Rate of convergence analysis of dual-based variables decomposition methods for strongly convex problems

Amir Beck*, Luba Tetruashvili, Yakov Vaisbourd, Ariel Shemtov

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

We consider the problem of minimizing the sum of a strongly convex function and a term comprising the sum of extended real-valued proper closed convex functions. We derive the primal representation of dual-based block descent methods and establish a relation between primal and dual rates of convergence, allowing to compute the efficiency estimates of different methods. We illustrate the effectiveness of the methods by numerical experiments on total variation-based denoising problems.

Original languageEnglish
Pages (from-to)61-66
Number of pages6
JournalOperations Research Letters
Volume44
Issue number1
DOIs
StatePublished - Jan 2016
Externally publishedYes

Keywords

  • Block variables decomposition
  • Dual based methods
  • Total variation denoising

Fingerprint

Dive into the research topics of 'Rate of convergence analysis of dual-based variables decomposition methods for strongly convex problems'. Together they form a unique fingerprint.

Cite this