Convergence of proximal gradient method with alternated inertial step for minimization problem
Abstract
This paper proposes an alternating inertial-type extrapolation technique for solving the convex minimization problem. We provide a self-adaptive proximal gradient technique using an inertial step. Under certain conditions, the strong convergence theorem is established. The numerical results illustrate the performances of our algorithm.
Copyright © 2024 Chuanpit Mungkala, Anantachai Padcharoen, Naknimit Akkasriworn. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Cite this article
C. Mungkala, A. Padcharoen, N. Akkasriworn, Convergence of proximal gradient method with alternated inertial step for minimization problem, Adv. Fixed Point Theory 14 (2024), 35. https://doi.org/10.28919/afpt/8625
BibTeX
@article{Mungkala2024,
author = {Mungkala, Chuanpit and Padcharoen, Anantachai and Akkasriworn, Naknimit},
title = {Convergence of proximal gradient method with alternated inertial step for minimization problem},
journal = {Adv. Fixed Point Theory},
volume = {14},
year = {2024},
pages = {35},
doi = {10.28919/afpt/8625}
}