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| author | Rémi Flamary <remi.flamary@gmail.com> | 2016-10-27 16:31:56 +0200 |
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| committer | Rémi Flamary <remi.flamary@gmail.com> | 2016-10-27 16:31:56 +0200 |
| commit | cb187e2cf8316ee7fe9a65f1d4381fb8baf8050f (patch) | |
| tree | c3edcb1fafb35050f54c880bb7ae98942026afac /README.md | |
| parent | 0b1667deeef0640e0fcb7f125d0fb1658306b475 (diff) | |
better readme
Diffstat (limited to 'README.md')
| -rw-r--r-- | README.md | 12 |
1 files changed, 9 insertions, 3 deletions
@@ -1,14 +1,13 @@ # POT: Python Optimal Transport library -Python Optimal Transport library -This Python library is an open source implementation of several functions that allow to solve optimal transport problems in Python. +This open source Python library provide several solvers for optimization problems related to Optimal Transport for signal, image processing and machine learning. It provides the following solvers: * Linear program (LP) OT solver/ Earth Movers Distance (using code from Antoine Rolet and Nicolas Bonneel [1]). * Entropic regularization OT solver with Sinkhorn Knopp Algorithm [2]. * Bregman projections for Wasserstein barycenter [3] and unmixing [4]. * Optimal transport for domain adaptation with group lasso regularization [5] -* Conditional gradient and Generalized conditional gradient for regularized OT [5]. +* Conditional gradient [6] and Generalized conditional gradient for regularized OT [7]. Some demonstrations (both in Python and Jupyter Notebook Format) are available in the examples folder. @@ -18,6 +17,11 @@ Some demonstrations (both in Python and Jupyter Notebook Format) are available i ## Examples +The examples folder contain several examples abnd use case for the library. Here is a list of the Ypython notebook if you want a quick look. + +* [1D Optimal transport](examples/Demo_1D_OT.ipynb) + + ## Acknowledgements The main developers of this library are: @@ -44,3 +48,5 @@ This toolbox benefit a lot from Open Source research and we would like to thank [5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, "Optimal Transport for Domain Adaptation," in IEEE Transactions on Pattern Analysis and Machine Intelligence , vol.PP, no.99, pp.1-1 [6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). Regularized discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3), 1853-1882. + +[7] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). Generalized conditional gradient: analysis of convergence and applications. arXiv preprint arXiv:1510.06567. |