September 28, 2025Open Access

An Introduction to Sliced Optimal Transport

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Sliced optimal transport enables efficient computation of distances and barycenters in probability measures, enhancing traditional optimal transport methods.
SOT combines tools from optimal transport and integral geometry, offering scalable solutions like Monte Carlo approximations for practical applications.
The paper reviews advancements in methodologies, such as non-linear projections and statistical estimation techniques, relevant for computational statistics.
Applications of sliced optimal transport are found in machine learning, statistics, and computer graphics, demonstrating its versatility as a computational tool.

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This review discusses developments in sliced optimal transport, examining methods for computing distances and barycenters in probability measures.

Khai Nguyen (2025) studied this question.

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This review discusses developments in sliced optimal transport, examining methods for computing distances and barycenters in probability measures.