ADMM for sparse semidefinite programming with applications to optimal power flow problem

Ramtin Madani, Abdulrahman Kalbat, Javad Lavaei

Research output: Chapter in Book/Report/Conference proceedingConference contribution

37 Citations (Scopus)

Abstract

This paper designs a distributed algorithm for solving sparse semidefinite programming (SDP) problems, based on the alternating direction method of multipliers (ADMM). It is known that exploiting the sparsity of a large-scale SDP problem leads to a decomposed formulation with a lower computational cost. The algorithm proposed in this work solves the decomposed formulation of the SDP problem using an ADMM scheme whose iterations consist of two subproblems. Both subproblems are highly parallelizable and enjoy closed-form solutions, which make the iterations computationally very cheap. The developed numerical algorithm is also applied to the SDP relaxation of the optimal power flow (OPF) problem, and tested on the IEEE benchmark systems.

Original languageEnglish
Title of host publication54rd IEEE Conference on Decision and Control,CDC 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5932-5939
Number of pages8
ISBN (Electronic)9781479978861
DOIs
Publication statusPublished - Feb 8 2015
Externally publishedYes
Event54th IEEE Conference on Decision and Control, CDC 2015 - Osaka, Japan
Duration: Dec 15 2015Dec 18 2015

Publication series

NameProceedings of the IEEE Conference on Decision and Control
Volume54rd IEEE Conference on Decision and Control,CDC 2015
ISSN (Print)0743-1546
ISSN (Electronic)2576-2370

Conference

Conference54th IEEE Conference on Decision and Control, CDC 2015
Country/TerritoryJapan
CityOsaka
Period12/15/1512/18/15

Keywords

  • Algorithm design and analysis
  • Heuristic algorithms
  • Matrix decomposition
  • Optimization
  • Programming
  • Sparse matrices
  • Symmetric matrices

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Modelling and Simulation
  • Control and Optimization

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