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[Academic Lecture] To Prove or to Disprove: Information Inequalities and Sparse Optimization
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To Prove or to Disprove: Information Inequalities and Sparse Optimization


     Who:      Prof. Chee Wei Tan,The City University of Hong Kong

   When:     25/07/2016, Monday, 10:00 am

  Where:     The Fourth-floor Meeting Room, NO. 1 SEIEE Building

                   Shanghai Jiao Tong University



To prove or to disprove an information inequality is a crucial step in the converse theorems in information theory. When the problem size scales up, this is a computationally difficult task. We show how the framework of linear information inequalities proposed by Raymond Yeung in 1997 and recent ideas from sparse optimization theory can be used to explicitly construct the shortest proof of an information inequality or the smallest counterexample to disprove it if the inequality is not true in general. To scale up this scientific computing tasks, we also talk about how to bid for cloud resources to automate and scale up computation by cloud computing software-as-a-service. This is based on work published in IEEE ISIT 2014 and ACM SIGCOMM 2015.



Chee Wei Tan received the M.A. and Ph.D. degrees in electrical engineering from Princeton University. He is an Associate Professor with the City University of Hong Kong. Previously, he was a Postdoctoral Scholar at the California Institute of Technology (Caltech) and was a Visiting Faculty with Qualcomm R&D, San Diego in 2011. His research interests are in networks and cloud computing, statistical inference in online data analytics, and optimization theory and its applications. Dr. Tan currently serves as an Editor for both the IEEE Transactions on Communications and the IEEE/ACM Transactions on Networking.





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