The CoMirror algorithm for solving nonsmooth constrained convex problems

Amir Beck, Aharon Ben-Tal, Nili Guttmann-Beck, Luba Tetruashvili

Research output: Contribution to journalArticlepeer-review

Abstract

We introduce a first-order Mirror-Descent (MD) type algorithm for solving nondifferentiable convex problems having a combination of simple constraint set X (ball, simplex, etc.) and an additional functional constraint. The method is tuned to exploit the structure of X by employing an appropriate non-Euclidean distance-like function. Convergence results and efficiency estimates are derived. The performance of the algorithm is demonstrated by solving certain image deblurring problems.

Original languageEnglish
Pages (from-to)493-498
Number of pages6
JournalOperations Research Letters
Volume38
Issue number6
DOIs
StatePublished - Nov 2010
Externally publishedYes

Keywords

  • Convex optimization
  • Gradient-based methods
  • Mirror Descent
  • Non-Euclidean projection

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