The Modified Viscosity Approximation Method with Inertial Technique and Forward–Backward Algorithm for Convex Optimization Model

In this paper, we propose a new accelerated algorithm for finding a common fixed point of nonexpansive operators, and then, a strong convergence result of the proposed method is discussed and analyzed in real Hilbert spaces. As an application, we create a new accelerated viscosity forward–backward m...

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Main Authors: Adisak Hanjing, Limpapat Bussaban, Suthep Suantai
Format: Article
Language:English
Published: MDPI AG 2022-03-01
Series:Mathematics
Subjects:
Online Access:https://www.mdpi.com/2227-7390/10/7/1036
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author Adisak Hanjing
Limpapat Bussaban
Suthep Suantai
author_facet Adisak Hanjing
Limpapat Bussaban
Suthep Suantai
author_sort Adisak Hanjing
collection DOAJ
description In this paper, we propose a new accelerated algorithm for finding a common fixed point of nonexpansive operators, and then, a strong convergence result of the proposed method is discussed and analyzed in real Hilbert spaces. As an application, we create a new accelerated viscosity forward–backward method (AVFBM) for solving nonsmooth optimization problems of the sum of two objective functions in real Hilbert spaces, and the strong convergence of AVFBM to a minimizer of the sum of two convex functions is established. We also present the application and simulated results of AVFBM for image restoration and data classification problems.
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spelling doaj.art-891c28c5c4414386ac5d0c968c73542a2023-11-30T23:36:14ZengMDPI AGMathematics2227-73902022-03-01107103610.3390/math10071036The Modified Viscosity Approximation Method with Inertial Technique and Forward–Backward Algorithm for Convex Optimization ModelAdisak Hanjing0Limpapat Bussaban1Suthep Suantai2Department of Science and Mathematics, Rajamangala University of Technology Isan Surin Campus, Surin 32000, ThailandFaculty of Science, Chiang Mai University, Chiang Mai 50200, ThailandData Science Research Center, Department of Mathematics, Faculty of Science, Chiang Mai University, Chiang Mai 50200, ThailandIn this paper, we propose a new accelerated algorithm for finding a common fixed point of nonexpansive operators, and then, a strong convergence result of the proposed method is discussed and analyzed in real Hilbert spaces. As an application, we create a new accelerated viscosity forward–backward method (AVFBM) for solving nonsmooth optimization problems of the sum of two objective functions in real Hilbert spaces, and the strong convergence of AVFBM to a minimizer of the sum of two convex functions is established. We also present the application and simulated results of AVFBM for image restoration and data classification problems.https://www.mdpi.com/2227-7390/10/7/1036Hilbert spacecommon fixed pointsviscosity forward–backward algorithmconvergence theoremsconvex optimization model
spellingShingle Adisak Hanjing
Limpapat Bussaban
Suthep Suantai
The Modified Viscosity Approximation Method with Inertial Technique and Forward–Backward Algorithm for Convex Optimization Model
Mathematics
Hilbert space
common fixed points
viscosity forward–backward algorithm
convergence theorems
convex optimization model
title The Modified Viscosity Approximation Method with Inertial Technique and Forward–Backward Algorithm for Convex Optimization Model
title_full The Modified Viscosity Approximation Method with Inertial Technique and Forward–Backward Algorithm for Convex Optimization Model
title_fullStr The Modified Viscosity Approximation Method with Inertial Technique and Forward–Backward Algorithm for Convex Optimization Model
title_full_unstemmed The Modified Viscosity Approximation Method with Inertial Technique and Forward–Backward Algorithm for Convex Optimization Model
title_short The Modified Viscosity Approximation Method with Inertial Technique and Forward–Backward Algorithm for Convex Optimization Model
title_sort modified viscosity approximation method with inertial technique and forward backward algorithm for convex optimization model
topic Hilbert space
common fixed points
viscosity forward–backward algorithm
convergence theorems
convex optimization model
url https://www.mdpi.com/2227-7390/10/7/1036
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