Optimal transport (OT) is a principled approach for matching, having achieved success in diverse applications such as tracking and cluster alignment. It also the core computation problem solving Wasserstein metric between probabilistic distributions, which has been increasingly used machine learning. Despite its popularity, marginal constraints of OT impose fundamental limitations. For some mat...