31 citations · 38 across the 17 of their papers we have counts for
18 papers
Discrete-time feedback linearization control for nonsmooth constrained optimization
Vito Cerone, Sophie Marie Fosson, Simone Pirrera +2
We propose a discrete-time feedback linearization control approach to solve nonsmooth linearly constrained composite optimization problems. By using proximal operators, we construc…
Online sparse observers for cyber-physical systems under sensor bias
Vito Cerone, Sophie M. Fosson, Diego Regruto +1
The design of state observers for cyber-physical systems under sparse sensor biases, faults, or attacks has drawn substantial attention in recent years. While sparsity-based batch…
Global Convergence of Control-Based Lagrangian Flows for Non-Convex Optimization
Simone Pirrera, Francesco Ripa, Daniele Astolfi +3
This paper studies the continuous-time dynamics generated by control-theoretic Lagrangian methods for equality-constrained optimization. In particular, we consider dynamics induced…
Feedback control of Lagrange multipliers for non-smooth constrained optimization
V. Cerone, S. M. Fosson, S. Pirrera +2
In this work, we develop a control-theoretic framework for constrained optimization problems with composite objective functions including non-differentiable terms. Building on the…
A constrained optimization approach to nonlinear system identification through simulation error minimization
Vito Cerone, Sophie M. Fosson, Simone Pirrera +1
This paper introduces a novel approach to system identification for nonlinear input-output models that minimizes the simulation error and frames the problem as a constrained optimi…
Set-membership identification of continuous-time MIMO systems via Tustin discretization
Vito Cerone, Sophie M. Fosson, Simone Pirrera +1
In this paper, we deal with the identification of continuous-time systems from sampled data corrupted by unknown but bounded errors. A significant challenge in continuous-time iden…
Integral control of the proximal gradient method for unbiased sparse optimization
V. Cerone, S. M. Fosson, A. Re +1
Proximal gradient methods are popular in sparse optimization as they are straightforward to implement. Nevertheless, they achieve biased solutions, requiring many iterations to con…
Sparse learning with concave regularization: relaxation of the irrepresentable condition
V. Cerone, S. M. Fosson, D. Regruto +1
Learning sparse models from data is an important task in all those frameworks where relevant information should be identified within a large dataset. This can be achieved by formul…
Alternating direction method of multipliers for polynomial optimization
V. Cerone, S. M. Fosson, S. Pirrera +1
Multivariate polynomial optimization is a prevalent model for a number of engineering problems. From a mathematical viewpoint, polynomial optimization is challenging because it is…
Fast sparse optimization via adaptive shrinkage
Vito Cerone, Sophie M. Fosson, Diego Regruto
The need for fast sparse optimization is emerging, e.g., to deal with large-dimensional data-driven problems and to track time-varying systems. In the framework of linear sparse op…
A feedback control approach to convex optimization with inequality constraints
V. Cerone, S. M. Fosson, S. Pirrera +1
We propose a novel continuous-time algorithm for inequality-constrained convex optimization inspired by proportional-integral control. Unlike the popular primal-dual gradient dynam…
Lasso-based state estimation for cyber-physical systems under sensor attacks
Vito Cerone, Sophie M. Fosson, Diego Regruto +1
The development of algorithms for secure state estimation in vulnerable cyber-physical systems has been gaining attention in the last years. A consolidated assumption is that an ad…
A new framework for constrained optimization via feedback control of Lagrange multipliers
V. Cerone, S. M. Fosson, S. Pirrera +1
The continuous-time analysis of existing iterative algorithms for optimization has a long history. This work proposes a novel continuous-time control-theoretic framework for equali…
Sparse linear regression from perturbed data
S. M. Fosson, V. Cerone, D. Regruto
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, measure…
Enhancing low-rank solutions in semidefinite relaxations of Boolean quadratic problems
V. Cerone, S. M. Fosson, D. Regruto
Boolean quadratic optimization problems occur in a number of applications. Their mixed integer-continuous nature is challenging, since it is inherently NP-hard. For this motivation…
Sparse linear regression with compressed and low-precision data via concave quadratic programming
Vito Cerone, Sophie M. Fosson, Diego Regruto
We consider the problem of the recovery of a k-sparse vector from compressed linear measurements when data are corrupted by a quantization noise. When the number of measurements is…
A linear programming approach to sparse linear regression with quantized data
Vito Cerone, Sophie M. Fosson, Diego Regruto
The sparse linear regression problem is difficult to handle with usual sparse optimization models when both predictors and measurements are either quantized or represented in low-p…
A unified framework for solving a general class of conditional and robust set-membership estimation problems
Vito Cerone, Jean-Bernard Lasserre, Dario Piga +1
In this paper we present a unified framework for solving a general class of problems arising in the context of set-membership estimation/identification theory. More precisely, the…