Distributed Semidefinite Programming with Application to Large-scale System Analysis

Sina Khoshfetrat Pakazad*, Anders Hansson, Martin S. Andersen, Anders Rantzer

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

74 Downloads (Pure)

Abstract

Distributed algorithms for solving coupled semidefinite programs (SDPs) commonly require many iterations to converge. They also put high computational demand on the computational agents. In this paper we show that in case the coupled problem has an inherent tree structure, it is possible to devise an efficient distributed algorithm for solving such problems. The proposed algorithm relies on predictor-corrector primal-dual interior-point methods, where we use a message-passing algorithm to compute the search directions distributedly. Message-passing here is closely related to dynamic programming over trees. This allows us to compute the exact search directions in a finite number of steps. This is because, computing the search directions requires a recursion over the tree structure and hence, terminates after an upward and downward pass through the tree. Furthermore this number can be computed apriori and only depends on the coupling structure of the problem. We use the proposed algorithm for analyzing robustness of large-scale uncertain systems distributedly. We test the performance of this algorithm using numerical examples.
Original languageEnglish
JournalI E E E Transactions on Automatic Control
Volume63
Issue number4
Pages (from-to)1045 - 1058
ISSN0018-9286
DOIs
Publication statusPublished - 2017

Keywords

  • Distributed algorithms
  • Interconnected uncertain systems
  • Primal-dual methods
  • Robustness analysis
  • Semidefinite programs (SDPs)

Fingerprint

Dive into the research topics of 'Distributed Semidefinite Programming with Application to Large-scale System Analysis'. Together they form a unique fingerprint.

Cite this