5 citations · 17 across the 15 of their papers we have counts for
13 papers · 1 filter
Steering dynamic network centrality via control theory
Fabio Durastante, Beatrice Meini, Luca Saluzzi
Time-evolving networks, or temporal networks, play a crucial role in modeling dynamic interactions across various domains, including biology, social sciences, and information techn…
High order Tensor-Train-Based Schemes for High-Dimensional Mean Field Games
Elisabetta Carlini, Luca Saluzzi
We introduce a fully discrete scheme to solve a class of high-dimensional Mean Field Games systems. Our approach couples semi-Lagrangian (SL) time discretizations with Tensor-Train…
The State-Dependent Riccati Equation in Nonlinear Optimal Control: Analysis, Error Estimation and Numerical Approximation
Luca Saluzzi
The State-Dependent Riccati Equation (SDRE) approach is extensively utilized in nonlinear optimal control as a reliable framework for designing robust feedback control strategies.…
Dynamical Low-Rank Approximation Strategies for Nonlinear Feedback Control Problems
Luca Saluzzi, Maria Strazzullo
This paper addresses the stabilization of dynamical systems in the infinite horizon optimal control setting using nonlinear feedback control based on State-Dependent Riccati Equati…
On the data-sparsity of the solution of Riccati equations with applications to feedback control
Stefano Massei, Luca Saluzzi
Solving large-scale continuous-time algebraic Riccati equations is a significant challenge in various control theory applications. This work demonstrates that when the matrix coeff…
A comparison study of supervised learning techniques for the approximation of high dimensional functions and feedback control
Mathias Oster, Luca Saluzzi, Tizian Wenzel
Approximation of high dimensional functions is in the focus of machine learning and data-based scientific computing. In many applications, empirical risk minimisation techniques ov…
A multilinear HJB-POD method for the optimal control of PDEs
Gerhard Kirsten, Luca Saluzzi
Optimal control problems driven by evolutionary partial differential equations arise in many industrial applications and their numerical solution is known to be a challenging probl…
Feedback reconstruction techniques for optimal control problems on a tree structure
Alessandro Alla, Luca Saluzzi
The computation of feedback control using Dynamic Programming equation is a difficult task due the curse of dimensionality. The tree structure algorithm is one the methods introduc…
Data-driven Tensor Train Gradient Cross Approximation for Hamilton-Jacobi-Bellman Equations
Sergey Dolgov, Dante Kalise, Luca Saluzzi
A gradient-enhanced functional tensor train cross approximation method for the resolution of the Hamilton-Jacobi-Bellman (HJB) equations associated to optimal feedback control of n…
A tree structure algorithm for optimal control problems with state constraints
Alessandro Alla, Maurizio Falcone, Luca Saluzzi
We present a tree structure algorithm for optimal control problems with state constraints. We prove a convergence result for a discrete time approximation of the value function bas…
A HJB-POD approach for the control of nonlinear PDEs on a tree structure
Alessandro Alla, Luca Saluzzi
The Dynamic Programming approach allows to compute a feedback control for nonlinear problems, but suffers from the curse of dimensionality. The computation of the control relies on…
Error estimates for a tree structure algorithm solving finite horizon control problems
Luca Saluzzi, Alessandro Alla, Maurizio Falcone
In the Dynamic Programming approach to optimal control problems a crucial role is played by the value function that is characterized as the unique viscosity solution of a Hamilton-…
An efficient DP algorithm on a tree-structure for finite horizon optimal control problems
Alessandro Alla, Maurizio Falcone, Luca Saluzzi
The classical Dynamic Programming (DP) approach to optimal control problems is based on the characterization of the value function as the unique viscosity solution of a Hamilton-Ja…