Abstract
We present a versatile automated theorem proving framework capable of automated proofs of outer bounds in network information theory, automated discovery of inner bounds in network information theory (in conjunction with the method by Lee and Chung), simplification of capacity regions involving auxiliary random variables, automated deduction of properties of information-theoretic quantities (e.g. Wyner and Gács-Körner common information), and automated discovery of non-Shannon-type inequalities, under a unified framework. Our method is based on the linear programming approach for proving Shannon-type information inequalities by Yeung and Zhang, together with a novel pruning method for searching auxiliary random variables. We introduce the concept of existential information inequalities, which provides an axiomatic framework for a wide range of problems in information theory. A full version of this paper is accessible at: https://arxiv.org/pdf/2101.12370.pdf
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
