A double proximal gradient method with new linesearch for solving convex minimization problem with application to data classification

Year 2022, Volume: 5 Issue: 4, 412 - 422, 30.12.2022

Suparat Kesornprom , Prasit Cholamjiak

https://doi.org/10.53006/rna.1143531

Cited By: 1

Abstract

In this paper, we propose a new proximal gradient method for a convex minimization problem in real Hilbert spaces. We suggest a new linesearch which does not require the condition of Lipschitz constant and improve conditions of inertial term which speed up performance of convergence. Moreover, we prove the weak convergence of the proposed method under some suitable conditions. The numerical implementations in data classification are reported to show its efficiency.

Keywords

minimization problem, proximal method, linesearch rule

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