Distributed solver for linear matrix inequalities: an optimization perspective

Weijian Li, Wen Deng*, Xianlin Zeng, Yiguang Hong

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

Abstract

In this paper, we develop a distributed solver for a group of strict (non-strict) linear matrix inequalities over a multi-agent network, where each agent only knows one inequality, and all agents co-operate to reach a consensus solution in the intersection of all the feasible regions. The formulation is transformed into a distributed optimization problem by introducing slack variables and consensus constraints. Then, by the primal–dual methods, a distributed algorithm is proposed with the help of projection operators and derivative feedback. Finally, the convergence of the algorithm is analyzed, followed by illustrative simulations.

Original languageEnglish
Pages (from-to)507-515
Number of pages9
JournalControl Theory and Technology
Volume19
Issue number4
DOIs
Publication statusPublished - Nov 2021

Keywords

  • Distributed computation
  • Distributed optimization
  • Linear matrix inequalities
  • Primal–dual method

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