@inproceedings{606f95fc7952455f928d5fc2663e1c1a,

title = "Upper bounds on the relative entropy and R{\'e}nyi divergence as a function of total variation distance for finite alphabets",

abstract = "A new upper bound on the relative entropy is derived as a function of the total variation distance for probability measures defined on a common finite alphabet. The bound improves a previously reported bound by Csisz{\'a}r and Talata. It is further extended to an upper bound on the R{\'e}nyi divergence of an arbitrary non-negative order (including ∞) as a function of the total variation distance.",

keywords = "Pinsker's inequality, R{\'e}nyi divergence, relative entropy, relative information, total variation distance",

author = "Igal Sason and Sergio Verdu",

note = "Funding Information: ACKNOWLEDGMENT The work of I. Sason has been supported by the Israeli Science Foundation (ISF) under Grant 12/12, and the work of S. Verdu has been supported by the US National Science Foundation under Grant CCF-1016625, and in part by the Center for Science of Information, an NSF Science and Technology Center under Grant CCF-0939370. Publisher Copyright: {\textcopyright} 2015 IEEE.; IEEE Information Theory Workshop, ITW 2015 ; Conference date: 11-10-2015 Through 15-10-2015",

year = "2015",

month = dec,

day = "17",

doi = "10.1109/ITWF.2015.7360766",

language = "English (US)",

series = "ITW 2015 - 2015 IEEE Information Theory Workshop",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "214--218",

booktitle = "ITW 2015 - 2015 IEEE Information Theory Workshop",

address = "United States",

}