Minimax Rényi Redundancy

Semih Yagli, Yucel Altug, Sergio Verdu

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


The redundancy for universal lossless compression of discrete memoryless sources in Campbell's setting is characterized as a minimax Rényi divergence, which is shown to be equal to the maximal \alpha -mutual information via a generalized redundancy-capacity theorem. Special attention is placed on the analysis of the asymptotics of minimax Rényi divergence, which is determined up to a term vanishing in blocklength.

Original languageEnglish (US)
Pages (from-to)3715-3733
Number of pages19
JournalIEEE Transactions on Information Theory
Issue number5
StatePublished - May 2018

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Computer Science Applications
  • Library and Information Sciences


  • Jeffreys' prior
  • Rényi divergence
  • Universal lossless compression
  • generalized redundancy-capacity theorem
  • minimax redundancy
  • minimax regret
  • risk aversion
  • α-mutual information


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