Efficient and Scalable Spatial Regularization of Optimal Transport

In this paper, we introduce a novel approach to spatial regularization of optimal transport problems. Based on the notion of forward and backward “mean maps” of a transport plan, we introduce a convex formulation of optimal transport problems that incorporates regularization of these mean maps to promote spatial continuity of the resulting optimal plan. Unlike previous regularization approaches that required the optimization of all the transport plan coefficients, our formulation translates into an ADMM-based solver combined with Sinkhorn type algorithms, which drastically reduces the number of variables and scales up to large problems. We demonstrate the usefulness and efficiency of this new computational tool for various applications and for different regularizations.