Entropic proximal decomposition methods for convex programs and variational inequalities

Alfred Auslander, Marc Teboulle

Research output: Contribution to journalArticlepeer-review

Abstract

We consider convex optimization and variational inequality problems with a given separable structure. We propose a new decomposition method for these problems which combines the recent logarithmic-quadratic proximal theory introduced by the authors with a decomposition method given by Chen-Teboulle for convex problems with particular structure. The resulting method allows to produce for the first time provably convergent decomposition schemes based on C Lagrangians for solving convex structured problems. Under the only assumption that the primal-dual problems have nonempty solution sets, global convergence of the primal-dual sequences produced by the algorithm is established.

Original languageEnglish
Pages (from-to)33-47
Number of pages15
JournalMathematical Programming
Volume91
Issue number1
DOIs
StatePublished - Oct 2001

Keywords

  • Convex optimization
  • Decomposition methods
  • Entropic/interior proximal methods
  • Lagrangian multiplier methods
  • Variational inequalities

Fingerprint

Dive into the research topics of 'Entropic proximal decomposition methods for convex programs and variational inequalities'. Together they form a unique fingerprint.

Cite this