Parallel and Distributed Computation: Numerical Methods
Parallel and Distributed Computation: Numerical Methods
Introduction to Stochastic Search and Optimization
Introduction to Stochastic Search and Optimization
Distributed average consensus with least-mean-square deviation
Journal of Parallel and Distributed Computing
Brief paper: Distributed nonlinear control algorithms for network consensus
Automatica (Journal of IFAC)
Optimal consensus algorithms for cooperative team of agents subject to partial information
Automatica (Journal of IFAC)
ACC'09 Proceedings of the 2009 conference on American Control Conference
Optimal consensus seeking in a network of multiagent systems: an LMI approach
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Optimal linear-consensus algorithms: an LQR perspective
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on game theory
IEEE Transactions on Information Theory
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A new optimal distributed linear averaging (ODLA) problem is presented in this paper. This problem is motivated by the distributed averaging problem which arises in the context of distributed algorithms in computer science and coordination of groups of autonomous agents in engineering. The aim of the ODLA problem is to compute the average of the initial values at nodes of a graph through an optimal distributed algorithm in which the nodes in the graph can only communicate with their neighbors. Optimality is given by a minimization problem of a quadratic cost functional under infinite horizon. We show that this problem has a very close relationship with the notion of semistability. By developing new necessary and sufficient conditions for semistability of linear discrete-time systems, we convert the proposed ODLA problem into an equivalent, constrained optimization problem and then derive a solvable, fixed-structure convex optimization problem.