分布不准确的牛顿方法与适应式步骤大小
Dušan Jakovetić1, Nataša Krejić1, Greta Malaspina2
1Department of Mathematics and Informatics, Faculty of Sciences, University of Novi Sad, Trg Dositeja Obradovića 4, Novi Sad, 21000 Serbia.
概括
一种名为DINAS (分布式不准确牛顿方法与自适应步骤大小) 的新方法加快了分布式优化. 它实现了个性化和共识优化问题的更快的融合,同时减少了数据共享.
科学领域:
- 分布式优化 分布式优化
- 网络系统 网络系统是网络系统.
- 机器学习 机器学习
背景情况:
- 分布式优化对于大规模问题至关重要.
- 现有的方法面临的挑战是通信和计算成本.
研究的目的:
- 介绍了一种新的方法,DINAS (分布式不准确牛顿方法与自适应步骤大小).
- 在分布式优化中提高效率并降低通信开销.
主要方法:
- DINAS使用适应式步骤大小和减少的全球参数知识.
- 它避免了当地的赫森反向计算和赫森通信.
- 提供了适应性步骤大小的不精确牛顿方法的收分析.
主要成果:
- DINAS实现了二次融合 (计算) 和线性融合 (通信) 以实现个性化的优化.
- 通信收率独立于网络拓和局部功能条件.
- 迪纳斯汇聚到共识优化问题的全球解决方案.
- 数字实验显示,比现有方法有显著的改进.
结论:
- DINAS为分布式优化提供了一种更有效,更实用的方法.
- 该方法在不同的网络结构和问题的复杂性中是稳健的.
- 提供了适应性步骤大小方法的理论见解.
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