Time-Varying Convex Optimization: Time-Structured Algorithms and Applications
Time-Varying Convex Optimization: Time-Structured Algorithms and Applications
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DOI：
10.1109/jproc.2020.3003156
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发表时间：
2020-06
影响因子：
20.6
通讯作者：
Andrea Simonetto;E. Dall’Anese;Santiago Paternain;G. Leus;G. Giannakis
中科院分区：
文献类型：
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作者：
Andrea Simonetto;E. Dall’Anese;Santiago Paternain;G. Leus;G. Giannakis
优化支撑着科学技术每天面临的许多挑战。近年来，人们见证了从基于批处理算法的传统优化范式到挑战动态、时变甚至大规模设置的重大转变。这是由技术变革推动的，技术变革将基础设施和社交平台转变为复杂和动态的网络系统，甚至具有无处不在的传感和计算能力。本文回顾了一大类用于时变优化的最先进算法，着眼于执行算法开发和性能分析。它提供了可用工具和方法的全面概述，并揭示了广泛感兴趣的应用领域中的开放挑战。现实世界的例子包括智能电力系统、机器人、机器学习和数据分析，突出了特定领域的问题和解决方案。最终目标是举例说明分析工具和相关理论基础的广泛工程相关性。
Optimization underpins many of the challenges that science and technology face on a daily basis. Recent years have witnessed a major shift from traditional optimization paradigms grounded on batch algorithms for medium-scale problems to challenging dynamic, time-varying, and even huge-size settings. This is driven by technological transformations that converted infrastructural and social platforms into complex and dynamic networked systems with even pervasive sensing and computing capabilities. This article reviews a broad class of state-of-the-art algorithms for time-varying optimization, with an eye to performing both algorithmic development and performance analysis. It offers a comprehensive overview of available tools and methods and unveils open challenges in application domains of broad range of interest. The real-world examples presented include smart power systems, robotics, machine learning, and data analytics, highlighting domain-specific issues and solutions. The ultimate goal is to exemplify wide engineering relevance of analytical tools and pertinent theoretical foundations.