We propose an adaptive diffusion mechanism to optimize global cost functions in a
distributed manner over a network of nodes. The cost function is assumed to consist of a …

In this work, we study the task of distributed optimization over a network of learners in which
each learner possesses a convex cost function, a set of affine equality constraints, and a set …

Adaptive networks consist of a collection of nodes with adaptation and learning abilities. The
nodes interact with each other on a local level and diffuse information across the network to …

Adaptive networks (AN) have been recently proposed to address distributed estimation
problems [1]-[4]. Here we extend prior work to changing topologies and data-normalized …

In this paper, the problem of adaptive distributed learning in diffusion networks is
considered. The algorithms are developed within the convex set theoretic framework. More …

We consider the problem of distributed estimation, where a set of nodes is required to
collectively estimate some parameter of interest from noisy measurements. The problem is …

Adaptive networks rely on in-network and collaborative processing among distributed
agents to deliver enhanced performance in estimation and inference tasks. Information is …

Using the diffusion strategies, an unknown parameter vector can be estimated over an
adaptive network by combining the intermediate estimates of neighboring nodes at each …

Adaptive networks are well-suited to perform decentralized information processing and
optimization tasks and to model various types of self-organized and complex behavior …

We formulate and study distributed estimation algorithms based on diffusion protocols to
implement cooperation among individual adaptive nodes. The individual nodes are …