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调查针对网络多代理系统在线优化的分布式算法。在线优化是指在信息是动态的，而不是先验的情况下，随着时间的推移越来越揭示的情况，在有限的代理资源中最好使用有限的代理资源。 当前在线优化方法的基本假设是中心位置的信息的可用性。在网络场景中，此假设变得有问题，其中信息分布在代理之间。 将所有数据传输到中心位置可能是昂贵或效率低下的，并且会引起隐私问题和信息泄漏的可能性。这项研究将导致设计可靠的分布式策略，这些策略可以处理现实世界应用中存在的多种破坏来源。 研究方法从通过马鞍点动态的在线分布式算法的合成发展，再到具有正确保证的严格数学分析的开发。 可交付成果包括可证明正确的在线分布式策略的目录，新颖的概念和工具，以评估性能和复杂性，通过计算机模拟进行演示和验证，研究结果的文档以及工程学生教育。网络的机器人系统在包括海洋学探索，灾难探索，灾难恢复，环境监测和监测的各种现代应用中产生了巨大影响。 科学和军事领域的当前趋势指向这些系统在不确定的情况下部署这些系统，即通信受到限制并受到失败和干扰。 这项研究的成功结果将使在这些高度分散的方案中能够强大而有效的网络系统运行。 这项研究将广泛传播并积极影响各种教育活动，包括高中，本科和研究生教育，本科生参与研究，研究生的监督以及少数民族在STEM学科中的保留。