Introduction to distributed algorithms
Introduction to distributed algorithms
Distributed Algorithms
Parallel and Distributed Computation: Numerical Methods
Parallel and Distributed Computation: Numerical Methods
Local Divergence of Markov Chains and the Analysis of Iterative Load-Balancing Schemes
FOCS '98 Proceedings of the 39th Annual Symposium on Foundations of Computer Science
IEEE/ACM Transactions on Networking (TON) - Special issue on networking and information theory
Automatica (Journal of IFAC)
Communication constraints in the average consensus problem
Automatica (Journal of IFAC)
On quantized consensus by means of gossip algorithm: part ii: convergence time
ACC'09 Proceedings of the 2009 conference on American Control Conference
Real-valued average consensus over noisy quantized channels
ACC'09 Proceedings of the 2009 conference on American Control Conference
Gossip consensus algorithms via quantized communication
Automatica (Journal of IFAC)
On quantized consensus by means of gossip algorithm: part i: convergence proof
ACC'09 Proceedings of the 2009 conference on American Control Conference
On quantized consensus by means of gossip algorithm: part ii: convergence time
ACC'09 Proceedings of the 2009 conference on American Control Conference
Brief paper: Quantized consensus in Hamiltonian graphs
Automatica (Journal of IFAC)
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This paper deals with the distributed averaging problem over a connected network of agents, subject to a quantization constraint. It is assumed that at each time update, only a pair of agents can update their own numbers in terms of the quantized data being exchanged. The agents are also required to communicate with one another in a stochastic fashion. In the first part of the paper, it was shown that the quantized consensus is reached by means of a stochastic gossip algorithm proposed in a recent paper, for any arbitrary quantization. The current part of the paper considers the expected value of the time at which the quantized consensus is reached. This quantity (corresponding to the worst case) is lower and upper bounded in terms of the topology of the graph, for uniform quantization. In particular, it is shown that the upper bound is related to the principal minors of the weighted Laplacian matrix. A convex optimization is also proposed to determine the set of probabilities (used to pick a pair of agents) which leads to the fast convergence of the gossip algorithm.