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 …

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 …

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 …

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 …

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 …

Advances in machine learning and deep neural networks have enabled complex
engineering tasks like image recognition, anomaly detection, regression, and multi-objective …

In this work, we introduce a new class of linear time-invariant systems for which, at each time
instant, the input is sparse with respect to an overcomplete dictionary of inputs. Such …

Identifying parsimonious models is generically a “hard” nonconvex problem. Available
approaches typically rely on relaxations such as Group Lasso or nuclear norm minimization …

Low complexity of a system model is essential for its use in real-time applications. However,
sparse identification methods commonly have stringent requirements that exclude them from …

Motivated by recent approaches to switched linear system identification based on sparse
optimization, the paper deals with the recovery of sparse solutions of underdetermined …