论文标题
时变凸优化：时间结构算法和应用
Time-Varying Convex Optimization: Time-Structured Algorithms and Applications
论文作者
论文摘要
优化基于科学和技术每天面临的许多挑战。近年来，从基于中等规模问题的批处理算法的传统优化范式转变为挑战性的动态，时间变化甚至庞大的设置。这是由技术转变驱动的，该技术转换将基础设施和社交平台转换为具有无处不在的感应和计算功能的复杂而动态的网络系统。本文回顾了一系列最先进的算法，以进行时变优化，并着眼于算法开发和性能分析。它提供了有关可用工具和方法的全面概述，并在广泛关注的应用领域中揭示了挑战。提出的现实示例包括智能电力系统，机器人技术，机器学习和数据分析，突出了特定领域的问题和解决方案。最终目标是体现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. The present paper reviews a broad class of state-of-the-art algorithms for time-varying optimization, with an eye to 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 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 exempify wide engineering relevance of analytical tools and pertinent theoretical foundations.
