Robust Distributed Online Convex Optimization

本奖项的研究目标是研究网络多智能体系统在线优化的分布式算法。在线优化指的是在信息是动态的、不是先验可用的、并且随着时间的推移而日益暴露的情况下，对有限代理资源的最佳利用。当前在线优化方法的一个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变得有问题，在网络场景中，信息在代理之间分布。将所有数据传输到一个中心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。该研究将导致设计健壮的分布式策略，可以处理现实应用中存在的多种中断来源。研究方法从基于鞍点动力学的在线分布式算法的综合发展到具有可证明的正确保证的严格数学分析。可交付成果包括可证明正确的在线分布式策略目录、用于评估性能和复杂性的新概念和工具、通过计算机模拟的演示和验证、研究结果的文档以及工程学生教育。网络机器人系统在广泛的现代应用中具有巨大的影响，包括海洋勘探、灾难恢复、环境监测和监视。目前科学和军事领域的趋势是在通信受限、容易发生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强大而有效地运行。这项研究将被广泛传播，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，以及在STEM学科中保留少数民族。

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.