On Optimal Stochastic Ballistic Transports

Abstract

For a given Lagrangian $L:[0,T]\times M\times M^{\ast }\to {\mathbb{R}}_{+}$ and probability measures $\mu \in \mathcal{P}(M^{\ast })$, $\nu \in \mathcal{P}(M)$, we introduce the stochastic ballistic transportation problems \begin{align}\tag{$\star$} \underline{B}(μ,ν):=\inf\left\{\mathbb{E}\left[\langle V,X_0\rangle +\int_0^T L(t,X,β(t,X))\,dt\right]\middle\rvert V\simμ,X_T\sim ν\right\}\\\tag{$\star \star$} \overline{B}(ν,μ):=\sup\left\{\mathbb{E}\left[\langle V,X_T\rangle -\int_0^T L(t,X,β(t,X))\,dt\right]\middle\rvert V\simμ,X_0\sim ν\right\} \end{align} where $X$ is a diffusion process with drift $\beta$. This cost is based on the stochastic optimal transport problem presented by Mikami and the deterministic ballistic transport introduced by Ghoussoub. We obtain a Kantorovich-style duality result that reformulates this problem in terms of solutions to the Hamilton-Jacobi-Bellman equation \begin{equation*} \frac{\partialϕ}{\partial t}+\frac{1}{2}Δϕ+H(t,x,\nablaϕ)=0, \end{equation*} and show how optimal processes may be thereby attained.

18 pages; Updated version - if any - can be downloaded at http://www.birs.ca/~nassif/