Two Relaxed Inertial Forward–Reflected–Backward Splitting Algorithms With Momentum Terms

In this paper, to solve the monotone inclusion problem consisting of the sum of two monotone operators in Hilbert spaces, we propose and study two modifications of Malitsky–Tam’s forward–reflection–backward splitting methods with double momentum terms. Meanwhile, we consider a relaxed inertial version to expand the range of allowable step sizes. Under the same assumptions as the Malitsky–Tam’s method (i.e., the set‐valued operator is maximally monotone, and the single‐valued operator is Lipschitz continuous and monotone), we prove the weak convergence and linear convergence of the proposed methods, respectively. Numerical results show that the relaxed inertial version effectively improves the convergence performance compared to the Malitsky–Tam’s splitting algorithm and its inertial version.

Authors

Institutions

Publication Details

Journal
Journal of Mathematics
Published
2025-01-01
DOI
https://doi.org/10.1155/jom/7014819
Primary Topic
Sparse and Compressive Sensing Techniques
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Two Relaxed Inertial Forward–Reflected–Backward Splitting Algorithms With Momentum Terms

Chengyou Yin, Binbin Zhang, Ximeng Guan
Journal of Mathematics
Sparse and Compressive Sensing Techniques
article

Two Relaxed Inertial Forward–Reflected–Backward Splitting Algorithms With Momentum Terms

Chengyou Yin, Binbin Zhang, Ximeng Guan
article en

Abstract

In this paper, to solve the monotone inclusion problem consisting of the sum of two monotone operators in Hilbert spaces, we propose and study two modifications of Malitsky–Tam’s forward–reflection–backward splitting methods with double momentum terms. Meanwhile, we consider a relaxed inertial version to expand the range of allowable step sizes. Under the same assumptions as the Malitsky–Tam’s method (i.e., the set‐valued operator is maximally monotone, and the single‐valued operator is Lipschitz continuous and monotone), we prove the weak convergence and linear convergence of the proposed methods, respectively. Numerical results show that the relaxed inertial version effectively improves the convergence performance compared to the Malitsky–Tam’s splitting algorithm and its inertial version.

Journal of MathematicsVol. 2025(1)
Kunming University of Science and Technology (CN)
Kunming University of Science and Technology, Yunnan Provincial Department of Education
Openalex Percentile: Top 70%
Sparse and Compressive Sensing Techniques
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Two Relaxed Inertial Forward–Reflected–Backward Splitting Algorithms With Momentum Terms — Chengyou Yin, Binbin Zhang, et al. · Journal of Mathematics (2025) | TGRS Research Map | TGRS