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diff --git a/docs/source/readme.rst b/docs/source/readme.rst index 76d37a4..4862523 100644 --- a/docs/source/readme.rst +++ b/docs/source/readme.rst @@ -10,31 +10,84 @@ machine learning. Website and documentation: https://PythonOT.github.io/ -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]. +Source Code (MIT): https://github.com/PythonOT/POT + +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). +- Bregman projections for `Wasserstein + barycenter <https://pythonot.github.io/auto_examples/plot_barycenter_lp_vs_entropic.html>`__ + [3], `convolutional + barycenter <https://pythonot.github.io/auto_examples/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]. +- Non regularized `Wasserstein barycenters + [16] <https://pythonot.github.io/auto_examples/plot_barycenter_lp_vs_entropic.html>`__) + with LP solver (only small scale). +- `Gromov-Wasserstein + distances <https://pythonot.github.io/auto_examples/plot_gromov.html>`__ + and `GW + barycenters <https://pythonot.github.io/auto_examples/plot_gromov_barycenter.html>`__ + (exact [13] and regularized [12]) +- `Fused-Gromov-Wasserstein distances + solver <https://pythonot.github.io/auto_examples/plot_fgw.html#sphx-glr-auto-examples-plot-fgw-py>`__ + and `FGW + barycenters <https://pythonot.github.io/auto_examples/plot_barycenter_fgw.html>`__ + [24] +- `Stochastic + solver <https://pythonot.github.io/auto_examples/plot_stochastic.html>`__ + for Large-scale Optimal Transport (semi-dual problem [18] and dual + problem [19]) +- Non regularized `free support Wasserstein + barycenters <https://pythonot.github.io/auto_examples/plot_free_support_barycenter.html>`__ + [20]. +- `Unbalanced + OT <https://pythonot.github.io/auto_examples/plot_UOT_1D.html>`__ + with KL relaxation and + `barycenter <https://pythonot.github.io/auto_examples/plot_UOT_barycenter_1D.html>`__ + [10, 25]. +- `Partial Wasserstein and + Gromov-Wasserstein <https://pythonot.github.io/auto_examples/plot_partial_wass_and_gromov.html>`__ + (exact [29] and entropic [3] formulations). + +POT provides the following Machine Learning related solvers: + +- `Optimal transport for domain + adaptation <https://pythonot.github.io/auto_examples/plot_otda_classes.html>`__ + with `group lasso + regularization <https://pythonot.github.io/auto_examples/plot_otda_classes.html>`__, + `Laplacian + regularization <https://pythonot.github.io/auto_examples/plot_otda_laplacian.html>`__ + [5] [30] and `semi supervised + setting <https://pythonot.github.io/auto_examples/plot_otda_semi_supervised.html>`__. +- `Linear OT + mapping <https://pythonot.github.io/auto_examples/plot_otda_linear_mapping.html>`__ + [14] and `Joint OT mapping + estimation <https://pythonot.github.io/auto_examples/plot_otda_mapping.html>`__ + [8]. +- `Wasserstein Discriminant + Analysis <https://pythonot.github.io/auto_examples/plot_WDA.html>`__ + [11] (requires autograd + pymanopt). +- `JCPOT algorithm for multi-source domain adaptation with target + shift <https://pythonot.github.io/auto_examples/plot_otda_jcpot.html>`__ + [27]. Some demonstrations are available in the `documentation <https://pythonot.github.io/auto_examples/index.html>`__. @@ -47,10 +100,16 @@ POT using the following bibtex reference: :: + RĂ©mi Flamary and Nicolas Courty, POT Python Optimal Transport library, Website: https://pythonot.github.io/, 2017 + +In Bibtex format: + +:: + @misc{flamary2017pot, title={POT Python Optimal Transport library}, author={Flamary, R{'e}mi and Courty, Nicolas}, - url={https://github.com/rflamary/POT}, + url={https://pythonot.github.io/}, year={2017} } |