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20212025
most citedModel predictive control strategies using consensus-based optimization

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math.OC2025

Control of Conservation Laws in the Nonlocal-to-Local Limit

Jan Friedrich, Michael Herty, Claudia Nocita

We analyze a class of control problems where the initial datum acts as a control and the state is given by the entropy solution of (local) conservation laws by a nonlocal-to-local…

math.OC2025

Sparse control in microscopic and mean-field leader-follower models

Melanie Harms, Michael Herty, Chiara Segala +1

This work investigates the decay properties of Lyapunov functions in leader-follower systems seen as a sparse control framework. Starting with a microscopic representation, we esta…

math.OC2025

Boundary Stabilization with restricted observability

Mapundi Kondwani Banda, Jan Friedrich, Michael Herty

Lyapunov functions are popularly used to investigate the stabilization problem of systems of hyperbolic conservation laws with boundary controls. In real life applications often no…

math.OC2025

Micro-Macro Decomposition of Particle Swarm Optimization Methods

Michael Herty, Sara Veneruso

Solving non-convex minimization problems using multi-particle metaheuristic derivative-free optimization methods is still an active area of research. Popular methods are Particle S…

math.OC2024

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…

math.OC2024

Kinetic models for optimization: a unified mathematical framework for metaheuristics

Giacomo Borghi, Michael Herty, Lorenzo Pareschi

Metaheuristic algorithms, widely used for solving complex non-convex and non-differentiable optimization problems, often lack a solid mathematical foundation. In this review, we ex…

math.OC2024

Exponential turnpike property for particle systems and mean-field limit

Michael Herty, Yizhou Zhou

This work is concerned with the exponential turnpike property for optimal control problems of particle systems and their mean-field limit. Under the assumption of the strict dissip…

math.OC2024

Kinetic Theories for Metropolis Monte Carlo Methods

Michael Herty, Christian Ringhofer

We consider generalizations of the classical inverse problem to Bayesien type estimators, where the result is not one optimal parameter but an optimal probability distribution in p…

math.OC2024

The turnpike property for high-dimensional interacting agent systems in discrete time

Martin Gugat, Michael Herty, Jiehong Liu +1

We investigate the interior turnpike phenomenon for discrete-time multi-agent optimal control problems. While for continuous systems the turnpike property has been established, we…

math.OC2024

Controllability of continuous networks and a kernel-based learning approximation

Michael Herty, Chiara Segala, Giuseppe Visconti

Residual deep neural networks are formulated as interacting particle systems leading to a description through neural differential equations, and, in the case of large input data, t…

math.OC2024

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…

math.OC2023★ 1 cited

Model predictive control strategies using consensus-based optimization

Giacomo Borghi, Michael Herty

Model predictive control strategies require to solve in an sequential manner, many, possibly non-convex, optimization problems. In this work, we propose an interacting stochastic a…

math.OC2021

Constrained consensus-based optimization

Giacomo Borghi, Michael Herty, Lorenzo Pareschi

In this work we are interested in the construction of numerical methods for high dimensional constrained nonlinear optimization problems by particle-based gradient-free techniques.…