Gossip over holonomic graphs

A gossip process is an iterative process in a multi-agent system where only two neighboring agents communicate at each iteration and update their states. The neighboring condition is by convention described by an undirected graph. In this paper, we consider a general update rule whereby each agent takes an arbitrary weighted average of its and its neighbor’s current states. In general, the limit of the gossip process (if it converges) depends on the order of iterations of the gossiping pairs. The main contribution of the paper is to provide a necessary and sufficient condition for convergence of the gossip process that is independent of the order of iterations. This result relies on the introduction of the novel notion of holonomy of local stochastic matrices for the communication graph. We also provide complete characterizations of the limit and the space of holonomic stochastic matrices over the graph.

[1]  John N. Tsitsiklis,et al.  A new condition for convergence in continuous-time consensus seeking systems , 2011, IEEE Conference on Decision and Control and European Control Conference.

[2]  Luc Moreau,et al.  Stability of multiagent systems with time-dependent communication links , 2005, IEEE Transactions on Automatic Control.

[3]  Tamer Basar,et al.  Cluster Consensus with Point Group Symmetries , 2017, SIAM J. Control. Optim..

[4]  Richard M. Murray,et al.  Consensus problems in networks of agents with switching topology and time-delays , 2004, IEEE Transactions on Automatic Control.

[5]  Stephen P. Boyd,et al.  Randomized gossip algorithms , 2006, IEEE Transactions on Information Theory.

[6]  E. Seneta,et al.  Towards consensus: some convergence theorems on repeated averaging , 1977, Journal of Applied Probability.

[7]  Angelia Nedic,et al.  On Convergence Rate of Weighted-Averaging Dynamics for Consensus Problems , 2017, IEEE Transactions on Automatic Control.

[8]  M. Degroot Reaching a Consensus , 1974 .

[9]  Behrouz Touri,et al.  Product of Random Stochastic Matrices , 2011, IEEE Transactions on Automatic Control.

[10]  Charles R. Johnson,et al.  Matrix analysis , 1985, Statistical Inference for Engineers and Data Scientists.

[11]  M. Belabbas,et al.  Triangulated Laman graphs, local stochastic matrices, and limits of their products , 2020, 2011.00746.

[12]  Brian D. O. Anderson,et al.  Reaching a Consensus in a Dynamically Changing Environment: Convergence Rates, Measurement Delays, and Asynchronous Events , 2008, SIAM J. Control. Optim..

[13]  M. Bartlett,et al.  Weak ergodicity in non-homogeneous Markov chains , 1958, Mathematical Proceedings of the Cambridge Philosophical Society.

[14]  Brian D. O. Anderson,et al.  Reaching a Consensus in a Dynamically Changing Environment: Convergence Rates, Measurement Delays, and Asynchronous Events , 2008, SIAM J. Control. Optim..

[15]  Randal W. Beard,et al.  Consensus seeking in multiagent systems under dynamically changing interaction topologies , 2005, IEEE Transactions on Automatic Control.

[16]  Shaoshuai Mou,et al.  Deterministic Gossiping , 2011, Proceedings of the IEEE.

[17]  J. Wolfowitz Products of indecomposable, aperiodic, stochastic matrices , 1963 .

[18]  Jie Lin,et al.  Coordination of groups of mobile autonomous agents using nearest neighbor rules , 2003, IEEE Trans. Autom. Control..

[19]  Tamer Basar,et al.  Distributed averaging with linear objective maps , 2016, Autom..

[20]  E. Seneta Non-negative Matrices and Markov Chains , 2008 .

[21]  Vincent D. Blondel,et al.  How to Decide Consensus? A Combinatorial Necessary and Sufficient Condition and a Proof that Consensus is Decidable but NP-Hard , 2012, SIAM J. Control. Optim..

[22]  Shaoshuai Mou,et al.  Distributed Averaging Using Periodic Gossiping , 2017, IEEE Transactions on Automatic Control.

[23]  John N. Tsitsiklis,et al.  Problems in decentralized decision making and computation , 1984 .