Rate-Distortion in Non-Convex Families

Hila Ratson, Ram Zamir

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Iterative constrained optimization often requires convexity conditions about the argument set in order to converge to the global optimum. One such instant is the parametric version of the Blahut algorithm for rate-distortion function computation. However, there are many interesting cases for which the parametric set is not convex, e.g a discrete reproduction alphabet at unknown (parametric) locations for a continuous source. In this paper we show examples of non-convex families for which the parametric Blahut algorithm does not converge to the global optimum, and suggest a combined parametric Blahut and random annealing (A) method to overcome this problem.

Original languageEnglish
Title of host publication2023 IEEE Information Theory Workshop, ITW 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages5
ISBN (Electronic)9798350301496
StatePublished - 2023
Event2023 IEEE Information Theory Workshop, ITW 2023 - Saint-Malo, France
Duration: 23 Apr 202328 Apr 2023

Publication series

Name2023 IEEE Information Theory Workshop, ITW 2023


Conference2023 IEEE Information Theory Workshop, ITW 2023

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Computer Networks and Communications
  • Signal Processing
  • Control and Optimization


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