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 …

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 …

This chapter concerns the use of convex optimization in real-time embedded systems, in
areas such as signal processing, automatic control, real-time estimation, real-time resource …

The goal of this paper is to derive a novel online algorithm for classification in reproducing
kernel hilbert spaces (RKHS) by exploiting projection-based adaptive filtering tools. The …

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 introduces a wide framework for online, ie, time-adaptive, supervised
multiregression tasks. The problem is formulated in a general infinite-dimensional …

This paper proposes a novel adaptive filtering scheme named the Krylov-proportionate
normalized least-mean-square (KPNLMS) algorithm. KPNLMS exploits the benefits (ie, fast …

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

This paper proposes a novel linear estimator named stochastic MV-PURE estimator,
developed for the stochastic linear model, and designed to provide improved performance …