From 20da1f630dac2639ae86f625b46d4270e384f351 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 22 Apr 2020 11:53:33 +0200 Subject: re-org readme --- docs/source/readme.rst | 64 ++++++++++++++++++++++---------------------------- 1 file changed, 28 insertions(+), 36 deletions(-) (limited to 'docs') diff --git a/docs/source/readme.rst b/docs/source/readme.rst index 4da1ceb..76d37a4 100644 --- a/docs/source/readme.rst +++ b/docs/source/readme.rst @@ -10,42 +10,34 @@ machine learning. Website and documentation: https://PythonOT.github.io/ -POT 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], - stabilized version [9][10] and greedy Sinkhorn [22] with optional GPU - implementation (requires cupy). -- Sinkhorn divergence [23] and entropic regularization OT from - empirical data. -- 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], convolutional - barycenter [21] and unmixing [4]. -- Optimal transport for domain adaptation with group lasso - regularization and Laplacian regularization [5][30] -- Conditional gradient [6] and Generalized conditional gradient for - regularized OT [7]. -- Linear OT [14] and Joint OT matrix and mapping estimation [8]. -- Wasserstein Discriminant Analysis [11] (requires autograd + - 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]. -- Unbalanced OT with KL relaxation distance and barycenter [10, 25]. -- Screening Sinkhorn Algorithm for OT [26]. -- JCPOT algorithm for multi-source domain adaptation with target shift - [27]. -- Partial Wasserstein and Gromov-Wasserstein (exact [29] and entropic - [3] formulations). - -Some demonstrations (both in Python and Jupyter Notebook format) are -available in the examples folder. +POT provides the following generic OT solvers: \* OT Network Flow solver +for the linear program/ Earth Movers Distance [1]. \* Conditional +gradient [6] and Generalized conditional gradient for regularized OT +[7]. \* Entropic regularization OT solver with Sinkhorn Knopp Algorithm +[2], stabilized version [9] [10], greedy Sinkhorn [22] and Screening +Sinkhorn [26] with optional GPU implementation (requires cupy). \* +Bregman projections for Wasserstein barycenter [3], convolutional +barycenter [21] and unmixing [4]. \* Sinkhorn divergence [23] and +entropic regularization OT from empirical data. \* 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). \* 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]. \* +Unbalanced OT with KL relaxation distance and barycenter [10, 25]. \* +Partial Wasserstein and Gromov-Wasserstein (exact [29] and entropic [3] +formulations). + +POT provides the following Machine Learning related solvers: \* Optimal +transport for domain adaptation with group lasso regularization and +Laplacian regularization [5] [30]. \* Linear OT [14] and Joint OT matrix +and mapping estimation [8]. \* Wasserstein Discriminant Analysis [11] +(requires autograd + pymanopt). \* JCPOT algorithm for multi-source +domain adaptation with target shift [27]. + +Some demonstrations are available in the +`documentation `__. Using and citing the toolbox ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -- cgit v1.2.3