Quantitative results on a Halpern-type proximal point algorithm

We apply proof mining methods to analyse a result of Boikanyo and Moroşanu on the strong convergence of a Halpern-type proximal point algorithm. As a consequence, we obtain quantitative versions of this result, providing uniform effective rates of asymptotic regularity and metastability.

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Bibliographic Details
Published in:	Computational optimization and applications Vol. 79; no. 1; pp. 101 - 125
Main Authors:	Leuştean, Laurenţiu, Pinto, Pedro
Format:	Journal Article
Language:	English
Published:	New York Springer US 01.05.2021 Springer Nature B.V
Subjects:	Algorithms Convex and Discrete Geometry Management Science Mathematics Mathematics and Statistics Operations Research Operations Research/Decision Theory Optimization Statistics Rates of convergence Rates of metastability Proof mining Maximally monotone operators Halpern iteration 47H09 Proximal point algorithm 47H05 47J25 03F10
ISSN:	0926-6003, 1573-2894
Online Access:	Get full text
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