Optimum Intrinsic Randomness Rate with Respect to f -Divergences Using the Smooth Min Entropy

Ryo Nomura, Hideki Yagi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The intrinsic randomness (IR) problem is considered for general setting. In the literature, the optimum IR rate with respect to the variational distance has been characterized in two ways. One is based on the information spectrum quantity and the other is based on the smooth Rényi entropy. Recently, Nomura has revealed the optimum IR rate with respect to f-divergences, which includes the variational distance, the Kullback-Leibler (KL) divergence and so on, by using the informational spectrum quantity. In this paper, we try to characterize the optimum IR rate with respect to a subclass of f-divergences by using the smooth Min entropy. The subclass of f-divergences considered in this paper includes typical distance measures such as the total variational distance, the KL divergence, the Hellinger distance and so on.

Original languageEnglish
Title of host publication2021 IEEE International Symposium on Information Theory, ISIT 2021 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1784-1789
Number of pages6
ISBN (Electronic)9781538682098
DOIs
Publication statusPublished - 2021 Jul 12
Event2021 IEEE International Symposium on Information Theory, ISIT 2021 - Virtual, Melbourne, Australia
Duration: 2021 Jul 122021 Jul 20

Publication series

NameIEEE International Symposium on Information Theory - Proceedings
Volume2021-July
ISSN (Print)2157-8095

Conference

Conference2021 IEEE International Symposium on Information Theory, ISIT 2021
Country/TerritoryAustralia
CityVirtual, Melbourne
Period21/7/1221/7/20

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Information Systems
  • Modelling and Simulation
  • Applied Mathematics

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