This paper surveys recent advances related to adaptation, learning, and optimization over
networks. Various distributed strategies are discussed that enable a collection of networked …

This work deals with the topic of information processing over graphs. The presentation is
largely self-contained and covers results that relate to the analysis and design of multi-agent …

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

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 …

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

In this paper, we propose a quantization approach, as an alternative of sparsification, to curb
the growth of the radial basis function structure in kernel adaptive filtering. The basic idea …

An adaptive distributed strategy is developed based on incremental techniques. The
proposed scheme addresses the problem of linear estimation in a cooperative fashion, in …

Affine projection algorithms are useful adaptive filters whose main purpose is to speed the
convergence of LMS-type filters. Most analytical results on affine projection algorithms …

We introduce a novel family of adaptive filtering algorithms based on a relative logarithmic
cost inspired by the “competitive methods” from the online learning literature. The …