Parallel and distributed computation: numerical methods
Parallel and distributed computation: numerical methods
Neuro-Dynamic Programming
Wireless sensor networks: a survey
Computer Networks: The International Journal of Computer and Telecommunications Networking
Incremental Subgradient Methods for Nondifferentiable Optimization
SIAM Journal on Optimization
Distributed optimization in sensor networks
Proceedings of the 3rd international symposium on Information processing in sensor networks
Probability and Computing: Randomized Algorithms and Probabilistic Analysis
Probability and Computing: Randomized Algorithms and Probabilistic Analysis
Brief paper: Convergence speed in distributed consensus over dynamically switching random networks
Automatica (Journal of IFAC)
Foundations and Trends® in Networking
Broadcast gossip algorithms for consensus
IEEE Transactions on Signal Processing
Distributed LMS for consensus-based in-network adaptive processing
IEEE Transactions on Signal Processing
Distributed recursive least-squares for consensus-based in-network adaptive estimation
IEEE Transactions on Signal Processing
Adaptive fast consensus algorithm for distributed sensor fusion
Signal Processing
Consensus-Based Distributed Support Vector Machines
The Journal of Machine Learning Research
Incremental Subgradients for Constrained Convex Optimization: A Unified Framework and New Methods
SIAM Journal on Optimization
Foundations and Trends® in Machine Learning
Consensus in Ad Hoc WSNs With Noisy Links—Part I: Distributed Estimation of Deterministic Signals
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
IEEE Transactions on Information Theory
Toward a theory of in-network computation in wireless sensor networks
IEEE Communications Magazine
Randomized consensus algorithms over large scale networks
IEEE Journal on Selected Areas in Communications
Fast Consensus by the Alternating Direction Multipliers Method
IEEE Transactions on Signal Processing
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We first propose an unbiased consensus algorithm in wireless networks via random broadcast, by which all the nodes tend to the initial average in mean almost surely. The innovation of the algorithm lies in that it can work in any connected topology, in spite of the possible collisions from simultaneous data arriving at receivers in a shared channel. Based on the consensus algorithm, we propose a distributed optimization algorithm for a sum of convex objective functions, which is the fundamental model for many applications on signal processing in network. Simulation results show that our algorithms provide an appealing performance with lower communicational complexity compared with existing algorithms.