Talks and Poster Presentations (with Proceedings-Entry):
O. Sluciak, M. Rupp:
"Almost Sure Convergence Of Consensus Algorithms By Relaxed Projection Mappings";
Poster: IEEE-SP Workshop on Statistical Signal Processing (SSP),
Ann Arbor, MI, USA;
- 08-08-2012; in: "Proceedings of the IEEE Statistical Signal Processing Workshop",
In this contribution we present a stronger notion of almost
sure convergence for a large class of consensus algorithms including
also asynchronous updates. We introduce the concept
of the so-called relaxed projection algorithms and show that
many consensus algorithms can be interpreted as such relaxed
projection updates. It is well known that such algorithms converge
to a solution lying in the intersection of the projections.
The convergence of such algorithms is, however, guaranteed
only for deterministic ordering of the projections. Since we
are interested in random data exchanges, we analyze the convergence
in case of random orderings of the projections and
show that the algorithms converge in the underrelaxed case
even for time-varying and individual mixing parameters.
relaxed projection algorithm, average consensus, asynchronous updates, wireless sensor network
Electronic version of the publication:
Created from the Publication Database of the Vienna University of Technology.