POT and PPOT

POT is a foundational optimal transport library that PPOT builds upon to implement its progressive partial optimal transport algorithm for clustering applications, making them complements rather than competitors.

POT

Verified

PPOT

Experimental

Maintenance 16/25

Adoption 15/25

Maturity 25/25

Community 25/25

Maintenance 0/25

Adoption 6/25

Maturity 9/25

Community 9/25

Stars: 2,772

Forks: 540

Downloads: —

Commits (30d): 1

Language: Python

License: MIT

Stars: 18

Forks: 2

Downloads: —

Commits (30d): 0

Language: Python

License: —

No risk flags

Stale 6m No Package No Dependents

About POT

PythonOT/POT

POT : Python Optimal Transport

Implements differentiable solvers for linear, entropic, and quadratic regularized optimal transport problems using algorithms like Sinkhorn-Knopp and conditional gradient, plus specialized variants for Gromov-Wasserstein distances, unbalanced/partial OT, and domain adaptation. Provides multiple computational backends (PyTorch, JAX, TensorFlow, NumPy, CuPy) enabling seamless integration with deep learning frameworks and GPU acceleration for large-scale problems.

About PPOT

rhfeiyang/PPOT

Official implementation of 'P$^2$OT: Progressive Partial Optimal Transport for Deep Imbalanced Clustering'. (Accepted by ICLR 2024)

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