Distributed consensus-based solver for semi-definite programming: An optimization viewpoint

Weijian Li, Xianlin Zeng, Yiguang Hong*, Haibo Ji

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

Abstract

This paper aims at the distributed computation for semi-definite programming (SDP) problems over multi-agent networks. Two SDP problems, including a non-sparse case and a sparse case, are transformed into distributed optimization problems, respectively, by fully exploiting their structures and introducing consensus constraints. Inspired by primal–dual and consensus methods, we propose two distributed algorithms for the two cases with the help of projection and derivative feedback techniques. Furthermore, we prove that the algorithms converge to their optimal solutions, and moreover, their convergences rates are evaluated by the duality gap.

Original languageEnglish
Article number109737
JournalAutomatica
Volume131
DOIs
Publication statusPublished - Sept 2021

Keywords

  • Consensus-based algorithm
  • Distributed optimization
  • Semi-definite programming
  • Sparsity

Fingerprint

Dive into the research topics of 'Distributed consensus-based solver for semi-definite programming: An optimization viewpoint'. Together they form a unique fingerprint.

Cite this