A class of distributed optimization methods with event-triggered communication

Martin Meinel, Michael Ulbrich, Sebastian Albrecht

Research output: Contribution to journalArticlepeer-review

19 Scopus citations

Abstract

We present a class of methods for distributed optimization with event-triggered communication. To this end, we extend Nesterov's first order scheme to use event-triggered communication in a networked environment. We then apply this approach to generalize the proximal center algorithm (PCA) for separable convex programs by Necoara and Suykens. Our method uses dual decomposition and applies the developed event-triggered version of Nesterov's scheme to update the dual multipliers. The approach is shown to be well suited for solving the active optimal power flow (DC-OPF) problem in parallel with event-triggered and local communication. Numerical results for the IEEE 57 bus and IEEE 118 bus test cases confirm that approximate solutions can be obtained with significantly less communication while satisfying the same accuracy estimates as solutions computed without event-triggered communication.

Original languageEnglish
Pages (from-to)517-553
Number of pages37
JournalComputational Optimization and Applications
Volume57
Issue number3
DOIs
StatePublished - Apr 2014

Keywords

  • Convex optimization
  • DC-OPF problem
  • Distributed optimization
  • Distributed regularization
  • Dual decomposition
  • Event-triggered communication
  • IEEE test cases
  • Local communication
  • Non-differentiable optimization

Fingerprint

Dive into the research topics of 'A class of distributed optimization methods with event-triggered communication'. Together they form a unique fingerprint.

Cite this