PMGT-VR:一个分散的近距离梯度算法框架
IEEE transactions on pattern analysis and machine intelligence
|September 5, 2025
概括
我们推出PMGT-VR, 一个新的分散算法, 它实现了与集中式方法相比较的快速收率,为分散式随机复合问题提供了第一个线性收率.
科学领域:
- 优化理论
- 分布式系统
- 机器学习
背景情况:
- 在分布式机器学习和信号处理中,分散的复合优化问题至关重要.
- 现有的去中心化算法往往需要缓慢的融合或强有力的假设.
- 弥合集中和分散优化性能之间的差距是一个关键挑战.
研究的目的:
- 为复合优化提出一种新型的分散减差近接梯度算法框架 (PMGT-VR).
- 在分散的环境中实现与集中算法相似的融合率.
- 介绍这个问题类的第一个线性收的去中心化随机算法.
主要方法:
- 开发PMGT-VR框架,结合多个共识,梯度跟踪和减小差异.
- 分析两个特定的算法:PMGT-SAGA和PMGT-LSVRG.
- 与最先进的分散的近似算法进行比较.
主要成果:
- PMGT-VR框架使去中心化算法能够模仿中心化的融合率.
- 与现有方法相比,PMGT-SAGA和PMGT-LSVRG的表现具有竞争力.
- PMGT-VR是第一个实现分散的随机复合优化的线性收的框架.
结论:
- 拟议的PMGT-VR框架显著提升了去中心化的优化.
- 开发的算法为大规模分布式问题提供了有效的解决方案.
- 数字实验验证了理论发现和实际有效性.
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