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

Ramtin Madani; Abdulrahman Kalbat; Javad Lavaei

doi:10.1109/CDC.2015.7403152

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 proceeding › Conference contribution

43 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 language	English
Title of host publication	54rd IEEE Conference on Decision and Control,CDC 2015
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	5932-5939
Number of pages	8
ISBN (Electronic)	9781479978861
DOIs	https://doi.org/10.1109/CDC.2015.7403152
Publication status	Published - Feb 8 2015
Externally published	Yes
Event	54th IEEE Conference on Decision and Control, CDC 2015 - Osaka, Japan Duration: Dec 15 2015 → Dec 18 2015

Publication series

Name	Proceedings of the IEEE Conference on Decision and Control
Volume	54rd IEEE Conference on Decision and Control,CDC 2015
ISSN (Print)	0743-1546
ISSN (Electronic)	2576-2370

Conference

Conference	54th IEEE Conference on Decision and Control, CDC 2015
Country/Territory	Japan
City	Osaka
Period	12/15/15 → 12/18/15

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

Access to Document

10.1109/CDC.2015.7403152

Cite this

Madani, R., Kalbat, A., & Lavaei, J. (2015). ADMM for sparse semidefinite programming with applications to optimal power flow problem. In 54rd IEEE Conference on Decision and Control,CDC 2015 (pp. 5932-5939). Article 7403152 (Proceedings of the IEEE Conference on Decision and Control; Vol. 54rd IEEE Conference on Decision and Control,CDC 2015). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CDC.2015.7403152

ADMM for sparse semidefinite programming with applications to optimal power flow problem. / Madani, Ramtin; Kalbat, Abdulrahman; Lavaei, Javad.
54rd IEEE Conference on Decision and Control,CDC 2015. Institute of Electrical and Electronics Engineers Inc., 2015. p. 5932-5939 7403152 (Proceedings of the IEEE Conference on Decision and Control; Vol. 54rd IEEE Conference on Decision and Control,CDC 2015).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Madani, R, Kalbat, A & Lavaei, J 2015, ADMM for sparse semidefinite programming with applications to optimal power flow problem. in 54rd IEEE Conference on Decision and Control,CDC 2015., 7403152, Proceedings of the IEEE Conference on Decision and Control, vol. 54rd IEEE Conference on Decision and Control,CDC 2015, Institute of Electrical and Electronics Engineers Inc., pp. 5932-5939, 54th IEEE Conference on Decision and Control, CDC 2015, Osaka, Japan, 12/15/15. https://doi.org/10.1109/CDC.2015.7403152

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

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

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