Robust Distributed Online Convex Optimization

鲁棒分布式在线凸优化

• 批准号: 1300272
• 负责人: Jorge Cortes
• 金额: $ 28万
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-04-15 至 2017-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1300272&HistoricalAwards=false
• 关键词: Robust; Distributed; Online; Convex; Optimization

The research objective of this award is to investigate distributed algorithms for online optimization over networked multi-agent systems. Online optimization refers to the best use of limited agent resources in scenarios where information is dynamic, not a priori available, and increasingly revealed over time. An underlying assumption of present online optimization approaches is the availability of information at a central location. This assumption becomes problematic in networked scenarios, where information is distributed among agents. Transmitting all data to a central location might be costly or inefficient, and raises privacy concerns and the possibility of information leakage. The research will result in the design of robust, distributed strategies that can deal with multiple sources of disruption present in real-world applications. The research approach progresses from the synthesis of online distributed algorithms via saddle-point dynamics to the development of rigorous mathematical analysis with provably correct guarantees. Deliverables include a catalog of provably correct online distributed strategies, novel concepts and tools for the evaluation of performance and complexity, demonstration and validation via computer simulations, documentation of research results, and engineering student education.Networked robotic systems have an enormous impact in a wide range of modern applications, including oceanographic exploration, disaster recovery, environmental monitoring, and surveillance. The current trend in scientific and military domains points towards the deployment of these systems in uncertain scenarios where communication is limited and subject to failure and interference. A successful outcome of this research will enable the robust and efficient operation of networked systems in these highly decentralized scenarios. The research will be broadly disseminated and positively impact a variety of educational activities, including high-school, undergraduate, and graduate education, involvement of undergraduates in research, supervision of graduate students, and retention of minorities in STEM disciplines.

该奖项的研究目标是研究网络多智能体系统上在线优化的分布式算法。在线优化是指在信息是动态的、非先验可用的、并且随着时间的推移越来越多地显示的场景中，充分利用有限的代理资源。 当前在线优化方法的基本假设是信息在中心位置的可用性。这种假设在网络场景中会出现问题，因为信息在代理之间分发。 将所有数据传输到中央位置可能成本高昂或效率低下，并且会引起隐私问题和信息泄露的可能性。该研究将设计出稳健的分布式策略，可以处理现实应用中存在的多种干扰源。 研究方法从通过鞍点动力学综合在线分布式算法发展到具有可证明正确保证的严格数学分析的发展。 可交付成果包括一系列可证明正确的在线分布式策略、用于评估性能和复杂性的新颖概念和工具、通过计算机模拟进行演示和验证、研究结果记录以及工程学生教育。网络机器人系统在广泛的现代应用中产生巨大影响，包括海洋勘探、灾难恢复、环境监测和监视。 当前科学和军事领域的趋势表明，这些系统是在通信受限、容易出现故障和干扰的不确定场景中部署的。 这项研究的成功成果将使网络系统在这些高度分散的场景中稳健高效地运行。 该研究将得到广泛传播，并对各种教育活动产生积极影响，包括高中、本科和研究生教育、本科生参与研究、监督研究生以及保留 STEM 学科中的少数群体。