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Cooperative Multi-agent Optimization, National Science Foundation (NSF) Career Grant in Operations Research 2008

Angelia Nedić


This grant 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 the 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.

Research in Distributed Optimization and Duality

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