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 …

We present an algorithm that minimizes asymptotically a sequence of nonnegative convex
functions over diffusion networks. In the proposed algorithm, at each iteration the nodes in …

We consider a convex optimization problem for non-hierarchical agent networks where each
agent has access to a local or private time-varying function, and the network-wide objective …

This paper indicates that an appropriate design of metric leads to significant improvements
in the adaptive projected subgradient method (APSM), which unifies a wide range of …

This paper presents an analysis of the steady-state mean-square error (MSE) of the set-
membership normalized least-mean square (SM-NLMS) algorithm with relaxation and …

We present a unified analytic tool named variable-metric adaptive projected subgradient
method (V-APSM) that encompasses the important family of adaptive variable-metric …

The adaptive parallel subgradient projection (PSP) algorithm was proposed in 2002 as a set-
theoretic adaptive filtering algorithm providing fast and stable convergence, robustness …

This paper introduces adaptive filters that are effective to suppress multiple access
interference (MAI) in orthogonal space-time block coded/multiple-input multiple-output …

In this paper, we propose a novel reduced-rank adaptive filtering algorithm exploiting the
Krylov subspace associated with estimates of certain statistics of input and output signals …

We propose the multi-domain adaptive learning that enables us to find a point meeting
possibly time-varying specifications simultaneously in multiple domains, eg space, time …