A proximal multiplier method for separable convex minimization

O. Sarmiento, E. A. Papa Quiroz, P. R. Oliveira

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

In this paper, we propose an inexact proximal multiplier method using proximal distances for solving convex minimization problems with a separable structure. The proposed method unifies the works of Chen and Teboulle (PCPM method), Kyono and Fukushima (NPCPMM) and Auslender and Teboulle (EPDM), and extends the convergence properties for the class of (Formula presented.) -divergence distances. We prove, under standard assumptions, that the iterations generated by the method are well defined and the sequence converges to an optimal solution of the problem.

Original languageEnglish
Pages (from-to)501-537
Number of pages37
JournalOptimization
Volume65
Issue number2
DOIs
StatePublished - 1 Feb 2016

Bibliographical note

Funding Information:
The research of the first and second authors was supported by CAPES (Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior) and the PosDoctoral Scolarchip CAPES-FAPERJ Edital PAPD-2011, respectively.

Publisher Copyright:
© 2015 Taylor & Francis.

Copyright:
Copyright 2016 Elsevier B.V., All rights reserved.

Keywords

  • convex functions
  • proximal distances
  • proximal multiplier methods
  • separable convex problems

Fingerprint Dive into the research topics of 'A proximal multiplier method for separable convex minimization'. Together they form a unique fingerprint.

Cite this