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Keywords = inertial-like proximal point algorithm

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22 pages, 1101 KB  
Article
New Self-Adaptive Inertial-like Proximal Point Methods for the Split Common Null Point Problem
by Yan Tang, Yeyu Zhang and Aviv Gibali
Symmetry 2021, 13(12), 2316; https://doi.org/10.3390/sym13122316 - 3 Dec 2021
Cited by 7 | Viewed by 2171
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 [...] Read more.
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 xn1 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. Full article
(This article belongs to the Special Issue Symmetry in Nonlinear Analysis and Fixed Point Theory)
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