对分布式随机梯度下降的过渡时间进行了精确估计
Shi Pu1, Alex Olshevsky2, Ioannis Ch Paschalidis2
1School of Data Science, Shenzhen Research Institute of Big Data, The Chinese University of Hong Kong, Shenzhen, China.
概括
这项研究分析了分布式随机梯度下降 (DSGD) 用于使用杂数据进行网络优化. DSGD实现了最佳的融合率,并对其过渡时间性能进行了新的发现.
科学领域:
- 优化理论 优化理论
- 分布式系统 分布式系统
- 机器学习 机器学习
背景情况:
- 分散优化问题涉及将网络中的平均成本函数最小化.
- 代理商通常依赖于杂的梯度信息进行决策.
- 分布式随机梯度下降 (DSGD) 是这种场景的一个关键方法.
研究的目的:
- 为了执行DSGD的非对称的收分析.
- 描述DSGD达到其非对称收率的过渡时间.
- 通过构建的优化问题来确定理论结果的清晰度.
主要方法:
- 对DSGD进行非非对称的收分析.
- 强烈凸起和光滑的客观函数的理论分析.
- 构建一个具有挑战性的优化问题的构建,以验证理论界限.
主要成果:
- 在预期中,DSGD实现了最佳的网络独立的融合率,与集中式随机梯度下降 (SGD) 相似.
- 该研究量化了DSGD接近其非对称收率所需的过渡时间.
- 一个"硬"的优化问题证明了衍生的收边界的度.
结论:
- DSGD是使用噪音梯度进行分布式优化的有效方法.
- 过渡时间的表征为DSGD的实际性能提供了关键的见解.
- 理论结果通过数值实验得到验证,证实了它们的紧密性.
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