconstrained Optimization," Journal of Optimization Theory and Applications, Springer, vol. 178(1), pages 191-218, July.

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