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Strong convergence of a regularization method for Rockafellar’s proximal point algorithm

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  • ChangAn Tian
  • Yisheng Song

Abstract

In this paper, for a monotone operator T, we shall show strong convergence of the regularization method for Rockafellar’s proximal point algorithm under more relaxed conditions on the sequences {r k } and {t k }, $$\lim\limits_{k\to\infty}t_k=0;\quad \sum\limits_{k=0}^{+\infty}t_k=\infty;\quad\ \liminf\limits_{k\to\infty}r_k > 0.$$ Our results unify and improve some existing results. Copyright Springer Science+Business Media, LLC. 2013

Suggested Citation

  • ChangAn Tian & Yisheng Song, 2013. "Strong convergence of a regularization method for Rockafellar’s proximal point algorithm," Journal of Global Optimization, Springer, vol. 55(4), pages 831-837, April.
    • Handle: RePEc:spr:jglopt:v:55:y:2013:i:4:p:831-837
    DOI: 10.1007/s10898-011-9827-6
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    References listed on IDEAS

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    1. H.K. Xu, 2003. "An Iterative Approach to Quadratic Optimization," Journal of Optimization Theory and Applications, Springer, vol. 116(3), pages 659-678, March.
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