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This paper presents novel constrained consensus least mean square (cLMS) algorithms with adjustable constraints that can improve the learning performance of distributed estimation problems in sensor networks by exploiting the spatial diversity of the estimates. For the first algorithm, the constraint vectors are adjusted by combining the components of the estimate orthogonal to its neighbor estimates...
This paper considers distributed multi-agents optimization problems where agents collaborate to minimize the sum of locally known convex functions. We focus on the case when the communication between agents is described by a directed graph. The proposed algorithm achieves the best known rate of convergence for this class of problems, O(μk) for 0 < μ < 1, given that the objective functions are...
Distributed Gradient Descent (DGD) is a well established algorithm to solve the minimization of a sum of multi-agents' objective functions in the network, with the assumption that the network is undirected, i.e., requiring the weight matrices to be doubly-stochastic. In this paper, we present a distributed algorithm, called Directed-Distributed Gradient Descent (D-DGD), to solve the same problem over...
Scheduling of storage devices in microgrids with multiple renewable energy resources is crucial for their optimal and reliable operation. With proper scheduling, the storage devices can capture the energy when the renewable generation is high and utility energy price is low, and release it when the demand is high or utility energy price is expensive. This scheduling is a multi-step optimization problem...
The ability to recover a low-rank matrix from a subset of its entries is the leitmotif of recent advances for localization of wireless sensors, unveiling traffic anomalies in backbone networks, and preference modeling for recommender systems. This paper develops a distributed algorithm for low-rank matrix completion over networks. While nuclear-norm minimization has well-documented merits when centralized...
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