摘要
随着互联网、物联网、传感器网络等信息技术的迅猛发展,各个应用领域的数据呈爆炸性增长,特别是电力行业,每时每刻都会产生大量的数据。在大数据时代,如何高效准确地从大规模数据集中获取有价值的知识引起学术界和工业界的日益关注。迫切需要高效的机器学习和数据挖掘技术对海量数据进行分析和处理。同时,并行计算、高性能计算和分布式计算等丰富的计算资源为大规模机器学习算法研究提供强有力的计算平台。分布式机器学习作为机器学习最重要的研究领域之一,受到广泛关注。基于此,围绕ADMM分布式算法研究工作展开,在此基础上针对L1/2稀疏迭代算法进行分布式优化。
With the rapid development of the Internet,Internet of Things,sensor networks and other information technology,the data in various application areas are exploding,especially in the power industry,where a large amount of data is generated every moment.In the era of big data,how to obtain valuable knowledge from large-scale data sets efficiently and accurately has attracted increasing attention from academia and industry.There is an urgent need for efficient machine learning and data mining techniques to analyze and process massive data.At the same time,abundant computing resources such as parallel computing,high performance computing and distributed computing provide powerful computing platforms for large-scale machine learning algorithm research.As one of the most important research areas of machine learning,distributed machine learning has received a lot of attention from researchers from all walks of life.In this paper,we make the following contributions:we focus on the research work of ADMM distributed algorithm,based on which we perform distributed optimization for L1/2 sparse iterative algorithm.
作者
李辉
黄祖源
田园
Li Hui;Huang Zu-Yuan;Tian Yuan
出处
《今日自动化》
2022年第12期134-136,共3页
Automation Today
关键词
分布式架构
ADMM
L1/2稀疏迭代算法
distributed computing
ADMM
L1/2 sparse iterative distributed algorithm
