5 citations · 17 across the 15 of their papers we have counts for
4 papers · 1 filter
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.…
Separable Approximations of Optimal Value Functions and Their Representation by Neural Networks
Mario Sperl, Luca Saluzzi, Dante Kalise +1
The use of separable approximations is proposed to mitigate the curse of dimensionality related to the approximation of high-dimensional value functions in optimal control. The sep…
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…