New inertial forward–backward algorithm for convex minimization with applications

In this work, we present a new proximal gradient algorithm based on Tseng’s extragradient method and an inertial technique to solve the convex minimization problem in real Hilbert spaces. Using the stepsize rules, the selection of the Lipschitz constant of the gradient of functions is avoided. We th...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Demonstratio mathematica Ročník 56; číslo 1; s. 1168 - 1200
Hlavní autoři: Kankam, Kunrada, Cholamjiak, Watcharaporn, Cholamjiak, Prasit
Médium: Journal Article
Jazyk:angličtina
Vydáno: De Gruyter 15.02.2023
Témata:
65K05
90C25
90C30
image recovery
inertial technique
minimization problem
Tseng’s extragradient method
ISSN:2391-4661, 2391-4661
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:In this work, we present a new proximal gradient algorithm based on Tseng’s extragradient method and an inertial technique to solve the convex minimization problem in real Hilbert spaces. Using the stepsize rules, the selection of the Lipschitz constant of the gradient of functions is avoided. We then prove the weak convergence theorem and present the numerical experiments for image recovery. The comparative results show that the proposed algorithm has better efficiency than other methods.
ISSN:2391-4661
2391-4661
DOI:10.1515/dema-2022-0188