Fast Unbalanced Optimal Transport on a Tree
This work addresses computational bottlenecks for researchers and practitioners applying unbalanced optimal transport to large-scale datasets, though it is incremental in advancing theoretical foundations.
The study tackled the computational complexity of unbalanced optimal transport problems, proving that some cannot be solved efficiently under certain hypotheses, and proposed an algorithm that solves a general version exactly in quasi-linear time on a tree metric, processing a tree with one million nodes in less than one second.
This study examines the time complexities of the unbalanced optimal transport problems from an algorithmic perspective for the first time. We reveal which problems in unbalanced optimal transport can/cannot be solved efficiently. Specifically, we prove that the Kantorovich Rubinstein distance and optimal partial transport in the Euclidean metric cannot be computed in strongly subquadratic time under the strong exponential time hypothesis. Then, we propose an algorithm that solves a more general unbalanced optimal transport problem exactly in quasi-linear time on a tree metric. The proposed algorithm processes a tree with one million nodes in less than one second. Our analysis forms a foundation for the theoretical study of unbalanced optimal transport algorithms and opens the door to the applications of unbalanced optimal transport to million-scale datasets.