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The log methods for sinkhorn() and
barycenter() both require row-by-row and column-by-column
soft-minimum operations for each iteration of the algorithm,
and therefore suffer from slow computation time compared to the
vanilla/parallel version.
If the dimensions of \(M\) and \(N\) are large (recall that cost matrix C is of size \(M \times N\)), we can use multi-threading to process the rows and columns simultaneously.
This can be done by setting n_threads to an integer
bigger than 0. By default it is 0, and it means that threading is
disabled. Also, setting n_threads for “vanilla” or
“parallel” methods will be ignored automatically.
But you might ask: if multi-threading is so wonderful, why don’t you set threading as the default? This is because threading comes with an overhead, and sometimes for small problems, it can even be slower than serial processing. So be sure to benchmark your code and see if threading actually helps.
See also vignette("sinkhorn"),
vignette("barycenter").
Peyré, G., & Cuturi, M. (2019). Computational Optimal Transport: With Applications to Data Science. Foundations and Trends® in Machine Learning, 11(5–6), 355–607. https://doi.org/10.1561/2200000073
Xie, F. (2025). Deriving the Gradients of Some Popular Optimal Transport Algorithms (No. arXiv:2504.08722). arXiv. https://doi.org/10.48550/arXiv.2504.08722
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