Work

A golden ratio proximal method for monotone variational inequality problems

Year: 2025

Type: article

Abstract: In this work, we propose a golden ratio proximal algorithm for solving monotone variational inequality problems in a real Hilbert space. It is a self-adaptive technique, endowed with a conjugate gradient type direction to facilitate convergence, and requires one evaluation of the underlying operator in an iteration. We established the convergence of the proposed method under appropriate assumptions. Numerical examples demonstrate the robustness and efficiency of the proposed method compared with some state-of-the-art methods. Additionally, we present an application of the proposed algorithms in the tomography reconstruction problem.

Source: Fixed Point Theory and Algorithms for Sciences and Engineering

DOI: 10.1186/s13663-025-00814-4

Authors: Ibrahim Arzuka, Parin Chaipunya, Poom Kumam

Institutions: Sa'adu Zungur University, Center of Excellence in Theoretical and Computational Science, King Mongkut's University of Technology Thonburi

Language: English

Topic: Optimization and Variational Analysis

Subfield: Computational Theory and Mathematics

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,790

OpenAlex ID: W4417026176

FWCI: 0.496

Cites: 21

Cited by: 1

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