---
res:
  bibo_abstract:
  - Strong data processing inequalities (SDPI) are an important object of study in
    Information Theory and have been well studied for f -divergences. Universal upper
    and lower bounds have been provided along with several applications, connecting
    them to impossibility (converse) results, concentration of measure, hypercontractivity,
    and so on. In this paper, we study Renyi divergence and the corresponding SDPI
    constant whose behavior seems to deviate from that of ordinary <1>-divergences.
    In particular, one can find examples showing that the universal upper bound relating
    its SDPI constant to the one of Total Variation does not hold in general. In this
    work, we prove, however, that the universal lower bound involving the SDPI constant
    of the Chi-square divergence does indeed hold. Furthermore, we also provide a
    characterization of the distribution that achieves the supremum when is equal
    to 2 and consequently compute the SDPI constant for Renyi divergence of the general
    binary channel.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Lifu
      foaf_name: Jin, Lifu
      foaf_surname: Jin
  - foaf_Person:
      foaf_givenName: Amedeo Roberto
      foaf_name: Esposito, Amedeo Roberto
      foaf_surname: Esposito
      foaf_workInfoHomepage: http://www.librecat.org/personId=9583e921-e1ad-11ec-9862-cef099626dc9
  - foaf_Person:
      foaf_givenName: Michael
      foaf_name: Gastpar, Michael
      foaf_surname: Gastpar
  bibo_doi: 10.1109/ISIT57864.2024.10619367
  dct_date: 2024^xs_gYear
  dct_identifier:
  - UT:001304426903055
  dct_isPartOf:
  - http://id.crossref.org/issn/2157-8095
  - http://id.crossref.org/issn/9798350382846
  dct_language: eng
  dct_publisher: IEEE@
  dct_title: Properties of the strong data processing constant for Rényi divergence@
...