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Poster
Fast Unbalanced Optimal Transport on a Tree
Ryoma Sato · Makoto Yamada · Hisashi Kashima

Tue Dec 08 09:00 PM -- 11:00 PM (PST) @ Poster Session 2 #699

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.

Author Information

Ryoma Sato (Kyoto University)

I am a first year student in master's program at Kashima-Yamada Lab, Kyoto University Interests: Machine Learning on Graphs, Discrete Algorithms

Makoto Yamada (Kyoto University/RIKEN AIP)
Hisashi Kashima (Kyoto University/RIKEN Center for AIP)

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