By Topic

Euclidean Information Theory

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

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