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Diffstat (limited to 'docs')
-rw-r--r-- | docs/source/readme.rst | 54 |
1 files changed, 27 insertions, 27 deletions
diff --git a/docs/source/readme.rst b/docs/source/readme.rst index 4862523..b00d7b7 100644 --- a/docs/source/readme.rst +++ b/docs/source/readme.rst @@ -15,82 +15,82 @@ 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>`__ + solver <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>`__ + gradient <auto_examples/plot_optim_OTreg.html>`__ [6] and `Generalized conditional - gradient <https://pythonot.github.io/auto_examples/plot_optim_OTreg.html>`__ + gradient <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>`__ + Algorithm <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>`__ + [26] <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>`__ + barycenter <auto_examples/plot_barycenter_lp_vs_entropic.html>`__ [3], `convolutional - barycenter <https://pythonot.github.io/auto_examples/plot_convolutional_barycenter.html>`__ + barycenter <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>`__ + solvers <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>`__) + [16] <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>`__ + distances <auto_examples/plot_gromov.html>`__ and `GW - barycenters <https://pythonot.github.io/auto_examples/plot_gromov_barycenter.html>`__ + barycenters <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>`__ + solver <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>`__ + barycenters <auto_examples/plot_barycenter_fgw.html>`__ [24] - `Stochastic - solver <https://pythonot.github.io/auto_examples/plot_stochastic.html>`__ + solver <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>`__ + barycenters <auto_examples/plot_free_support_barycenter.html>`__ [20]. - `Unbalanced - OT <https://pythonot.github.io/auto_examples/plot_UOT_1D.html>`__ + OT <auto_examples/plot_UOT_1D.html>`__ with KL relaxation and - `barycenter <https://pythonot.github.io/auto_examples/plot_UOT_barycenter_1D.html>`__ + `barycenter <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>`__ + Gromov-Wasserstein <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>`__ + adaptation <auto_examples/plot_otda_classes.html>`__ with `group lasso - regularization <https://pythonot.github.io/auto_examples/plot_otda_classes.html>`__, + regularization <auto_examples/plot_otda_classes.html>`__, `Laplacian - regularization <https://pythonot.github.io/auto_examples/plot_otda_laplacian.html>`__ + regularization <auto_examples/plot_otda_laplacian.html>`__ [5] [30] and `semi supervised - setting <https://pythonot.github.io/auto_examples/plot_otda_semi_supervised.html>`__. + setting <auto_examples/plot_otda_semi_supervised.html>`__. - `Linear OT - mapping <https://pythonot.github.io/auto_examples/plot_otda_linear_mapping.html>`__ + mapping <auto_examples/plot_otda_linear_mapping.html>`__ [14] and `Joint OT mapping - estimation <https://pythonot.github.io/auto_examples/plot_otda_mapping.html>`__ + estimation <auto_examples/plot_otda_mapping.html>`__ [8]. - `Wasserstein Discriminant - Analysis <https://pythonot.github.io/auto_examples/plot_WDA.html>`__ + Analysis <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>`__ + shift <auto_examples/plot_otda_jcpot.html>`__ [27]. Some demonstrations are available in the -`documentation <https://pythonot.github.io/auto_examples/index.html>`__. +`documentation <auto_examples/index.html>`__. Using and citing the toolbox ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ |