A New Class of Incremental Gradient Methods for Least Squares Problems
SIAM Journal on Optimization
Incremental Subgradient Methods for Nondifferentiable Optimization
SIAM Journal on Optimization
Incremental Adaptive Strategies Over Distributed Networks
IEEE Transactions on Signal Processing
Mean-square performance of a convex combination of two adaptive filters
IEEE Transactions on Signal Processing
IEEE Communications Magazine
Quantized incremental algorithms for distributed optimization
IEEE Journal on Selected Areas in Communications
An adaptive projected subgradient approach to learning in diffusion networks
IEEE Transactions on Signal Processing
Diffusion LMS strategies for distributed estimation
IEEE Transactions on Signal Processing
Adaptive filter algorithms for accelerated discrete-time consensus
IEEE Transactions on Signal Processing
Distributed estimation over an adaptive incremental network based on the affine projection algorithm
IEEE Transactions on Signal Processing
Diffusion least-mean squares with adaptive combiners: formulation and performance analysis
IEEE Transactions on Signal Processing
Decentralized subspace tracking via gossiping
DCOSS'10 Proceedings of the 6th IEEE international conference on Distributed Computing in Sensor Systems
Distributed estimation over complex networks
Information Sciences: an International Journal
Distributed estimation via iterative projections with application to power network monitoring
Automatica (Journal of IFAC)
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The article describes recent adaptive estimation algorithms over distributed networks. The algorithms rely on local collaborations and exploit the space-time structure of the data. Each node is allowed to communicate with its neighbors in order to exploit the spatial dimension, while it also evolves locally to account for the time dimension. Algorithms of the least-mean-squares and least-squares types are described. Both incremental and diffusion strategies are considered.