An Inertial Semi-forward-reflected-backward Splitting and Its Application

Inertial methods play a vital role in accelerating the convergence speed of optimization algorithms. This work is concerned with an inertial semi-forward-reflected-backward splitting algorithm of approaching the solution of sum of a maximally monotone operator, a cocoercive operator and a monotone-L...

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Vydáno v:Acta mathematica Sinica. English series Ročník 38; číslo 2; s. 443 - 464
Hlavní autoři: Zong, Chun Xiang, Tang, Yu Chao, Zhang, Guo Feng
Médium: Journal Article
Jazyk:angličtina
Vydáno: Beijing Institute of Mathematics, Chinese Academy of Sciences and Chinese Mathematical Society 01.02.2022
Springer Nature B.V
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ISSN:1439-8516, 1439-7617
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Abstract Inertial methods play a vital role in accelerating the convergence speed of optimization algorithms. This work is concerned with an inertial semi-forward-reflected-backward splitting algorithm of approaching the solution of sum of a maximally monotone operator, a cocoercive operator and a monotone-Lipschitz continuous operator. The theoretical convergence properties of the proposed iterative algorithm are also presented under mild conditions. More importantly, we use an adaptive stepsize rule in our algorithm to avoid calculating Lipschitz constant, which is generally unknown or difficult to estimate in practical applications. In addition, a large class of composite monotone inclusion problem involving mixtures of linearly composed and parallel-sum type monotone operators is solved by combining the primal-dual approach and our proposed algorithm. As a direct application, the obtained inertial algorithm is exploited to composite convex optimization problem and some numerical experiments on image deblurring problem are also investigated to demonstrate the efficiency of the proposed algorithm.
AbstractList Inertial methods play a vital role in accelerating the convergence speed of optimization algorithms. This work is concerned with an inertial semi-forward-reflected-backward splitting algorithm of approaching the solution of sum of a maximally monotone operator, a cocoercive operator and a monotone-Lipschitz continuous operator. The theoretical convergence properties of the proposed iterative algorithm are also presented under mild conditions. More importantly, we use an adaptive stepsize rule in our algorithm to avoid calculating Lipschitz constant, which is generally unknown or difficult to estimate in practical applications. In addition, a large class of composite monotone inclusion problem involving mixtures of linearly composed and parallel-sum type monotone operators is solved by combining the primal-dual approach and our proposed algorithm. As a direct application, the obtained inertial algorithm is exploited to composite convex optimization problem and some numerical experiments on image deblurring problem are also investigated to demonstrate the efficiency of the proposed algorithm.
Author Zhang, Guo Feng
Tang, Yu Chao
Zong, Chun Xiang
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  fullname: Zong, Chun Xiang
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  givenname: Yu Chao
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  fullname: Tang, Yu Chao
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  givenname: Guo Feng
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  fullname: Zhang, Guo Feng
  email: gf_zhang@lzu.edu.cn
  organization: School of Mathematics and Statistics, Lanzhou University
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Issue 2
Keywords composite monotone inclusions
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composite convex optimization
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Operator splitting
total variation
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inertial scheme
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Snippet Inertial methods play a vital role in accelerating the convergence speed of optimization algorithms. This work is concerned with an inertial...
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SubjectTerms Algorithms
Computational geometry
Convergence
Iterative algorithms
Mathematics
Mathematics and Statistics
Operators (mathematics)
Optimization
Splitting
Title An Inertial Semi-forward-reflected-backward Splitting and Its Application
URI https://link.springer.com/article/10.1007/s10114-022-0649-x
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