New Self-Adaptive Inertial-like Proximal Point Methods for the Split Common Null Point Problem

Yan Tang,Yeyu Zhang,Aviv Gibali

doi:10.3390/sym13122316

Abstract

Symmetry plays an important role in solving practical problems of applied science, especially in algorithm innovation. In this paper, we propose what we call the self-adaptive inertial-like proximal point algorithms for solving the split common null point problem, which use a new inertial structure to avoid the traditional convergence condition in general inertial methods and avoid computing the norm of the difference between xn and xn−1 before choosing the inertial parameter. In addition, the selection of the step-sizes in the inertial-like proximal point algorithms does not need prior knowledge of operator norms. Numerical experiments are presented to illustrate the performance of the algorithms. The proposed algorithms provide enlightenment for the further development of applied science in order to dig deep into symmetry under the background of technological innovation.

Highlights

ObjectivesThe purpose of this paper is to present a new self-adaptive inertial-like technique to give an affirmative answer to the above questions
Combining the inertial-like proximal point algorithm and the forward–backward method, we propose the following self adaptive inertial-like proximal algorithms
We proposed two new self-adaptive inertial-like proximal point algorithms (Algorithms 1 and 2) for the split common null point problem (SCNPP)