ABSTRACT:

Optimization problems arising in decentralized multi-agent systems have gained significant attention in the context of cyber-physical, communication, power, and robotic networks combined with privacy preservation, distributed data mining and processing issues. The distributed nature of the problems is inherent due to partial knowledge of the problem data (i.e., a portion of the cost function or a subset of the constraints is known to different entities in the system), which necessitates costly communications among neighboring agents. In this talk, we present a new class of decentralized first-order methods for nonsmooth and stochastic optimization problems which can significantly reduce the number of inter-node communications. Our major contribution is the development of decentralized communication sliding methods, which can skip inter-node communications while agents solve the primal subproblems iteratively through linearizations of their local objective functions. 

Bio

Soomin Lee is a postdoc fellow in industrial and systems engineering at Georgia Tech. She received her Ph.D. in Electrical and Computer Engineering (2013) and Master's in Computer Science (2012) from the University of Illinois, Urbana-Champaign. After graduation, she joined in Duke Robotics Group as a postdoc associate. In 2009, she worked as an assistant research officer at the Advanced Digital Science Center (ADSC) in Singapore. She is a recipient of the NSF fellowship program for enhancing partnership with industry. Her research interests include control and optimization of various distributed engineering systems interconnected over complex networks, large-scale machine learning for big data analytics as well as theoretical optimization.