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SIAM Chapter Seminar

Yaoqing Yang
Carnegie Melllon University
Title: "Compressive Computing" in Data Processing

Abstract: When we process a big amount of data in a distributed network, we may consider using “compressive computing” to reduce the required in-network communication. This idea is a natural generalization of Shannon's source compression theory to distributed computing. In particular, we consider the problem of linear function computation in a tree network. We examine two cases: data aggregation (only one sink node computes a function of distributed data) and consensus (all nodes compute the same function). In both cases, we provide upper and lower bounds on the overall communication cost, which meet with each other in the zero distortion limit.

Date: Thursday, October 22, 2015
Time: 5:30 pm
Location: Wean Hall 8220
Submitted by:  Samuel Cohn