Abstract
We introduce four novel relaxed CQ algorithms with alternating inertial for solving split feasibility problems in real Hilbert spaces. The proposed algorithms employ a new non-monotonic adaptive step size criterion and utilize two different step sizes in each iteration. The weak convergence of the iterative sequences generated by the proposed algorithms is established under some weak conditions. Moreover, the Fejér monotonicity of the even subsequence with respect to the solution set is recovered. Two applications in signal denoising and image deblurring are given to illustrate the computational efficiency of our algorithms.
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The datasets generated during and/or analyzed during the current study are available from the corresponding author.
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Available on the website https://www.tau.ac.il/~becka/home
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Acknowledgements
The authors would like to thank the handling editor and the anonymous referee for careful reading and constructive comments, which improved the quality of the manuscript.
Funding
Bing Tan was supported by the China Scholarship Council (CSC No. 202106070094). Xianfu Wang was partially supported by the Natural Sciences and Engineering Research Council of Canada.
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Tan, B., Qin, X. & Wang, X. Alternated inertial algorithms for split feasibility problems. Numer Algor 95, 773–812 (2024). https://doi.org/10.1007/s11075-023-01589-8
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DOI: https://doi.org/10.1007/s11075-023-01589-8
Keywords
- Split feasibility problem
- CQ method
- Projection and contraction method
- Alternated inertial method
- Signal processing
- Image restoration