An Efficient DP Algorithm on a Tree-Structure for Finite Horizon Optimal Control Problems. - Alessandro Alla; Maurizio Falcone; Luca Saluzzi (SIAM J. Sci. Comput., 2019)

A tree structure algorithm for optimal control problems with state constraints. - Alessandro Alla; Maurizio Falcone; Luca Saluzzi (CoRR, 2020)

Optimizing semilinear representations for State-dependent Riccati Equation-based feedback control. - Sergey Dolgov; Dante Kalise; Luca Saluzzi (CoRR, 2022)

Feedback reconstruction techniques for optimal control problems on a tree structure. - Alessandro Alla; Luca Saluzzi (CoRR, 2022)

Separable Approximations of Optimal Value Functions Under a Decaying Sensitivity Assumption. - Mario Sperl; Luca Saluzzi; Lars Grüne; Dante Kalise (CDC, 2023)

Approximation of optimal control problems for the Navier-Stokes equation via multilinear HJB-POD. - Maurizio Falcone; Gerhard Kirsten; Luca Saluzzi (Appl. Math. Comput., 2023)

A multilinear HJB-POD method for the optimal control of PDEs. - Gerhard Kirsten; Luca Saluzzi (CoRR, 2023)

Statistical Proper Orthogonal Decomposition for model reduction in feedback control. - Sergey Dolgov; Dante Kalise; Luca Saluzzi (CoRR, 2023)

Data-Driven Tensor Train Gradient Cross Approximation for Hamilton-Jacobi-Bellman Equations. - Sergey Dolgov; Dante Kalise; Luca Saluzzi (SIAM J. Sci. Comput., 2023)

On the data-sparsity of the solution of Riccati equations with applications to feedback control. - Stefano Massei; Luca Saluzzi (CoRR, 2024)

A Multilinear HJB-POD Method for the Optimal Control of PDEs on a Tree Structure. - Gerhard Kirsten; Luca Saluzzi (J. Sci. Comput., 2024)

State Dependent Riccati for dynamic boundary control to optimize irrigation in Richards' equation framework. - Alessandro Alla; Marco Berardi; Luca Saluzzi (Math. Comput. Simul., 2025)

Dynamical Low-Rank Approximation Strategies for Nonlinear Feedback Control Problems. - Luca Saluzzi; Maria Strazzullo (CoRR, 2025)

The State-Dependent Riccati Equation in Nonlinear Optimal Control: Analysis, Error Estimation and Numerical Approximation. - Luca Saluzzi (CoRR, 2025)

A Comparison Study of Supervised Learning Techniques for the Approximation of High Dimensional Functions and Feedback Control. - Mathias Oster; Luca Saluzzi; Tizian Wenzel (Dyn. Games Appl., 2025)