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

ChangAn Tian () and Yisheng Song ()

Journal of Global Optimization, 2013, vol. 55, issue 4, 837 pages

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

Keywords: Monotone operator; Projection; Proximal point algorithm; Regularization method (search for similar items in EconPapers)
Date: 2013
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DOI: 10.1007/s10898-011-9827-6

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