Suparat KesornpromKunrada KankamPapatsara InkrongNattawut PholasaPrasit Cholamjiak2025-03-102025-03-102024Journal of Nonlinear Functional Analysis2052532X10.23952/jnfa.2024.142-s2.0-85206478750https://repository.dusit.ac.th//handle/123456789/4463This 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. ©2024 Journal of Nonlinear Functional Analysis.Constrained convex minimizationImage recoveryProjected forward-backward methodSignal recoveryArticleScopus