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New fast proximal point algorithms for monotone inclusion problems with applications to image recovery

New fast proximal point algorithms for monotone inclusion problems with applications to image recovery
New fast proximal point algorithms for monotone inclusion problems with applications to image recovery

The proximal point algorithm has many applications for convex optimization with several versions of the proximal point algorithm including generalized proximal point algorithms and accelerated proximal point algorithms that have been studied in the literature. In this paper, we propose accelerated versions of generalized proximal point algorithms to find a zero of a maximal monotone operator in Hilbert spaces. We give both weak and linear convergence results of our proposed algorithms under standard conditions. Numerical applications of our results to image recovery are given and numerical implementations show that our algorithms are effective and superior to other related accelerated proximal point algorithms in the literature.

Accelerated proximal point algorithm, image recovery, maximal monotone operators, weak & linear convergence
0233-1934
Jolaoso, Lateef O.
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Bai, Jianchao
071d75c0-cbc7-46a4-ae99-31029c21e79b
Shehu, Yekini
df727925-5bf0-457a-87fa-f70de3bfd11a
Jolaoso, Lateef O.
102467df-eae0-4692-8668-7f73e8e02546
Bai, Jianchao
071d75c0-cbc7-46a4-ae99-31029c21e79b
Shehu, Yekini
df727925-5bf0-457a-87fa-f70de3bfd11a

Jolaoso, Lateef O., Bai, Jianchao and Shehu, Yekini (2024) New fast proximal point algorithms for monotone inclusion problems with applications to image recovery. Optimization. (doi:10.1080/02331934.2024.2345761).

Record type: Article

Abstract

The proximal point algorithm has many applications for convex optimization with several versions of the proximal point algorithm including generalized proximal point algorithms and accelerated proximal point algorithms that have been studied in the literature. In this paper, we propose accelerated versions of generalized proximal point algorithms to find a zero of a maximal monotone operator in Hilbert spaces. We give both weak and linear convergence results of our proposed algorithms under standard conditions. Numerical applications of our results to image recovery are given and numerical implementations show that our algorithms are effective and superior to other related accelerated proximal point algorithms in the literature.

Text
New_Accelerated_Proximal_Point_Algorithms_for_Monotone_Inclusion_Problems_with_Applications_to_Image_Recovery (6) - Accepted Manuscript
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More information

Accepted/In Press date: 12 April 2024
e-pub ahead of print date: 2 May 2024
Published date: 2 May 2024
Keywords: Accelerated proximal point algorithm, image recovery, maximal monotone operators, weak & linear convergence

Identifiers

Local EPrints ID: 491518
URI: http://eprints.soton.ac.uk/id/eprint/491518
ISSN: 0233-1934
PURE UUID: b95d487c-6e85-4a90-8c96-0d490a81ce12
ORCID for Lateef O. Jolaoso: ORCID iD orcid.org/0000-0002-4838-7465

Catalogue record

Date deposited: 25 Jun 2024 16:58
Last modified: 12 Jul 2024 02:10

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Contributors

Author: Jianchao Bai
Author: Yekini Shehu

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