6 papers
Hessian-augmented Supervised Learning for Hamilton-Jacobi-Bellman PDEs
Matías Gómez-Aedo, Behzad Azmi, Yuyang Huang +2
A data-driven method is developed for approximating value functions in deterministic optimal control problems with nonlinear control-affine dynamics. The Pontryagin Maximum Princip…
Non-Convex Global Optimization as an Optimal Stabilization Problem: Dynamical Properties
Yuyang Huang, Dante Kalise, Hicham Kouhkouh
We study global optimization of non-convex functions through optimal control theory. Our main result establishes that (quasi-)optimal trajectories of a discounted control problem c…
Non-Convex Global Optimization as an Optimal Stabilization Problem: Convergence Rates
Yuyang Huang, Dante Kalise, Hicham Kouhkouh
We develop a rigorous framework for global non-convex optimization by reformulating the minimization problem as a discounted infinite-horizon optimal control problem. For non-conve…
Fast and robust consensus-based optimization via optimal feedback control
Yuyang Huang, Michael Herty, Dante Kalise +1
We propose a variant of consensus-based optimization (CBO) algorithms, controlled-CBO, which introduces a feedback control term to improve convergence towards global minimizers of…
A multiscale Consensus-Based algorithm for multi-level optimization
Michael Herty, Yuyang Huang, Dante Kalise +1
A novel multiscale consensus-based optimization (CBO) algorithm for solving bi- and tri-level optimization problems is introduced. Existing CBO techniques are generalized by the pr…
Data/moment-driven approaches for fast predictive control of collective dynamics
Giacomo Albi, Sara Bicego, Michael Herty +3
Feedback control synthesis for large-scale particle systems is reviewed in the framework of model predictive control (MPC). The high-dimensional character of collective dynamics ha…