Computational Optimization and Applications

, Volume 57, Issue 3, pp 517–553

A class of distributed optimization methods with event-triggered communication

  • Martin Meinel
  • Michael Ulbrich
  • Sebastian Albrecht


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.


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


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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Martin Meinel
    • 1
  • Michael Ulbrich
    • 1
  • Sebastian Albrecht
    • 1
  1. 1.Lehrstuhl für Mathematische Optimierung, Fakultät für MathematikTechnische Universität MünchenGarching b. MünchenGermany

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