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Euclidean Information Theory

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2 Author(s)
Borade, S. ; EECS, MIT Cambridge, Cambridge, MA ; Lizhong Zheng

Many problems in information theory involve optimizing the Kullback-Leibler (KL) divergence between probability distributions. Since KL divergence is difficult to analyze, these optimizations are often intractable. We simplify these problems by assuming the distributions of interest to be close to each other. Under this assumption, the KL divergence behaves like a squared Euclidean distance. With this simplification, we solve the open problem of broadcasting with degraded message sets, as a canonical example of network information theory problems.

Published in:

Communications, 2008 IEEE International Zurich Seminar on

Date of Conference:

12-14 March 2008

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