CAREER: Cooperative Multi-Agent Optimization

该教师早期职业发展（CAREER）奖为有关优化这一共同主题的研究和教育活动提供资金。研究目标是为大规模分布式多智能体系统建立新的计算模型、理论进展和优化算法。令人感兴趣的是由具有不同性能标准的互连多个代理组成的系统。由于各种原因，例如私人或专有信息，代理人不共享自己的目标，但共享稀缺资源并希望合作实现共同目标。在没有中央协调器或中央信息访问的情况下，此类多智能体系统的协调和优化必须是分布式的。主要研究目标是开发和研究数学模型，以及设计和分析分布式多智能体算法。在分布式模型中，每个智能体在本地运行并与其邻居共享一些有限的信息，而智能体的连接性随时间动态变化。另一个目标是探索和量化算法在系统各种特征（例如存在通信噪声或延迟）下的性能限制。 算法开发需要一些基础研究，为系统性能的分析和表征提供新的数学工具。该研究与建立新的本科生和研究生优化课程的教育计划紧密结合，为学生提供有效、系统地识别、建模、分析和解决优化问题的知识。更广泛的教育目标包括提高女性和其他弱势群体对优化的兴趣，以及宣传和教育年轻人了解优化的重要性和美丽。研究活动的成功完成将带来大型网络系统的去中心化协调和优化算法的新有效设计。此外，它将导致针对一大类非线性非凸问题的具有保证性能的全局优化算法的设计。总的来说，结果将增强现有的优化知识。规划的教育活动将促进学生群体的优化并增强学生群体的多样性。

This Faculty Early Career Development (CAREER) award provides funds for research and education activities on a common theme of optimization. The research objective is to establish new computational models, theoretical advances, and optimization algorithms for large scale distributed multi-agent systems. Of interest are systems that consist of interconnected multiple agents with different performance criteria. For various reasons, such as private or proprietary information, the agents do not share their own objectives, but do share scarce resources and want to cooperatively achieve a common goal. In the absence of a central coordinator or central information access, the coordination and optimization of such multi-agent systems have to be distributed. A primary research objective is to develop and study mathematical models, and design and analyze distributed multi-agent algorithms. In the distributed model, each agent acts locally and shares some limited information with its neighbors while the agent connectivity is dynamically changing with time. Another objective is to explore and quantify the performance limits of the algorithms under various characteristics of the system, such as the presence of communication noise or delays. The algorithmic development necessitates some fundamental research providing new mathematical tools for analysis and characterization of the system performance. The research is closely tied with educational plans to build new undergraduate and graduate optimization courses to equip the students with the knowledge to recognize, model, analyze, and solve optimization problems efficiently and systematically. A broader educational goal includes promoting interest for women and other under-represented groups in optimization, as well as outreaching and educating young minds about the significance and beauty of optimization.Successful completion of the research activities will lead to new efficient designs of decentralized coordination and optimization algorithms for large network systems. Also, it will lead to the designs of global optimization algorithms with guaranteed performance for a large class of non-linear non-convex problems. Overall, the results will enhance the existing knowledge in optimization in general. The planned educational activities will promote optimization and enhance the diversity in the student population.