A recent proximal gradient algorithm for convex minimization problem using double inertial extrapolations

In this study, we suggest a new class of forward-backward (FB) algorithms designed to solve convex minimization problems. Our method incorporates a linesearch technique, eliminating the need to choose Lipschitz assumptions explicitly. Additionally, we apply double inertial extrapolations to enhance...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	AIMS mathematics Ročník 9; číslo 7; s. 18841 - 18859
Hlavní autoři:	Kesornprom, Suparat, Inkrong, Papatsara, Witthayarat, Uamporn, Cholamjiak, Prasit
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	AIMS Press 01.01.2024
Témata:	forward-backward algorithm inertial extrapolation minimization problem weak convergence
ISSN:	2473-6988, 2473-6988
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Popis
Shrnutí:	In this study, we suggest a new class of forward-backward (FB) algorithms designed to solve convex minimization problems. Our method incorporates a linesearch technique, eliminating the need to choose Lipschitz assumptions explicitly. Additionally, we apply double inertial extrapolations to enhance the algorithm's convergence rate. We establish a weak convergence theorem under some mild conditions. Furthermore, we perform numerical tests, and apply the algorithm to image restoration and data classification as a practical application. The experimental results show our approach's superior performance and effectiveness, surpassing some existing methods in the literature.
ISSN:	2473-6988 2473-6988
DOI:	10.3934/math.2024917