On Convergence of Regularized Barzilai-Borwein Method

Fuente: arXiv
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Main Author: Xu, Xin
Format: Preprint
Published: 2025
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author Xu, Xin
author_facet Xu, Xin
contents The regularized Barzilai-Borwein (RBB) method represents a promising gradient-based optimization algorithm. In this paper, by splitting the gradient into two parts and analyzing the dynamical system of difference equations governing the ratio of their magnitudes, we establish that the RBB method achieves R-linear convergence for strongly convex quadratic functions of arbitrary dimensions. Specifically, for the two-dimensional case, we provide a concise proof demonstrating that the method exhibits at least R-linear convergence. We propose a simple yet effective adaptive regularization parameter scheme to further improve its performance. A typical numerical example verifies the effectiveness of this scheme.
format Preprint
id arxiv_https___arxiv_org_abs_2512_21972
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle On Convergence of Regularized Barzilai-Borwein Method
Xu, Xin
Optimization and Control
90C20, 90C25, 90C30
The regularized Barzilai-Borwein (RBB) method represents a promising gradient-based optimization algorithm. In this paper, by splitting the gradient into two parts and analyzing the dynamical system of difference equations governing the ratio of their magnitudes, we establish that the RBB method achieves R-linear convergence for strongly convex quadratic functions of arbitrary dimensions. Specifically, for the two-dimensional case, we provide a concise proof demonstrating that the method exhibits at least R-linear convergence. We propose a simple yet effective adaptive regularization parameter scheme to further improve its performance. A typical numerical example verifies the effectiveness of this scheme.
title On Convergence of Regularized Barzilai-Borwein Method
topic Optimization and Control
90C20, 90C25, 90C30
url https://arxiv.org/abs/2512.21972