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An Inertial Spectral CG Projection Method Based on the Memoryless BFGS Update

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  • Xiaoyu Wu

    (China University of Mining and Technology)

  • Hu Shao

    (China University of Mining and Technology)

  • Pengjie Liu

    (China University of Mining and Technology)

  • Yue Zhuo

    (China University of Mining and Technology)

Abstract

Combining the derivative-free projection with inertial technique, we propose a hybrid inertial spectral conjugate gradient projection method for solving constrained nonlinear monotone equations. The conjugate parameter is a hybrid modification based on the memoryless BFGS update. The spectral parameter is obtained from quasi-Newton equations and double-truncated to ensure the sufficient descent. The search direction with a restart procedure satisfies sufficient descent condition and the trust region property at each iteration, independent of the choice of line search. We also investigate the theoretical properties, such as the global convergence and linear convergence rate, of the inertial projection method under normal assumptions. Numerical performances indicate the superiority of the proposed method in solving large-scale equations and restoring the blurred images contaminated by the Gaussian noise.

Suggested Citation

  • Xiaoyu Wu & Hu Shao & Pengjie Liu & Yue Zhuo, 2023. "An Inertial Spectral CG Projection Method Based on the Memoryless BFGS Update," Journal of Optimization Theory and Applications, Springer, vol. 198(3), pages 1130-1155, September.
    • Handle: RePEc:spr:joptap:v:198:y:2023:i:3:d:10.1007_s10957-023-02265-6
    DOI: 10.1007/s10957-023-02265-6
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    References listed on IDEAS

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    1. Parvaneh Faramarzi & Keyvan Amini, 2019. "A Modified Spectral Conjugate Gradient Method with Global Convergence," Journal of Optimization Theory and Applications, Springer, vol. 182(2), pages 667-690, August.
    2. Gonglin Yuan & Zehong Meng & Yong Li, 2016. "A Modified Hestenes and Stiefel Conjugate Gradient Algorithm for Large-Scale Nonsmooth Minimizations and Nonlinear Equations," Journal of Optimization Theory and Applications, Springer, vol. 168(1), pages 129-152, January.
    3. Predrag S. Stanimirović & Branislav Ivanov & Snežana Djordjević & Ivona Brajević, 2018. "New Hybrid Conjugate Gradient and Broyden–Fletcher–Goldfarb–Shanno Conjugate Gradient Methods," Journal of Optimization Theory and Applications, Springer, vol. 178(3), pages 860-884, September.
    4. Papp, Zoltan & Rapajić, Sanja, 2015. "FR type methods for systems of large-scale nonlinear monotone equations," Applied Mathematics and Computation, Elsevier, vol. 269(C), pages 816-823.
    5. Gao, Peiting & He, Chuanjiang & Liu, Yang, 2019. "An adaptive family of projection methods for constrained monotone nonlinear equations with applications," Applied Mathematics and Computation, Elsevier, vol. 359(C), pages 1-16.
    6. XiaoLiang Dong & Hongwei Liu & Yubo He, 2015. "A Self-Adjusting Conjugate Gradient Method with Sufficient Descent Condition and Conjugacy Condition," Journal of Optimization Theory and Applications, Springer, vol. 165(1), pages 225-241, April.
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