In many practical situations, it is highly desirable to estimate an accurate mathematical
model of a real system using as few parameters as possible. At the same time, the need for …

A sparse estimator with close ties with the LASSO (least absolute shrinkage and selection
operator) is analysed. The basic idea of the estimator is to relax the least-squares cost …

The problem of sparse linear regression is relevant in the context of linear system
identification from large datasets. When data are collected from real-world experiments …

This paper addresses the problem of identifying sparse linear time-invariant (LTI) systems
from a single sample trajectory generated by the system dynamics. We introduce a Lasso …

Sparse estimation methods based on ℓ1 relaxation, such as Lasso and Dantzig Selector, are
powerful tools for estimating high dimensional linear models. However, in order to properly …

Focusing on identification, this paper develops techniques to reconstruct zero and nonzero
elements of a sparse parameter vector θ of a stochastic dynamic system with general …

Accurate parametric identification of Linear Parameter-Varying (LPV) systems requires an
optimal prior selection of model order and a set of functional dependencies for the …

The purpose of this article is to survey some sparsity‐inducing methods in system
identification and state estimation. Such methods can be divided into two main categories …

We consider the problem of sparse estimation in a Bayesian framework. We outline the
derivation of the Lasso in terms of marginalization of a particular Bayesian model. A different …

In this paper, we study the system identification problem for sparse linear time-invariant
systems. We propose a sparsity promoting Lasso-type estimator to identify the dynamics of …