A Proximal Multiplier Method for Convex Separable Symmetric Cone Optimization

Erik Alex Papa Quiroz, Julio López Luis, Miguel Cano Lengua

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

1 Scopus citations

Abstract

This work is devoted to the study of a proximal decomposition algorithm for solving convex symmetric cone optimization with separable structures. The algorithm considered is based on a decomposition method and proximal distances. Under suitable assumptions, we prove that each limit point of the primal-dual sequences generated by the algorithm solves the problem. Finally, the global convergence is established.

Original languageEnglish
Title of host publicationProceedings of the 2020 5th International Conference on Multimedia Systems and Signal Processing, ICMSSP 2020
PublisherAssociation for Computing Machinery
Pages92-97
Number of pages6
ISBN (Electronic)9781450377485
DOIs
StatePublished - 28 May 2020
Event5th International Conference on Multimedia Systems and Signal Processing, ICMSSP 2020 - Chengdu, China
Duration: 28 May 202030 May 2020

Publication series

NameACM International Conference Proceeding Series

Conference

Conference5th International Conference on Multimedia Systems and Signal Processing, ICMSSP 2020
Country/TerritoryChina
CityChengdu
Period28/05/2030/05/20

Bibliographical note

Publisher Copyright:
© 2020 ACM.

Keywords

  • Descomposition method
  • Euclidean Jordan algebra
  • Proximal distance
  • Symmetric cone optimization

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