A blob method for mean field control with terminal constraints

¹ Department of Mathematics, University of California, Santa Barbara, CA, USA
² Karthik Elamvazhuthi is with the Applied Mathematics and Plasma Physics Group, Los Alamos National Laboratory, Los Alamos, NM, USA
³ School of Data Science and Society, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

^* Corresponding author: kcraig@math.ucsb.edu

Received: 14 March 2024
Accepted: 17 January 2025

Abstract

In the present work, we develop a novel particle method for a general class of mean field control problems, with source and terminal constraints. Specific examples of the problems we consider include the dynamic formulation of the p-Wasserstein metric, optimal transport around an obstacle, and measure transport subject to acceleration controls. Unlike existing numerical approaches, our particle method is meshfree and does not require global knowledge of an underlying cost function or of the terminal constraint. A key feature of our approach is a novel way of enforcing the terminal constraint via a soft, nonlocal approximation, inspired by recent work on blob methods for diffusion equations.We prove convergence of our particle approximation to solutions of the continuum mean-field control problem in the sense of Γ-convergence. A byproduct of our result is an extension of existing discrete-to-continuum convergence results for mean field control problems to more general state and measure costs, as arise when modeling transport around obstacles, and more general constraint sets, including controllable linear time invariant systems. Finally, we conclude by implementing our method numerically and using it to compute solutions the example problems discussed above. We conduct a detailed numerical investigation of the convergence properties of our method, as well as its behavior in sampling applications and for approximation of optimal transport maps.