Quantitative results on a Halpern-type proximal point algorithm

Laurenţiu Leuştean () and Pedro Pinto ()
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Laurenţiu Leuştean: University of Bucharest
Pedro Pinto: Technische Universität Darmstadt

Computational Optimization and Applications, 2021, vol. 79, issue 1, No 4, 125 pages

Abstract: Abstract 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.

Keywords: Proximal point algorithm; Maximally monotone operators; Halpern iteration; Rates of convergence; Rates of metastability; Proof mining; 47H05; 47H09; 47J25; 03F10 (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s10589-021-00263-w

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