Distributed Synchronous and Asynchronous Stochastic Optimization Algorithms over Networks

网络分布式同步和异步随机优化算法

• 批准号: 2012439
• 负责人: Ming Yan
• 金额: $ 20万
• 依托单位: Michigan State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-01 至 2023-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2012439&HistoricalAwards=false
• 关键词: Distributed; Synchronous; Asynchronous; Stochastic; Optimization

The number of installed internet of things (IoT) devices reached 26.66 billion in 2019, and this number will reach 75 billion by 2025. These devices collect massive volumes of datasets, and the analysis of these datasets can significantly improve our daily lives. However, how to process these datasets efficiently is still challenging. First, it is impossible to transfer all the data to a location because of the large volume and privacy concerns. Second, the networks formed by these devices are complex. Thus, existing distributed methods can not be directly applied to this scenario, and novel algorithms based on the communication between these devices have to be developed to make sense of these large-scale datasets.In this project, the PI will tackle the major drawbacks of existing decentralized consensus algorithms and greatly promote the efficiency and scalability of large-scale decentralized algorithms. To achieve this goal, the PI will systematically investigate the theoretical understanding of decentralized algorithms and two major challenges, i.e., large-scale data and large-scale networks. There are three objectives. The first objective is a better convergence rate for existing, and new, decentralized deterministic algorithms. The success of this objective will be the first step that will form the foundation of the next two objectives. The second objective is to develop decentralized stochastic algorithms with variance reduction for large-scale data. The last objective is asynchronous decentralized algorithms. This project will pave the way for new research endeavors to deal with large-scale distributed datasets and largely push the research boundaries of decentralized optimization in various application domains. This research will impact the use of decentralized algorithms in fields including wireless sensor networks, machine learning, the internet of things, and healthcare.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

2019年，安装的物联网设备（IoT）设备的数量达到266.6亿，到2025年，该数量将达到750亿。这些设备收集了大量数据集，对这些数据集的分析可以显着改善我们的日常生活。但是，如何有效地处理这些数据集仍然具有挑战性。首先，由于众多的批量和隐私问题，不可能将所有数据转移到一个位置。其次，这些设备形成的网络很复杂。因此，现有的分布式方法不能直接应用于这种情况，并且必须开发基于这些设备之间的通信的新颖算法来理解这些大型数据集。在此项目中，PI将解决主要缺点。现有的分散共识算法，并大大促进了大规模分散算法的效率和可扩展性。为了实现这一目标，PI将系统地研究对分散算法和两个主要挑战的理论理解，即大规模数据和大规模网络。有三个目标。第一个目标是针对现有的，新的，分散的确定性算法的融合率更好。这个目标的成功将是将构成接下来两个目标的基础的第一步。第二个目标是开发分散的随机算法，以减少大规模数据的方差。最后一个目标是异步分散算法。该项目将为新的研究努力处理大规模分布式数据集铺平道路，并在很大程度上推动了各个应用领域中分散优化的研究界限。这项研究将影响分散算法在包括无线传感器网络，机器学习，物联网和医疗保健等领域的使用。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响来评估的。审查标准。

项目成果

Phase retrieval from incomplete data via weighted nuclear norm minimization

• DOI: 10.1016/j.patcog.2022.108537
• 发表时间: 2022-01
• 期刊: Pattern Recognit.
• 作者: Zhi Li;Ming Yan;T. Zeng;Guixu Zhang

On the Linear Convergence of Two Decentralized Algorithms

两种去中心化算法的线性收敛性

• DOI: 10.1007/s10957-021-01833-y
• 发表时间: 2021
• 期刊: Journal of Optimization Theory and Applications
• 影响因子: 1.9
• 作者: Li, Yao;Yan, Ming
• 通讯作者: Yan, Ming

Image enhancement in active incoherent millimeter-wave imaging

主动非相干毫米波成像中的图像增强

• DOI: 10.1117/12.2585650
• 发表时间: 2021
• 作者: Vakalis, Stavros;Chen, Daniel;Yan, Ming;Nanzer, Jeffrey
• 通讯作者: Nanzer, Jeffrey

A Novel Regularization Based on the Error Function for Sparse Recovery

一种基于误差函数的新颖正则化稀疏恢复方法

• DOI: 10.1007/s10915-021-01443-w
• 发表时间: 2021
• 期刊: Journal of Scientific Computing
• 影响因子: 2.5
• 作者: Guo, Weihong;Lou, Yifei;Qin, Jing;Yan, Ming
• 通讯作者: Yan, Ming

Linear Convergent Decentralized Optimization with Compression

• 发表时间: 2020-07
• 期刊: ArXiv
• 作者: Xiaorui Liu;Yao Li;Rongrong Wang;Jiliang Tang;Ming Yan