In this work, we propose inclusion problems based on a novel class of forward-backward-forward algorithms. Our approach incorporates multi-inertial extrapolations and utilizes a self-adaptive technique to eliminate the need for explicitly selecting Lipschitz assumptions to enhance the speed convergence of the algorithm. We establish a weak convergence theorem under suitable assumptions. Furthermore, we conduct numerical tests on image deblurring as a practical application. The experimental results demonstrate that our algorithm surpasses some existing methods in the literature, which shows its superior performance and effectiveness.

 

 

Additional Information

Author(s)

 Inkrong, Papatsara, Cholamjiak, Prasit 

DOI

https://doi.org/10.37193/CJM.2024.02.05