High-performance Python package for balanced k-means clustering using optimal transport and entropic regularization
-
Updated
Jan 30, 2026 - Python
High-performance Python package for balanced k-means clustering using optimal transport and entropic regularization
anyakrakusuma is a high-performance Python solver for the 2D Schrödinger Bridge Problem via Entropic Optimal Transport (EOT), utilizing the log-domain Sinkhorn-Knopp algorithm and Numba acceleration for robust stochastic process interpolation.
Optimal transport primitives: Wasserstein distance, Sinkhorn algorithm, and Sinkhorn divergences (balanced + unbalanced).
Add a description, image, and links to the entropic-regularization topic page so that developers can more easily learn about it.
To associate your repository with the entropic-regularization topic, visit your repo's landing page and select "manage topics."