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DOI: 10.23952/jnfa.2024.14
Received February 23, 2023; Accepted June 15, 2024; Published July 8, 2024
Abstract. This paper presents a new variant of the proximal gradient algorithm based on double inertial extrapolation to solve a constrained convex minimization problem in real Hilbert spaces. We discuss its weak convergence, including numerical image and signal recovery experiments to support the main results. Some comparisons with other algorithms are also provided. The experiments demonstrate that our method converges better than the other methods in the literature.
How to Cite this Article:
S. Kesornprom, K. Kankam, P. Inkrong, N. Pholasa, P. Cholamjiak, A variant of the proximal gradient method for constrained convex minimization problems, J. Nonlinear Funct. Anal. 2024 (2024) 14.