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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Podrobná bibliografia
Vydané v:	Computational optimization and applications Ročník 79; číslo 1; s. 101 - 125
Hlavní autori:	Leuştean, Laurenţiu, Pinto, Pedro
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Springer US 01.05.2021 Springer Nature B.V
Predmet:	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
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Popis
Shrnutí:	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.
Bibliografia:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ISSN:	0926-6003 1573-2894
DOI:	10.1007/s10589-021-00263-w