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On the contraction-proximal point algorithms with multi-parameters


  • Fenghui Wang


  • Huanhuan Cui


In this paper we consider the contraction-proximal point algorithm: $${x_{n+1}=\alpha_nu+\lambda_nx_n+\gamma_nJ_{\beta_n}x_n,}$$ where $${J_{\beta_n}}$$ denotes the resolvent of a monotone operator A. Under the assumption that lim n α n = 0, ∑ n α n = ∞, lim inf n β n > 0, and lim inf n γ n > 0, we prove the strong convergence of the iterates as well as its inexact version. As a result we improve and recover some recent results by Boikanyo and Morosanu. Copyright Springer Science+Business Media, LLC. 2012

Suggested Citation

  • Fenghui Wang & Huanhuan Cui, 2012. "On the contraction-proximal point algorithms with multi-parameters," Journal of Global Optimization, Springer, vol. 54(3), pages 485-491, November.
  • Handle: RePEc:spr:jglopt:v:54:y:2012:i:3:p:485-491
    DOI: 10.1007/s10898-011-9772-4

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    References listed on IDEAS

    1. Fenghui Wang, 2011. "A note on the regularized proximal point algorithm," Journal of Global Optimization, Springer, vol. 50(3), pages 531-535, July.
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    1. repec:eee:apmaco:v:265:y:2015:i:c:p:844-853 is not listed on IDEAS
    2. repec:spr:joptap:v:168:y:2016:i:3:d:10.1007_s10957-015-0835-4 is not listed on IDEAS
    3. repec:eee:apmaco:v:258:y:2015:i:c:p:67-71 is not listed on IDEAS
    4. repec:spr:joptap:v:166:y:2015:i:1:d:10.1007_s10957-014-0689-1 is not listed on IDEAS


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