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Strong Convergence of a Projected Gradient Method

Author

Listed:
  • Shunhou Fan
  • Yonghong Yao

Abstract

The projected‐gradient method is a powerful tool for solving constrained convex optimization problems and has extensively been studied. In the present paper, a projected‐gradient method is presented for solving the minimization problem, and the strong convergence analysis of the suggested gradient projection method is given.

Suggested Citation

  • Shunhou Fan & Yonghong Yao, 2012. "Strong Convergence of a Projected Gradient Method," Journal of Applied Mathematics, John Wiley & Sons, vol. 2012(1).
    • Handle: RePEc:wly:jnljam:v:2012:y:2012:i:1:n:410137
    DOI: 10.1155/2012/410137
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    References listed on IDEAS

    as
    1. Yonghong Yao & Muhammad Aslam Noor & Yeong-Cheng Liou, 2012. "Strong Convergence of a Modified Extragradient Method to the Minimum-Norm Solution of Variational Inequalities," Abstract and Applied Analysis, Hindawi, vol. 2012, pages 1-9, December.
    2. Yonghong Yao & Shin Min Kang & Wu Jigang & Pei-Xia Yang, 2012. "A Regularized Gradient Projection Method for the Minimization Problem," Journal of Applied Mathematics, Hindawi, vol. 2012, pages 1-9, February.
    3. Yonghong Yao & Muhammad Aslam Noor & Yeong-Cheng Liou, 2012. "Strong Convergence of a Modified Extragradient Method to the Minimum‐Norm Solution of Variational Inequalities," Abstract and Applied Analysis, John Wiley & Sons, vol. 2012(1).
    4. Yonghong Yao & Shin Min Kang & Wu Jigang & Pei-Xia Yang, 2012. "A Regularized Gradient Projection Method for the Minimization Problem," Journal of Applied Mathematics, John Wiley & Sons, vol. 2012(1).
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    Cited by:

    1. He, Yong & Li, Lingxiao & Liu, Dong & Zhou, Wen-Xin, 2025. "Huber Principal Component Analysis for large-dimensional factor models," Journal of Econometrics, Elsevier, vol. 249(PB).

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