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diff --git a/docs/source/readme.rst b/docs/source/readme.rst index 5d37f64..a839231 100644 --- a/docs/source/readme.rst +++ b/docs/source/readme.rst @@ -13,13 +13,14 @@ It provides the following solvers: - OT Network Flow solver for the linear program/ Earth Movers Distance [1]. - Entropic regularization OT solver with Sinkhorn Knopp Algorithm [2] - and stabilized version [9][10] with optional GPU implementation - (requires cudamat). + and stabilized version [9][10] and greedy SInkhorn [22] with optional + GPU implementation (requires cudamat). - Smooth optimal transport solvers (dual and semi-dual) for KL and squared L2 regularizations [17]. - Non regularized Wasserstein barycenters [16] with LP solver (only small scale). -- Bregman projections for Wasserstein barycenter [3] and unmixing [4]. +- Bregman projections for Wasserstein barycenter [3], convolutional + barycenter [21] and unmixing [4]. - Optimal transport for domain adaptation with group lasso regularization [5] - Conditional gradient [6] and Generalized conditional gradient for @@ -29,6 +30,9 @@ It provides the following solvers: pymanopt). - Gromov-Wasserstein distances and barycenters ([13] and regularized [12]) +- Stochastic Optimization for Large-scale Optimal Transport (semi-dual + problem [18] and dual problem [19]) +- Non regularized free support Wasserstein barycenters [20]. Some demonstrations (both in Python and Jupyter Notebook format) are available in the examples folder. @@ -219,6 +223,9 @@ The contributors to this library are: - `Stanislas Chambon <https://slasnista.github.io/>`__ - `Antoine Rolet <https://arolet.github.io/>`__ - Erwan Vautier (Gromov-Wasserstein) +- `Kilian Fatras <https://kilianfatras.github.io/>`__ +- `Alain + Rakotomamonjy <https://sites.google.com/site/alainrakotomamonjy/home>`__ This toolbox benefit a lot from open source research and we would like to thank the following persons for providing some code (in various @@ -334,6 +341,31 @@ Optimal Transport <https://arxiv.org/abs/1710.06276>`__. Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics (AISTATS). +[18] Genevay, A., Cuturi, M., Peyré, G. & Bach, F. (2016) `Stochastic +Optimization for Large-scale Optimal +Transport <https://arxiv.org/abs/1605.08527>`__. Advances in Neural +Information Processing Systems (2016). + +[19] Seguy, V., Bhushan Damodaran, B., Flamary, R., Courty, N., Rolet, +A.& Blondel, M. `Large-scale Optimal Transport and Mapping +Estimation <https://arxiv.org/pdf/1711.02283.pdf>`__. International +Conference on Learning Representation (2018) + +[20] Cuturi, M. and Doucet, A. (2014) `Fast Computation of Wasserstein +Barycenters <http://proceedings.mlr.press/v32/cuturi14.html>`__. +International Conference in Machine Learning + +[21] Solomon, J., De Goes, F., Peyré, G., Cuturi, M., Butscher, A., +Nguyen, A. & Guibas, L. (2015). `Convolutional wasserstein distances: +Efficient optimal transportation on geometric +domains <https://dl.acm.org/citation.cfm?id=2766963>`__. ACM +Transactions on Graphics (TOG), 34(4), 66. + +[22] J. Altschuler, J.Weed, P. Rigollet, (2017) `Near-linear time +approximation algorithms for optimal transport via Sinkhorn +iteration <https://papers.nips.cc/paper/6792-near-linear-time-approximation-algorithms-for-optimal-transport-via-sinkhorn-iteration.pdf>`__, +Advances in Neural Information Processing Systems (NIPS) 31 + .. |PyPI version| image:: https://badge.fury.io/py/POT.svg :target: https://badge.fury.io/py/POT .. |Anaconda Cloud| image:: https://anaconda.org/conda-forge/pot/badges/version.svg |