The purpose of this paper is to introduce a new hybrid algorithm for finding a global minimization of best proximity points for a new class of mappings, called best proximally nonexpansive (BPNE), which is weaker than nonself nonexpansive mappings and then prove strong convergence of the proposed method under some suitable conditions in real Hilbert spaces. Finally, some numerical experiment is also given for demonstrating our main result.
| Author(s) | Suantai, Suthep, Suparatulatorn, Raweerote |
|---|---|
| DOI | https://doi.org/10.37193/CJM.2019.01.11 |
Approximate Solutions for Multi-Objective Optimization Problems via Scalarizing and Nonscalarizing Methods
A product of strongly quasi-nonexpansive mappings in Hadamard spaces
Inertial viscosity fixed point algorithms for monotone inclusion, convex minimization, and image restoration problems
Holderian Stability for Parametric Optimization Models and Applications
A Novel Fixed-Point Based Two-Step Inertial Algorithm for Convex Bilevel Optimization in Deep Learning Data Classification
Double-step inertial algorithms for equilibrium and fixed point problems in Hilbert spaces
Accelerated forward-backward algorithm based on inertial and correction terms with linesearch for solving convex minimization problem and its application
A Dual-Projective Double Inertial Forward-Backward Splitting Algorithm for Variational Inclusion Problems with Applications to External Validation in Breast Cancer Diagnosis
Efficient Large-Scale Classification with Linex Least Square Twin Bounded Support Vector Machine
Classifying compressive strength of clay bricks using an inertial projected forward-backward algorithm
