Entropy and Information Inequalities

Dear Colleagues,

In recent decades, information theoretic inequalities have provided an interface with both neighboring and seemingly disparate disciplines. What is more, bridges built from these interactions have produced new and richer understandings of Information theory itself. Important connections have been established between information theoretic inequalities and subjects including, convex geometry, optimal transport, concentration of measure, probability, statistics, estimation theory, additive combinatorics, and thermodynamics, by way of inequalities; entropy power, Brunn–Minkowski, HWI, log-Sobolev, monotonicity in CLT,  Sanov, sum-set, Landauer, and many more. Even within information theory, there has been renewed interest in developing inequalities in non-conventional settings such as convolution inequalities for Renyi or Tsallis entropy, inequalities for f-divergences, and entropy inequalities over discrete spaces.

In this Special Issue, we would like to invite contributions that establish novel information theoretic inequalities (broadly defined), extend the applications thereof, and deepen our understanding of information theory and related fields. Expository submissions are welcomed, and we envisage that these contributions will lead to an improvement of acumen in information theory, while also strengthening the growing bonds between the subject and the other areas outlined above, with the hope of generating further inter-field and interdisciplinary dialog.

Dr. James Melbourne
Dr. Varun Jog
Guest Editors

Manuscript Submission Information

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