diff options
Diffstat (limited to 'README.md')
| -rw-r--r-- | README.md | 8 |
1 files changed, 4 insertions, 4 deletions
@@ -20,7 +20,7 @@ POT provides the following generic OT solvers (links to examples): * [OT Network Simplex solver](https://pythonot.github.io/auto_examples/plot_OT_1D.html) for the linear program/ Earth Movers Distance [1] . * [Conditional gradient](https://pythonot.github.io/auto_examples/plot_optim_OTreg.html) [6] and [Generalized conditional gradient](https://pythonot.github.io/auto_examples/plot_optim_OTreg.html) for regularized OT [7]. -* Entropic regularization OT solver with [Sinkhorn Knopp Algorithm](https://pythonot.github.io/auto_examples/plot_OT_1D.html) [2] , stabilized version [9] [10], greedy Sinkhorn [22] and [Screening Sinkhorn [26] ](https://pythonot.github.io/auto_examples/plot_screenkhorn_1D.html) with optional GPU implementation (requires cupy). +* Entropic regularization OT solver with [Sinkhorn Knopp Algorithm](https://pythonot.github.io/auto_examples/plot_OT_1D.html) [2] , stabilized version [9] [10], greedy Sinkhorn [22] and [Screening Sinkhorn [26] ](https://pythonot.github.io/auto_examples/plot_screenkhorn_1D.html). * Bregman projections for [Wasserstein barycenter](https://pythonot.github.io/auto_examples/barycenters/plot_barycenter_lp_vs_entropic.html) [3], [convolutional barycenter](https://pythonot.github.io/auto_examples/barycenters/plot_convolutional_barycenter.html) [21] and unmixing [4]. * Sinkhorn divergence [23] and entropic regularization OT from empirical data. * [Smooth optimal transport solvers](https://pythonot.github.io/auto_examples/plot_OT_1D_smooth.html) (dual and semi-dual) for KL and squared L2 regularizations [17]. @@ -33,6 +33,7 @@ POT provides the following generic OT solvers (links to examples): * [Partial Wasserstein and Gromov-Wasserstein](https://pythonot.github.io/auto_examples/unbalanced-partial/plot_partial_wass_and_gromov.html) (exact [29] and entropic [3] formulations). * [Sliced Wasserstein](https://pythonot.github.io/auto_examples/sliced-wasserstein/plot_variance.html) [31, 32]. +* [Several backends](https://pythonot.github.io/quickstart.html#solving-ot-with-multiple-backends) for easy use of POT with [Pytorch](https://pytorch.org/)/[jax](https://github.com/google/jax)/[Numpy](https://numpy.org/) arrays. POT provides the following Machine Learning related solvers: @@ -77,8 +78,7 @@ The library has been tested on Linux, MacOSX and Windows. It requires a C++ comp - Numpy (>=1.16) - Scipy (>=1.0) -- Cython (>=0.23) -- Matplotlib (>=1.5) +- Cython (>=0.23) (build only, not necessary when installing wheels from pip or conda) #### Pip installation @@ -129,7 +129,7 @@ Some sub-modules require additional dependences which are discussed below pip install pymanopt autograd ``` -* **ot.gpu** (GPU accelerated OT) depends on cupy that have to be installed following instructions on [this page](https://docs-cupy.chainer.org/en/stable/install.html). Obviously you will need CUDA installed and a compatible GPU. +* **ot.gpu** (GPU accelerated OT) depends on cupy that have to be installed following instructions on [this page](https://docs-cupy.chainer.org/en/stable/install.html). Obviously you will need CUDA installed and a compatible GPU. Note that this module is deprecated since version 0.8 and will be deleted in the future. GPU is now handled automatically through the backends and several solver already can run on GPU using the Pytorch backend. ## Examples |