FR2.R4.1

Proving Information Inequalities by Gaussian Elimination

Laigang Guo, Beijing Normal University, China; Raymond Yeung, The Chinese University of Hong Kong, China; Xiao-Shan Gao, Chinese Academy of Sciences, China
Session:
Information Inequalities 1
Track:
9: Shannon Theory
Location:
Omikron II
Presentation Time:
Fri, 12 Jul, 11:30 - 11:50
Session Chair:
Lampros Gavalakis, Gustave Eiffel University
Abstract
The proof of information inequalities under linear constraints on the information measures is an important problem in information theory. For this purpose, ITIP and other variant algorithms have been developed and implemented, which are all based on solving a linear program (LP). Building on our recent work [13], we develop in this paper an enhanced approach for solving this problem.
Session FR2.R4
FR2.R4.1: Proving Information Inequalities by Gaussian Elimination
Laigang Guo, Beijing Normal University, China; Raymond Yeung, The Chinese University of Hong Kong, China; Xiao-Shan Gao, Chinese Academy of Sciences, China
FR2.R4.2: Properties of the Strong Data Processing Constant for Rényi Divergence
Lifu Jin, École Polytechnique Fédérale de Lausanne, Switzerland; Amedeo Roberto Esposito, Institute of Science and Technology Austria, Austria; Michael Gastpar, École Polytechnique Fédérale de Lausanne, Switzerland
FR2.R4.3: Self Improvement of the McEliece-Yu Inequality
Andrei Tanasescu, Pantelimon-George Popescu, University Politehnica of Bucharest, Romania
FR2.R4.4: A Poisson Decomposition for Information and the Information-Event Diagram
Cheuk Ting Li, The Chinese University of Hong Kong, Hong Kong SAR of China
Resources
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