Abstract Semi-discrete optimal transport problems, which evaluate the Wasserstein distance between a discrete and generic (possibly non-discrete) probability measure, are believed to be computationally hard. Even though such problems ubiquitous in statistics, machine learning computer vision, however, this perception has not yet received theoretical justification. To fill gap, we prove that com...