Accelerated Self-Adaptive Method for Solving Nonsmooth Convex Minimization Problem in Real Hilbert Spaces

Mokaba, L.; Abass, Hammed A.; Oyewole, Olawale K.; Malebana, Koketso P.

doi:10.28924/2291-8639-23-2025-160

Research Article

Accelerated Self-Adaptive Method for Solving Nonsmooth Convex Minimization Problem in Real Hilbert Spaces

L. Mokaba
Hammed A. Abass*
Olawale K. Oyewole
Koketso P. Malebana

* Corresponding author

Abstract

In this manuscript, we propose a proximal gradient type algorithm together with a two step inertia method for approximating solution of convex minimization problem in real Hilbert spaces. The proposed proximal gradient type method is designed in such a way that it does not depend on the Lipschitz constant. Using a self-adaptive rule, we obtain a weak convergence result under the condition that the gradient function of one of the convex functions is uniformly continuous. Preliminary numerical results show that our proposed method has a better convergence in comparison to some other related results in the literature.

How to Cite

L. Mokaba, H. A. Abass, O. K. Oyewole, K. P. Malebana, Accelerated Self-Adaptive Method for Solving Nonsmooth Convex Minimization Problem in Real Hilbert Spaces, Int. J. Anal. Appl. 23 (2025), 160. https://doi.org/10.28924/2291-8639-23-2025-160

BibTeX

@article{Mokaba2025,
  author  = {Mokaba, L. and Abass, Hammed A. and Oyewole, Olawale K. and Malebana, Koketso P.},
  title   = {Accelerated Self-Adaptive Method for Solving Nonsmooth Convex Minimization Problem in Real Hilbert Spaces},
  journal = {Int. J. Anal. Appl.},
  volume  = {23},
  year    = {2025},
  pages   = {160},
  doi     = {10.28924/2291-8639-23-2025-160}
}

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