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

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.