[CCoE Notice] SEMINAR 9/5 10am W205 Engineering building iI

[Zhu Han]

Decentralized Jointly Sparse Optimization by Reweighted Lq Minimization

Qing Ling, Professor, University of Science and Technology of China
Host: Zhu Han
Location:  Engineering Building II, W205
Time: 9/5/12, Wednesday, 10am.


 
Abstract: A set of vectors (or signals) are jointly sparse if their nonzero entries are commonly supported on a small subset of locations. Consider a network of agents which collaborative recover a set of joint sparse vectors. This talk proposes novel decentralized algorithms to recover these vectors in a way that every agent runs a recovery algorithm while neighbor agents exchange only their estimates of the joint support but not their data. The agents will obtain their solutions by taking advantages of the joint sparse structure while keeping their data private. As such, the proposed approach finds applications in distributed (compressive) sensing, decentralized event detection, as well as collaborative data mining. We use a non-convex minimization model and propose algorithms that alternate between support estimate consensus and signal estimate update. The latter step is based on reweighted Lq iterations, where q can be 1 or 2. We numerically compare the proposed decentralized algorithms with existing centralized and decentralized algorithms. Simulation results demonstrate that the proposed decentralized approaches have strong recovery performance and converge reasonably fast.
 
Bio: Qing Ling received the B.S. degree in automation and the Ph.D. degree in control theory and control engineering from University of Science and Technology of China, Hefei, Anhui, China, in 2001 and 2006, respectively. From 2006 to 2009, he was a postdoctoral research fellow with the Department of Electrical and Computer Engineering, Michigan Technological University, Houghton, MI, USA. Since 2009 he has been an assistant professor with the Department of Automation, University of Science and Technology of China. His current research focuses on decentralized optimization of networked multi-agent systems.
 

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