1 citations · 1 across the 13 of their papers we have counts for
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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…
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…
Dynamics of measure-valued agents in the space of probabilities
Giacomo Borghi, Michael Herty, Andrey Stavitskiy
Motivated by the development of dynamics in probability spaces, we propose a novel multi-agent dynamic of consensus type where each agent is a probability measure. The agents move…
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…
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…
Source Identification by Consensus-Based Optimization
Jan Friedrich, Sarah Schraven, Fabian Kiessling +1
A consensus-based optimization (CBO) algorithm, which enables derivative and mesh-free optimization, is presented to localize a bioluminescent source. The light propagation is mode…
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…
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…
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…