Generalized Bregman Projection Algorithms for Solving Nonlinear Split Feasibility Problems in Infinite-Dimensional Spaces

Fuente: arXiv
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Main Authors: Sababe, Saeed Hashemi, Ghasab, Ehsan Lotfali
Format: Preprint
Published: 2025
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author Sababe, Saeed Hashemi
Ghasab, Ehsan Lotfali
author_facet Sababe, Saeed Hashemi
Ghasab, Ehsan Lotfali
contents This paper introduces generalized Bregman projection algorithms for solving nonlinear split feasibility problems (SF P s) in infinitedimensional Hilbert spaces. The methods integrate Bregman projections, proximal gradient steps, and adaptive inertial terms to enhance convergence. Strong convergence is established under mild assumptions, and numerical experiments demonstrate the efficiency and robustness of the proposed algorithms in comparison to classical methods. These results contribute to advancing optimization techniques for nonlinear and high-dimensional problems.
format Preprint
id arxiv_https___arxiv_org_abs_2505_11537
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Generalized Bregman Projection Algorithms for Solving Nonlinear Split Feasibility Problems in Infinite-Dimensional Spaces
Sababe, Saeed Hashemi
Ghasab, Ehsan Lotfali
Optimization and Control
This paper introduces generalized Bregman projection algorithms for solving nonlinear split feasibility problems (SF P s) in infinitedimensional Hilbert spaces. The methods integrate Bregman projections, proximal gradient steps, and adaptive inertial terms to enhance convergence. Strong convergence is established under mild assumptions, and numerical experiments demonstrate the efficiency and robustness of the proposed algorithms in comparison to classical methods. These results contribute to advancing optimization techniques for nonlinear and high-dimensional problems.
title Generalized Bregman Projection Algorithms for Solving Nonlinear Split Feasibility Problems in Infinite-Dimensional Spaces
topic Optimization and Control
url https://arxiv.org/abs/2505.11537