对比多种基于大最小二乘问题的随机梯度方法
Compare different SG-Schemes based on large least square problems
- 从最小二乘角度分析不同超参数的随机梯度优化器
- 揭示不同优化器在大规模最小二乘问题中的性能差异
- 适合研究优化算法原理或机器学习调参的读者
本研究回顾了基于大规模最小二乘问题的主流随机梯度优化方法。这些方法在机器学习中常被称为优化器，对寻找更优模型参数至关重要。因此，本文聚焦于通过最小二乘问题视角，考察不同超参数设置下的优化器表现，并进行系统分析。相关代码已公开于 https://github.com/q-viper/gradients-based-methods-on-large-least-square。
原文摘要 · Abstract (English)
This study reviews popular stochastic gradient-based schemes based on large least-square problems. These schemes, often called optimizers in machine learning, play a crucial role in finding better model parameters. Hence, this study focuses on viewing such optimizers with different hyper-parameters and analyzing them based on least square problems. Codes that produced results in this work are available on https://github.com/q-viper/gradients-based-methods-on-large-least-square.
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