Generalized Information Inequalities via Submodularity, and Two Combinatorial Problems
Gunank Jakhar, Gowtham R. Kurri, Suryajith Chillara, Vinod M. Prabhakaran

TL;DR
This paper extends information inequalities linked to submodularity, introduces a new Loomis-Whitney-type inequality, and explores an extremal graph theory problem, unifying and advancing theoretical frameworks in these areas.
Contribution
It provides convex-functional generalizations of existing inequalities, derives a novel projection inequality, and extends extremal graph theory results using submodular functions.
Findings
New Loomis-Whitney-type projection inequality for finite point sets.
Convex-functional generalizations of Madiman-Tetali inequalities.
Extended extremal graph theory results using Shearer's lemma.
Abstract
It is well known that there is a strong connection between entropy inequalities and submodularity, since the entropy of a collection of random variables is a submodular function. Unifying frameworks for information inequalities arising from submodularity were developed by Madiman and Tetali (2010) and Sason (2022). Madiman and Tetali (2010) established strong and weak fractional inequalities that subsume classical results such as Han's inequality and Shearer's lemma. Sason (2022) introduced a convex-functional framework for generalizing Han's inequality, and derived unified inequalities for submodular and supermodular functions. In this work, we build on these frameworks and make three contributions. First, we establish convex-functional generalizations of the strong and weak Madiman and Tetali inequalities for submodular functions. Second, using a special case of the strong…
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Taxonomy
TopicsComplexity and Algorithms in Graphs · Wireless Communication Security Techniques · Radar Systems and Signal Processing
