Robust Distributed Online Convex Optimization

该奖项的研究目标是研究分布式算法在线优化网络多智能体系统。在线优化是指在信息是动态的、不是先验可用的、并且随着时间的推移越来越多地被揭示的情况下，最好地利用有限的代理资源。 当前在线优化方法的基本假设是在中心位置处的信息的可用性。这种假设在网络场景中变得有问题，其中信息在代理之间分布。 将所有数据传输到一个中央位置可能成本高昂或效率低下，并引起隐私问题和信息泄露的可能性。该研究将导致设计出强大的分布式策略，可以处理现实世界应用中存在的多种中断源。 研究方法从通过鞍点动力学的在线分布式算法的合成进展到具有可证明正确保证的严格数学分析的发展。 可验证的内容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、研究结果文档以及工程专业学生教育。联网机器人系统在现代广泛的应用中产生了巨大的影响，包括海洋勘探、灾难恢复、环境监测和监视。 科学和军事领域目前的趋势是在通信有限、易受故障和干扰的不确定情况下部署这些系统。 这项研究的成功结果将使网络系统在这些高度分散的场景中能够稳健和高效地运行。 该研究将被广泛传播，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，研究生的监督以及少数民族在STEM学科中的保留。

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.