Generalized subsampled Newton methods for optimization and its application

doi:10.1007/s41060-025-00832-w

2025-06-23

PRE

Ali Shahmoradi Moghaddam

Saeed Ketabchi *

DOI：10.1007/s41060-025-00832-w

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摘要

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本文中，我们介绍了一种新颖的广义子采样牛顿方法，旨在有效解决有限和优化问题。该方法特别适用于旨在求解形如$$Ax \le b$$的线性不等式组的迭代技术，尤其是在目标函数不一定是二阶可微的情况下。该方法的实现显著提升了速度、节约了资源并优化了系统内存使用，从而改善了算法性能，尤其是在大规模问题上。所提方法的收敛性在目标函数梯度满足凸性和利普希茨连续性的条件下得到证明。在大型随机生成问题和来自UCI基准数据集及Netlib存储库的各种数据集上的初步数值实验，为所提方法的理论发现提供了实证支持，并与类似方法进行了比较。

Keyword:

Generalized Hessian

Subsampled Newton method

Stochastic optimization

Linear feasibility problem

Real world dataset

期刊

International Journal of Data Science and Analytics

IF:

2.8

论文数:

1.1K

被引数:

1.3K

机构

Faculty of Mathematical Sciences

学者数:

论文数: 34

被引数: 0

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