Two relaxed inertial forward-backward-forward algorithms for solving monotone inclusions and an application to compressed sensing

被引:3
作者
Tan, Bing [1 ]
Qin, Xiaolong [2 ,3 ]
机构
[1] Southwest Univ, Sch Math & Stat, Chongqing, Peoples R China
[2] Hangzhou Normal Univ, Dept Math, Hangzhou, Peoples R China
[3] Nanjing Ctr Appl Math, Nanjing, Peoples R China
来源
CANADIAN JOURNAL OF MATHEMATICS-JOURNAL CANADIEN DE MATHEMATIQUES | 2025年
关键词
Inclusion problems; monotone operator; signal recovery; forward-backward-forward method; convergence rate; SPLITTING METHOD; CONVERGENCE; OPERATORS; SUM;
D O I
10.4153/S0008414X24000889
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
Two novel algorithms, which incorporate inertial terms and relaxation effects, are introduced to tackle a monotone inclusion problem. The weak and strong convergence of the algorithms are obtained under certain conditions, and the R-linear convergence for the first algorithm is demonstrated if the set-valued operator involved is strongly monotone in real Hilbert spaces. The proposed algorithms are applied to signal recovery problems and demonstrate improved performance compared to existing algorithms in the literature.
引用
收藏
页数:22
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