activity
20172022
most citedThe Pontryagin Maximum Principle in the Wasserstein Space

43 citations · 172 across the 14 of their papers we have counts for

collaborators

16 papers

math.OC2022

Viability and Exponentially Stable Trajectories for Differential Inclusions in Wasserstein Spaces

Benoît Bonnet, Hélène Frankowska

In this article, we prove a general viability theorem for continuity inclusions in Wasserstein spaces, and provide an application thereof to the existence of exponentially stable t…

math.OC2022

First-Order Pontryagin Maximum Principle for Risk-Averse Stochastic Optimal Control Problems

Riccardo Bonalli, Benoît Bonnet

In this paper, we derive first-order Pontryagin optimality conditions for risk-averse stochastic optimal control problems subject to final time inequality constraints, and whose co…

math.OC2021★ 25 cited

Consensus Formation in First-Order Graphon Models with Time-Varying Topologies

Benoît Bonnet, Nastassia Pouradier Duteil, Mario Sigalotti

In this article, we investigate the asymptotic formation of consensus for several classes of time-dependent cooperative graphon dynamics. After motivating the use of this type of m…

math.OC2021★ 15 cited

Semiconcavity and Sensitivity Analysis in Mean-Field Optimal Control and Applications

Benoît Bonnet, Hélène Frankowska

In this article, we investigate some of the fine properties of the value function associated to an optimal control problem in the Wasserstein space of probability measures. Buildin…

math.OC2021

On the Properties of the Value Function Associated to a Mean-Field Optimal Control Problem of Bolza Type

Benoît Bonnet, Hélène Frankowska

In this paper, we obtain several structural results for the value function associated to a mean-field optimal control problem of Bolza type in the space of measures. After establis…

math.OC2021★ 4 cited

A Measure Theoretical Approach to the Mean-field Maximum Principle for Training NeurODEs

Benoît Bonnet, Cristina Cipriani, Massimo Fornasier +1

In this paper we consider a measure-theoretical formulation of the training of NeurODEs in the form of a mean-field optimal control with -regularization of the control. We der…