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 …

Various distributed optimization methods have been developed for solving problems which
have simple local constraint sets and whose objective function is the sum of local cost …

This article presents a general tool for convexly constrained parameter/function estimation
both for classification and regression tasks, in a timeadaptive setting and in (infinite …

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 a private distributed online optimization problem where a set of agents aim to
minimize the sum of locally convex cost functions while each desires that the local cost …

We present a novel diffusion scheme for online kernel-based learning over networks. So far,
a major drawback of any online learning algorithm, operating in a reproducing kernel Hilbert …

The first aim of this paper is to present a useful toolbox of quasi-nonexpansive mappings for
convex optimization from the viewpoint of using their fixed point sets as constraints. Many …

In this paper, a sparsity promoting adaptive algorithm for distributed learning in diffusion
networks is developed. The algorithm follows the set-theoretic estimation rationale. At each …

Rationale: Drug-related cues may acquire incentive properties through classical
conditioning. Objective: The present study investigated whether arbitrary stimuli paired with …