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Entropy 2019, 21(3), 276; https://doi.org/10.3390/e21030276

A Monotone Path Proof of an Extremal Result for Long Markov Chains

1,*
and
2,*
1
Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
2
Department of Electrical and Computer Engineering, McMaster University, Hamilton, ON L8S 4K1, Canada
*
Authors to whom correspondence should be addressed.
Received: 31 January 2019 / Revised: 5 March 2019 / Accepted: 11 March 2019 / Published: 13 March 2019
(This article belongs to the Special Issue Multiuser Information Theory II)
Full-Text   |   PDF [271 KB, uploaded 13 March 2019]   |  

Abstract

We prove an extremal result for long Markov chains based on the monotone path argument, generalizing an earlier work by Courtade and Jiao. View Full-Text
Keywords: entropy power inequality; Karush–Kuhn–Tucker; Markov chain; mean squared error; semidefinite programming entropy power inequality; Karush–Kuhn–Tucker; Markov chain; mean squared error; semidefinite programming
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Wang, J.; Chen, J. A Monotone Path Proof of an Extremal Result for Long Markov Chains. Entropy 2019, 21, 276.

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