Dynamical Optimal Transport with $\mathfrak{s}\mathfrak{o}(d)$-Invariance: From Theory to Computation

We introduce a modified Benamou–Brenier (MBB) formulation of optimal transport that incorporates Euclidean invariance at the dynamical level. We establish existence of minimizers for the resulting variational problem and prove its equivalence to a static formulation defining the Procrustes–Wasserstein distance. In the Gaussian setting, we show that this distance admits a closed-form expression, reducing to the Euclidean distance between the vectors of square roots of the ordered eigenvalues of the covariance matrices. On the computational side, we formulate a primal–dual scheme for the discretized problem. We prove a local conditional subsequential convergence result through an abstract analysis of a class of parameter-dependent saddle-point problems and illustrate the method’s performance numerically.