求解非线性方程组的随机柱块梯度下降法

IF 2.8 2区数学 Q1 MATHEMATICS, APPLIED

Applied Mathematics Letters Pub Date : 2025-08-28 DOI:10.1016/j.aml.2025.109735

Naiyu Jiang, Wendi Bao, Lili Xing, Weiguo Li

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引用次数: 0

摘要

本文提出了一种求解非线性方程组的随机柱块梯度下降法。它有一个下降方向，并保持通过优化问题得到的近似最优步长。对该方法进行了全面的收敛性分析，并给出了该方法收敛速度的上界。数值实验表明，该方法优于现有方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

A stochastic column-block gradient descent method for solving nonlinear systems of equations

In this paper, we propose a new stochastic column-block gradient descent method for solving nonlinear systems of equations. It has a descent direction and holds an approximately optimal step size obtained through an optimization problem. We provide a thorough convergence analysis, and derive an upper bound for the convergence rate of the new method. Numerical experiments demonstrate that the proposed method outperforms the existing ones.

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来源期刊

Applied Mathematics Letters 数学-应用数学

CiteScore

7.70

自引率

5.40%

发文量

347

审稿时长

10 days

期刊介绍： The purpose of Applied Mathematics Letters is to provide a means of rapid publication for important but brief applied mathematical papers. The brief descriptions of any work involving a novel application or utilization of mathematics, or a development in the methodology of applied mathematics is a potential contribution for this journal. This journal''s focus is on applied mathematics topics based on differential equations and linear algebra. Priority will be given to submissions that are likely to appeal to a wide audience.