A NEW INERTIAL FORWARD-BACKWARD SPLITTING ALGORITHM FOR SOLVING MONOTONE INCLUSION PROBLEM AND APPLICATIONS

被引：0

作者：

Puttharak, Phopgao ^{[1
]}

Peeyada, Pronpat ^{[2
]}

Cholamjiak, Prasit ^{[2
]}

Cholamjiak, Watcharaporn ^{[2
]}

机构：

[1] Univ Phayao, Dept Biol, Fac Sci, Phayao 56000, Thailand

[2] Univ Phayao, Sch Sci, Phayao 56000, Thailand

来源：

JOURNAL OF NONLINEAR AND CONVEX ANALYSIS | 2024年 / 25卷 / 07期

关键词：

Monotone inclusions problem; machine learning; cannabis sativa; mar-; ijuana; hemp; STRONG-CONVERGENCE; PROXIMAL METHOD; OPERATORS; CANNABINOIDS; RADIATION; EVOLUTION;

D O I：

暂无

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

The purpose of this paper is to study the monotone inclusion problem in the framework of real Hilbert spaces by combining the inertial technique with forward-backward splitting algorithm. We have proved a weak convergence for the proposed algorithm under some standard and mild conditions. Furthermore, we analyze its efficiency by applying the proposed algorithm to classification problems of cannabis sativa (marijuana, hemp) datasets and compare it with the other three algorithms. The comparison is done in terms of precision, recall, F1-score, and accuracy. The results revealed that the proposed algorithm has better efficiency in handling classification problems.

引用

页码：1659 / 1671

页数：13

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