Exponential Turnpike Theorems for Nonlinear Deterministic Meanfield Optimal Control Problems

TL;DR

This paper proves exponential turnpike theorems for nonlinear deterministic meanfield optimal control problems using both Lagrangian and Eulerian frameworks, involving advanced mathematical tools like Riccati operators and Wasserstein space analysis.

Contribution

It introduces novel exponential turnpike results in both Lagrangian and Eulerian settings for meanfield control, linking Wasserstein Hessians and occupation measures.

Findings

Exponential turnpike theorem established in Lagrangian framework.

Exponential turnpike theorem derived in Wasserstein space.

Explicit connection between Wasserstein Hessians and Lagrangian lifts provided.

Abstract

In this article, we establish exponential turnpike theorems for a class of nonlinear deterministic meanfield optimal control problems. We carry out our analysis simultaneously in the so-called Lagrangian and Eulerian frameworks. In the Lagrangian setting, the problem is lifted to a Hilbert space of random variables, and we prove an exponential turnpike theorem by combining first-order optimality conditions, a second-order expansion of the lifted Hamiltonian, and an operator Riccati diagonalization argument. In the Eulerian setting, we derive intrinsic KKT conditions for the static constrained problem, and show how the Eulerian second-order hypotheses split into a horizontal part, transferred by unitary conjugation to the lifted space, and a vertical part which reduces to uniform pointwise stabilizability and detectability conditions on multiplication operators. This yields an exponential turnpike theorem in the Wasserstein space for optimal Pontryagin triples. Along the way, we %provide explicit the link between Wasserstein Hessians and their Lagrangian lifts, and provide several remarks clarifying the role of occupation measures, local Eulerian minimizers, and control constraints in our results.