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<section id="releases">
<h1>Releases<a class="headerlink" href="#releases" title="Link to this heading"></a></h1>
<section id="id1">
<h2>0.9.6<a class="headerlink" href="#id1" title="Link to this heading"></a></h2>
<p><em>September 2025</em></p>
<p>This new release contains several new features and bug fixes. Among the new features we have a new submodule <code class="docutils literal notranslate"><span class="pre">ot.batch</span></code> that contains batch parallel solvers for several OT problems including <a class="reference external" href="https://pythonot.github.io/master/auto_examples/backends/plot_ot_batch.html">Sinkhorn, Gromov-Wasserstein and Fused Gromov-Wasserstein</a>. This new submodule can be used to solve many independent OT problems in parallel on CPU or GPU with shared source and target support sizes. We also implemented a new Nystrom kernel approximation for the Sinkhorn solver that can be used to speed up the computation of the Sinkhorn divergence on large datasets. We also added new 1D solvers for <a class="reference external" href="https://pythonot.github.io/master/auto_examples/sliced-wasserstein/plot_compute_wasserstein_circle.html">Linear circular OT</a> and new solvers for free support <a class="reference external" href="https://pythonot.github.io/master/auto_examples/barycenters/plot_free_support_barycenter_generic_cost.html">OT barycenters with generic cost functions</a> and for <a class="reference external" href="https://pythonot.github.io/master/auto_examples/barycenters/plot_gmm_barycenter.html">barycenters between Gaussian Mixture Models (GMMs)</a>. We also implemented two solvers for partial Fused Gromov-Wasserstein problems based on <a class="reference external" href="https://pythonot.github.io/master/gen_modules/ot.gromov.html#ot.gromov.partial_fused_gromov_wasserstein">conditional gradient</a> and <a class="reference external" href="https://pythonot.github.io/master/gen_modules/ot.gromov.html#ot.gromov.entropic_partial_fused_gromov_wasserstein">projected gradient</a> descents.</p>
<p>Finally we have updated the documentation to reflect the new generic API and reorganized the <a class="reference external" href="https://pythonot.github.io/auto_examples/index.html">examples gallery</a>.</p>
<section id="new-features">
<h3>New features<a class="headerlink" href="#new-features" title="Link to this heading"></a></h3>
<ul class="simple">
<li><p>Implement CG solvers for partial FGW (<a class="reference external" href="https://github.com/PythonOT/POT/pull/687">PR #687</a>)</p></li>
<li><p>Added feature <code class="docutils literal notranslate"><span class="pre">grad=last_step</span></code> for <code class="docutils literal notranslate"><span class="pre">ot.solvers.solve</span></code> (<a class="reference external" href="https://github.com/PythonOT/POT/pull/693">PR #693</a>)</p></li>
<li><p>Automatic PR labeling and release file update check (<a class="reference external" href="https://github.com/PythonOT/POT/pull/704">PR #704</a>)</p></li>
<li><p>Reorganize sub-module <code class="docutils literal notranslate"><span class="pre">ot/lp/__init__.py</span></code> into separate files (<a class="reference external" href="https://github.com/PythonOT/POT/pull/714">PR #714</a>)</p></li>
<li><p>Implement fixed-point solver for OT barycenters with generic cost functions
(generalizes <code class="docutils literal notranslate"><span class="pre">ot.lp.free_support_barycenter</span></code>), with example. (<a class="reference external" href="https://github.com/PythonOT/POT/pull/715">PR #715</a>)</p></li>
<li><p>Implement fixed-point solver for barycenters between GMMs (<a class="reference external" href="https://github.com/PythonOT/POT/pull/715">PR #715</a>), with example.</p></li>
<li><p>Fix warning raise when import the library (<a class="reference external" href="https://github.com/PythonOT/POT/pull/716">PR #716</a>)</p></li>
<li><p>Implement projected gradient descent solvers for entropic partial FGW (<a class="reference external" href="https://github.com/PythonOT/POT/pull/702">PR #702</a>)</p></li>
<li><p>Fix documentation in the module <code class="docutils literal notranslate"><span class="pre">ot.gaussian</span></code> (<a class="reference external" href="https://github.com/PythonOT/POT/pull/718">PR #718</a>)</p></li>
<li><p>Refactored <code class="docutils literal notranslate"><span class="pre">ot.bregman._convolutional</span></code> to improve readability (<a class="reference external" href="https://github.com/PythonOT/POT/pull/709">PR #709</a>)</p></li>
<li><p>Added <code class="docutils literal notranslate"><span class="pre">ot.gaussian.bures_barycenter_gradient_descent</span></code> (<a class="reference external" href="https://github.com/PythonOT/POT/pull/680">PR #680</a>)</p></li>
<li><p>Added <code class="docutils literal notranslate"><span class="pre">ot.gaussian.bures_wasserstein_distance</span></code> (<a class="reference external" href="https://github.com/PythonOT/POT/pull/680">PR #680</a>)</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">ot.gaussian.bures_wasserstein_distance</span></code> can be batched (<a class="reference external" href="https://github.com/PythonOT/POT/pull/680">PR #680</a>)</p></li>
<li><p>Backend implementation of <code class="docutils literal notranslate"><span class="pre">ot.dist</span></code> for (<a class="reference external" href="https://github.com/PythonOT/POT/pull/701">PR #701</a>)</p></li>
<li><p>Updated documentation Quickstart guide and User guide with new API (<a class="reference external" href="https://github.com/PythonOT/POT/pull/726">PR #726</a>)</p></li>
<li><p>Fix jax version for auto-grad (<a class="reference external" href="https://github.com/PythonOT/POT/pull/732">PR #732</a>)</p></li>
<li><p>Add Nystrom kernel approximation for Sinkhorn (<a class="reference external" href="https://github.com/PythonOT/POT/pull/742">PR #742</a>)</p></li>
<li><p>Added <code class="docutils literal notranslate"><span class="pre">ot.solver_1d.linear_circular_ot</span></code> and <code class="docutils literal notranslate"><span class="pre">ot.sliced.linear_sliced_wasserstein_sphere</span></code> (<a class="reference external" href="https://github.com/PythonOT/POT/pull/736">PR #736</a>)</p></li>
<li><p>Implement 1d solver for partial optimal transport (<a class="reference external" href="https://github.com/PythonOT/POT/pull/741">PR #741</a>)</p></li>
<li><p>Fix reg_div function compatibility with numpy in <code class="docutils literal notranslate"><span class="pre">ot.unbalanced.lbfgsb_unbalanced</span></code> via new function <code class="docutils literal notranslate"><span class="pre">ot.utils.fun_to_numpy</span></code> (<a class="reference external" href="https://github.com/PythonOT/POT/pull/731">PR #731</a>)</p></li>
<li><p>Added to each example in the examples gallery the information about the release version in which it was introduced (<a class="reference external" href="https://github.com/PythonOT/POT/pull/743">PR #743</a>)</p></li>
<li><p>Removed release information from quickstart guide (<a class="reference external" href="https://github.com/PythonOT/POT/pull/744">PR #744</a>)</p></li>
<li><p>Implement batch parallel solvers in ot.batch (<a class="reference external" href="https://github.com/PythonOT/POT/pull/745">PR #745</a>)</p></li>
<li><p>Update REAMDE with new API and reorganize examples (<a class="reference external" href="https://github.com/PythonOT/POT/pull/754">PR #754</a>)</p></li>
<li><p>Speedup and update tests and wheels (<a class="reference external" href="https://github.com/PythonOT/POT/pull/759">PR #759</a>)</p></li>
</ul>
</section>
<section id="closed-issues">
<h3>Closed issues<a class="headerlink" href="#closed-issues" title="Link to this heading"></a></h3>
<ul class="simple">
<li><p>Fixed <code class="docutils literal notranslate"><span class="pre">ot.mapping</span></code> solvers which depended on deprecated <code class="docutils literal notranslate"><span class="pre">cvxpy</span></code> <code class="docutils literal notranslate"><span class="pre">ECOS</span></code> solver (<a class="reference external" href="https://github.com/PythonOT/POT/pull/692">PR #692</a>, <a class="reference external" href="https://github.com/PythonOT/POT/issues/668">Issue #668</a>)</p></li>
<li><p>Fixed numerical errors in <code class="docutils literal notranslate"><span class="pre">ot.gmm</span></code> (<a class="reference external" href="https://github.com/PythonOT/POT/pull/690">PR #690</a>, <a class="reference external" href="https://github.com/PythonOT/POT/issues/689">Issue #689</a>)</p></li>
<li><p>Add version number to the documentation (<a class="reference external" href="https://github.com/PythonOT/POT/pull/696">PR #696</a>)</p></li>
<li><p>Update doc for default regularization in <code class="docutils literal notranslate"><span class="pre">ot.unbalanced</span></code> sinkhorn solvers (<a class="reference external" href="https://github.com/PythonOT/POT/issues/691">Issue #691</a>, <a class="reference external" href="https://github.com/PythonOT/POT/pull/700">PR #700</a>)</p></li>
<li><p>Clean documentation for <code class="docutils literal notranslate"><span class="pre">gromov</span></code>, <code class="docutils literal notranslate"><span class="pre">lp</span></code> and <code class="docutils literal notranslate"><span class="pre">unbalanced</span></code> folders (<a class="reference external" href="https://github.com/PythonOT/POT/pull/710">PR #710</a>)</p></li>
<li><p>Clean references in documentation (<a class="reference external" href="https://github.com/PythonOT/POT/pull/722">PR #722</a>)</p></li>
<li><p>Clean documentation for <code class="docutils literal notranslate"><span class="pre">ot.gromov.gromov_wasserstein</span></code> (<a class="reference external" href="https://github.com/PythonOT/POT/pull/737">PR #737</a>)</p></li>
<li><p>Debug wheels building (<a class="reference external" href="https://github.com/PythonOT/POT/pull/739">PR #739</a>)</p></li>
<li><p>Fix doc for projection sparse simplex (<a class="reference external" href="https://github.com/PythonOT/POT/pull/734">PR #734</a>, PR #746)</p></li>
<li><p>Changed the default behavior of <code class="docutils literal notranslate"><span class="pre">ot.lp.solver_1d.wasserstein_circle</span></code> (<a class="reference external" href="https://github.com/PythonOT/POT/issues/738">Issue #738</a>)</p></li>
<li><p>Avoid raising unnecessary warnings in <code class="docutils literal notranslate"><span class="pre">ot.lp.solver_1d.binary_search_circle</span></code> (<a class="reference external" href="https://github.com/PythonOT/POT/issues/738">Issue #738</a>)</p></li>
<li><p>Avoid deprecation warning in <code class="docutils literal notranslate"><span class="pre">ot.lp.solver_1d.wasserstein_1d</span></code> (<a class="reference external" href="https://github.com/PythonOT/POT/issues/760">Issue #760</a>, <a class="reference external" href="https://github.com/PythonOT/POT/pull/761">PR #761</a>)</p></li>
</ul>
</section>
</section>
<section id="id2">
<h2>0.9.5<a class="headerlink" href="#id2" title="Link to this heading"></a></h2>
<p><em>November 2024</em></p>
<p>This new release contains several new features, starting with
a novel <a class="reference external" href="https://pythonot.github.io/master/gen_modules/ot.gmm.html#examples-using-ot-gmm-gmm-ot-apply-map">Gaussian Mixture Model Optimal Transport (GMM-OT)</a> solver to compare GMM while enforcing the transport plan to remain a GMM, that benefits from a closed-form solution making it practical for high-dimensional matching problems. We also extended our general unbalanced OT solvers to support any non-negative reference measure in the regularization terms, before adding the novel <a class="reference external" href="https://pythonot.github.io/master/auto_examples/unbalanced-partial/plot_conv_sinkhorn_ti.html">translation invariant UOT</a> solver showcasing a higher convergence speed. We also implemented several new solvers and enhanced existing ones to perform OT across spaces. These include a <a class="reference external" href="https://pythonot.github.io/master/auto_examples/gromov/plot_semirelaxed_gromov_wasserstein_barycenter.html">semi-relaxed FGW barycenter</a> solver, coupled with new initialization heuristics for the inner divergence computation, to perform graph partitioning or dictionary learning. Followed by novel <a class="reference external" href="https://pythonot.github.io/master/auto_examples/others/plot_outlier_detection_with_COOT_and_unbalanced_COOT.html">unbalanced FGW and Co-optimal transport</a> solvers to promote robustness to outliers in such matching problems. And we finally updated the implementation of partial GW now supporting asymmetric structures and the KL divergence, while leveraging a new generic conditional gradient solver for partial transport problems enabling significant speed improvements. These latest updates required some modifications to the line search functions of our generic conditional gradient solver, paving the way for future improvements to other GW-based solvers. Last but not least, we implemented a pre-commit scheme to automatically correct common programming mistakes likely to be made by our future contributors.</p>
<p>This release also contains few bug fixes, concerning the support of any metric in <code class="docutils literal notranslate"><span class="pre">ot.emd_1d</span></code> / <code class="docutils literal notranslate"><span class="pre">ot.emd2_1d</span></code>, and the support of any weights in <code class="docutils literal notranslate"><span class="pre">ot.gaussian</span></code>.</p>
<section id="breaking-change">
<h3>Breaking change<a class="headerlink" href="#breaking-change" title="Link to this heading"></a></h3>
<ul class="simple">
<li><p>Custom functions provided as parameter <code class="docutils literal notranslate"><span class="pre">line_search</span></code> to <code class="docutils literal notranslate"><span class="pre">ot.optim.generic_conditional_gradient</span></code> must now have the signature <code class="docutils literal notranslate"><span class="pre">line_search(cost,</span> <span class="pre">G,</span> <span class="pre">deltaG,</span> <span class="pre">Mi,</span> <span class="pre">cost_G,</span> <span class="pre">df_G,</span> <span class="pre">**kwargs)</span></code>, adding as input <code class="docutils literal notranslate"><span class="pre">df_G</span></code> the gradient of the regularizer evaluated at the transport plan <code class="docutils literal notranslate"><span class="pre">G</span></code>. This change aims at improving speed of solvers having quadratic polynomial functions as regularizer such as the Gromov-Wassertein loss (<a class="reference external" href="https://github.com/PythonOT/POT/pull/663">PR #663</a>).</p></li>
</ul>
</section>
<section id="id3">
<h3>New features<a class="headerlink" href="#id3" title="Link to this heading"></a></h3>
<ul class="simple">
<li><p>New linter based on pre-commit using ruff, codespell and yamllint (<a class="reference external" href="https://github.com/PythonOT/POT/pull/681">PR #681</a>)</p></li>
<li><p>Added feature <code class="docutils literal notranslate"><span class="pre">mass=True</span></code> for <code class="docutils literal notranslate"><span class="pre">nx.kl_div</span></code> (<a class="reference external" href="https://github.com/PythonOT/POT/pull/654">PR #654</a>)</p></li>
<li><p>Implemented Gaussian Mixture Model OT <code class="docutils literal notranslate"><span class="pre">ot.gmm</span></code> (<a class="reference external" href="https://github.com/PythonOT/POT/pull/649">PR #649</a>)</p></li>
<li><p>Added feature <code class="docutils literal notranslate"><span class="pre">semirelaxed_fgw_barycenters</span></code> and generic FGW-related barycenter updates <code class="docutils literal notranslate"><span class="pre">update_barycenter_structure</span></code> and <code class="docutils literal notranslate"><span class="pre">update_barycenter_feature</span></code> (<a class="reference external" href="https://github.com/PythonOT/POT/pull/659">PR #659</a>)</p></li>
<li><p>Added initialization heuristics for sr(F)GW problems via <code class="docutils literal notranslate"><span class="pre">semirelaxed_init_plan</span></code>, integrated in all sr(F)GW solvers (<a class="reference external" href="https://github.com/PythonOT/POT/pull/659">PR #659</a>)</p></li>
<li><p>Improved <code class="docutils literal notranslate"><span class="pre">ot.plot.plot1D_mat</span></code> (<a class="reference external" href="https://github.com/PythonOT/POT/pull/649">PR #649</a>)</p></li>
<li><p>Added <code class="docutils literal notranslate"><span class="pre">nx.det</span></code> (<a class="reference external" href="https://github.com/PythonOT/POT/pull/649">PR #649</a>)</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">nx.sqrtm</span></code> is now broadcastable (takes …, d, d) inputs (<a class="reference external" href="https://github.com/PythonOT/POT/pull/649">PR #649</a>)</p></li>
<li><p>Restructured <code class="docutils literal notranslate"><span class="pre">ot.unbalanced</span></code> module (<a class="reference external" href="https://github.com/PythonOT/POT/pull/658">PR #658</a>)</p></li>
<li><p>Added <code class="docutils literal notranslate"><span class="pre">ot.unbalanced.lbfgsb_unbalanced2</span></code> and add flexible reference measure <code class="docutils literal notranslate"><span class="pre">c</span></code> in all unbalanced solvers (<a class="reference external" href="https://github.com/PythonOT/POT/pull/658">PR #658</a>)</p></li>
<li><p>Implemented Fused unbalanced Gromov-Wasserstein and unbalanced Co-Optimal Transport (<a class="reference external" href="https://github.com/PythonOT/POT/pull/677">PR #677</a>)</p></li>
<li><p>Notes before depreciating partial Gromov-Wasserstein function in <code class="docutils literal notranslate"><span class="pre">ot.partial</span></code> moved to ot.gromov (<a class="reference external" href="https://github.com/PythonOT/POT/pull/663">PR #663</a>)</p></li>
<li><p>Create <code class="docutils literal notranslate"><span class="pre">ot.gromov._partial</span></code> add new features <code class="docutils literal notranslate"><span class="pre">loss_fun</span> <span class="pre">=</span> <span class="pre">"kl_loss"</span></code> and <code class="docutils literal notranslate"><span class="pre">symmetry=False</span></code> to all solvers while increasing speed + updating adequatly <code class="docutils literal notranslate"><span class="pre">ot.solvers</span></code> (<a class="reference external" href="https://github.com/PythonOT/POT/pull/663">PR #663</a>)</p></li>
<li><p>Added <code class="docutils literal notranslate"><span class="pre">ot.unbalanced.sinkhorn_unbalanced_translation_invariant</span></code> (<a class="reference external" href="https://github.com/PythonOT/POT/pull/676">PR #676</a>)</p></li>
</ul>
</section>
<section id="id4">
<h3>Closed issues<a class="headerlink" href="#id4" title="Link to this heading"></a></h3>
<ul class="simple">
<li><p>Fixed <code class="docutils literal notranslate"><span class="pre">ot.gaussian</span></code> ignoring weights when computing means (<a class="reference external" href="https://github.com/PythonOT/POT/pull/649">PR #649</a>, <a class="reference external" href="https://github.com/PythonOT/POT/issues/648">Issue #648</a>)</p></li>
<li><p>Fixed <code class="docutils literal notranslate"><span class="pre">ot.emd_1d</span></code> and <code class="docutils literal notranslate"><span class="pre">ot.emd2_1d</span></code> incorrectly allowing any metric (<a class="reference external" href="https://github.com/PythonOT/POT/pull/670">PR #670</a>, <a class="reference external" href="https://github.com/PythonOT/POT/issues/669">Issue #669</a>)</p></li>
</ul>
</section>
</section>
<section id="id5">
<h2>0.9.4<a class="headerlink" href="#id5" title="Link to this heading"></a></h2>
<p><em>June 2024</em></p>
<p>This new release contains several new features and bug fixes. Among the new features
we have novel <a class="reference external" href="https://pythonot.github.io/auto_examples/gromov/plot_quantized_gromov_wasserstein.html">Quantized FGW</a> solvers that can be used to speed up the computation of the FGW loss on large datasets or to promote a structure on the pairwise matrices. We also updated the continuous entropic mapping to provide efficient out-of-sample continuous mapping thanks to entropic regularization. We also have a new general unbalanced solvers for <code class="docutils literal notranslate"><span class="pre">ot.solve</span></code> and BFGS solver and illustrative example. Finally we have a new solver for the <a class="reference external" href="https://pythonot.github.io/auto_examples/others/plot_lowrank_GW.html">Low Rank Gromov-Wasserstein</a> that can be used to compute the GW distance between two large scale datasets with a low rank approximation.</p>
<p>From a maintenance point of view, we now have a new option to install optional dependencies with <code class="docutils literal notranslate"><span class="pre">pip</span> <span class="pre">install</span> <span class="pre">POT[all]</span></code> and the specific backends or submodules’ dependencies may also be installed individually. The pip options are: <code class="docutils literal notranslate"><span class="pre">backend-jax,</span> <span class="pre">backend-tf,</span> <span class="pre">backend-torch,</span> <span class="pre">cvxopt,</span> <span class="pre">dr,</span> <span class="pre">gnn,</span> <span class="pre">plot,</span> <span class="pre">all</span></code>. We also provide with this release support for NumPy 2.0 (the wheels should now be compatible with NumPy 2.0 and below). We also fixed several issues such as gradient sign errors for FGW solvers, empty weights for <code class="docutils literal notranslate"><span class="pre">ot.emd2</span></code>, and line-search in partial GW. We also split the <code class="docutils literal notranslate"><span class="pre">test/test_gromov.py</span></code> into <code class="docutils literal notranslate"><span class="pre">test/gromov/</span></code> to make the tests more manageable.</p>
<section id="id6">
<h3>New features<a class="headerlink" href="#id6" title="Link to this heading"></a></h3>
<ul class="simple">
<li><p>NumPy 2.0 support is added (<a class="reference external" href="https://github.com/PythonOT/POT/pull/629">PR #629</a>)</p></li>
<li><p>New quantized FGW solvers <code class="docutils literal notranslate"><span class="pre">ot.gromov.quantized_fused_gromov_wasserstein</span></code>, <code class="docutils literal notranslate"><span class="pre">ot.gromov.quantized_fused_gromov_wasserstein_samples</span></code> and <code class="docutils literal notranslate"><span class="pre">ot.gromov.quantized_fused_gromov_wasserstein_partitioned</span></code> (<a class="reference external" href="https://github.com/PythonOT/POT/pull/603">PR #603</a>)</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">ot.gromov._gw.solve_gromov_linesearch</span></code> now has an argument to specify if the matrices are symmetric in which case the computation can be done faster (<a class="reference external" href="https://github.com/PythonOT/POT/pull/607">PR #607</a>).</p></li>
<li><p>Continuous entropic mapping (<a class="reference external" href="https://github.com/PythonOT/POT/pull/613">PR #613</a>)</p></li>
<li><p>New general unbalanced solvers for <code class="docutils literal notranslate"><span class="pre">ot.solve</span></code> and BFGS solver and illustrative example (<a class="reference external" href="https://github.com/PythonOT/POT/pull/620">PR #620</a>)</p></li>
<li><p>Add gradient computation with envelope theorem to sinkhorn solver of <code class="docutils literal notranslate"><span class="pre">ot.solve</span></code> with <code class="docutils literal notranslate"><span class="pre">grad='envelope'</span></code> (<a class="reference external" href="https://github.com/PythonOT/POT/pull/605">PR #605</a>).</p></li>
<li><p>Added support for <a class="reference external" href="https://proceedings.mlr.press/v162/scetbon22b/scetbon22b.pdf">Low rank Gromov-Wasserstein</a> with <code class="docutils literal notranslate"><span class="pre">ot.gromov.lowrank_gromov_wasserstein_samples</span></code> (<a class="reference external" href="https://github.com/PythonOT/POT/pull/614">PR #614</a>)</p></li>
<li><p>Optional dependencies may now be installed with <code class="docutils literal notranslate"><span class="pre">pip</span> <span class="pre">install</span> <span class="pre">POT[all]</span></code> The specific backends or submodules’ dependencies may also be installed individually. The pip options are: <code class="docutils literal notranslate"><span class="pre">backend-jax,</span> <span class="pre">backend-tf,</span> <span class="pre">backend-torch,</span> <span class="pre">cvxopt,</span> <span class="pre">dr,</span> <span class="pre">gnn,</span> <span class="pre">all</span></code>. The installation of the <code class="docutils literal notranslate"><span class="pre">cupy</span></code> backend should be done with conda.</p></li>
</ul>
</section>
<section id="id7">
<h3>Closed issues<a class="headerlink" href="#id7" title="Link to this heading"></a></h3>
<ul class="simple">
<li><p>Fix gpu compatibility of sr(F)GW solvers when <code class="docutils literal notranslate"><span class="pre">G0</span> <span class="pre">is</span> <span class="pre">not</span> <span class="pre">None</span></code>(<a class="reference external" href="https://github.com/PythonOT/POT/pull/596">PR #596</a>)</p></li>
<li><p>Fix doc and example for lowrank sinkhorn (<a class="reference external" href="https://github.com/PythonOT/POT/pull/601">PR #601</a>)</p></li>
<li><p>Fix issue with empty weights for <code class="docutils literal notranslate"><span class="pre">ot.emd2</span></code> (<a class="reference external" href="https://github.com/PythonOT/POT/pull/606">PR #606</a>, <a class="reference external" href="https://github.com/PythonOT/POT/issues/534">Issue #534</a>)</p></li>
<li><p>Fix a sign error regarding the gradient of <code class="docutils literal notranslate"><span class="pre">ot.gromov._gw.fused_gromov_wasserstein2</span></code> and <code class="docutils literal notranslate"><span class="pre">ot.gromov._gw.gromov_wasserstein2</span></code> for the kl loss (<a class="reference external" href="https://github.com/PythonOT/POT/pull/610">PR #610</a>)</p></li>
<li><p>Fix same sign error for sr(F)GW conditional gradient solvers (<a class="reference external" href="https://github.com/PythonOT/POT/pull/611">PR #611</a>)</p></li>
<li><p>Split <code class="docutils literal notranslate"><span class="pre">test/test_gromov.py</span></code> into <code class="docutils literal notranslate"><span class="pre">test/gromov/</span></code> (<a class="reference external" href="https://github.com/PythonOT/POT/pull/619">PR #619</a>)</p></li>
<li><p>Fix (F)GW barycenter functions to support computing barycenter on 1 input + deprecate structures as lists (<a class="reference external" href="https://github.com/PythonOT/POT/pull/628">PR #628</a>)</p></li>
<li><p>Fix line-search in partial GW and change default init to the interior of partial transport plans (<a class="reference external" href="https://github.com/PythonOT/POT/pull/602">PR #602</a>)</p></li>
<li><p>Fix <code class="docutils literal notranslate"><span class="pre">ot.da.sinkhorn_lpl1_mm</span></code> compatibility with JAX (<a class="reference external" href="https://github.com/PythonOT/POT/pull/592">PR #592</a>)</p></li>
<li><p>Fiw linesearch import error on Scipy 1.14 (<a class="reference external" href="https://github.com/PythonOT/POT/pull/642">PR #642</a>, <a class="reference external" href="https://github.com/PythonOT/POT/issues/641">Issue #641</a>)</p></li>
<li><p>Upgrade supported JAX versions from jax<=0.4.24 to jax<=0.4.30 (<a class="reference external" href="https://github.com/PythonOT/POT/pull/643">PR #643</a>)</p></li>
</ul>
</section>
</section>
<section id="id8">
<h2>0.9.3<a class="headerlink" href="#id8" title="Link to this heading"></a></h2>
<p><em>January 2024</em></p>
<section id="id9">
<h3>Closed issues<a class="headerlink" href="#id9" title="Link to this heading"></a></h3>
<ul class="simple">
<li><p>Fixed an issue with cost correction for mismatched labels in <code class="docutils literal notranslate"><span class="pre">ot.da.BaseTransport</span></code> fit methods. This fix addresses the original issue introduced <a class="reference external" href="https://github.com/PythonOT/POT/pull/587">PR #587</a> (PR #593)</p></li>
</ul>
</section>
</section>
<section id="id10">
<h2>0.9.2<a class="headerlink" href="#id10" title="Link to this heading"></a></h2>
<p><em>December 2023</em></p>
<p>This new release contains several new features and bug fixes. Among the new features
we have a new solver for estimation of nearest Brenier potentials (SSNB) that can be used for OT mapping estimation (on small problems), new Bregman Alternated Projected Gradient solvers for GW and FGW, and new solvers for Bures-Wasserstein barycenters. We also provide a first solver for Low Rank Sinkhorn that will be ussed to provide low rak OT extensions in the next releases. Finally we have a new exact line-search for (F)GW solvers with KL loss that can be used to improve the convergence of the solvers.</p>
<p>We also have a new <code class="docutils literal notranslate"><span class="pre">LazyTensor</span></code> class that can be used to model OT plans and low rank tensors in large scale OT. This class is used to return the plan for the new wrapper for <code class="docutils literal notranslate"><span class="pre">geomloss</span></code> Sinkhorn solver on empirical samples that can lead to x10/x100 speedups on CPU or GPU and have a lazy implementation that allows solving very large problems of a few millions samples.</p>
<p>We also have a new API for solving OT problems from empirical samples with <code class="docutils literal notranslate"><span class="pre">ot.solve_sample</span></code> Finally we have a new API for Gromov-Wasserstein solvers with <code class="docutils literal notranslate"><span class="pre">ot.solve_gromov</span></code> function that centralizes most of the (F)GW methods with unified notation. Some example of how to use the new API below:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Generate random data</span>
<span class="n">xs</span><span class="p">,</span> <span class="n">xt</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">50</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
<span class="c1"># Solve OT problem with empirical samples</span>
<span class="n">sol</span> <span class="o">=</span> <span class="n">ot</span><span class="o">.</span><span class="n">solve_sample</span><span class="p">(</span><span class="n">xs</span><span class="p">,</span> <span class="n">xt</span><span class="p">)</span> <span class="c1"># Exact OT betwen smaples with uniform weights</span>
<span class="n">sol</span> <span class="o">=</span> <span class="n">ot</span><span class="o">.</span><span class="n">solve_sample</span><span class="p">(</span><span class="n">xs</span><span class="p">,</span> <span class="n">xt</span><span class="p">,</span> <span class="n">wa</span><span class="p">,</span> <span class="n">wb</span><span class="p">)</span> <span class="c1"># Exact OT with weights given by user</span>
<span class="n">sol</span> <span class="o">=</span> <span class="n">ot</span><span class="o">.</span><span class="n">solve_sample</span><span class="p">(</span><span class="n">xs</span><span class="p">,</span> <span class="n">xt</span><span class="p">,</span> <span class="n">reg</span><span class="o">=</span> <span class="mi">1</span><span class="p">,</span> <span class="n">metric</span><span class="o">=</span><span class="s1">'euclidean'</span><span class="p">)</span> <span class="c1"># sinkhorn with euclidean metric</span>
<span class="n">sol</span> <span class="o">=</span> <span class="n">ot</span><span class="o">.</span><span class="n">solve_sample</span><span class="p">(</span><span class="n">xs</span><span class="p">,</span> <span class="n">xt</span><span class="p">,</span> <span class="n">reg</span><span class="o">=</span> <span class="mi">1</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">'geomloss'</span><span class="p">)</span> <span class="c1"># faster sinkhorn solver on CPU/GPU</span>
<span class="n">sol</span> <span class="o">=</span> <span class="n">ot</span><span class="o">.</span><span class="n">solve_sample</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">x2</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">'factored'</span><span class="p">,</span> <span class="n">rank</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span> <span class="c1"># compute factored OT</span>
<span class="n">sol</span> <span class="o">=</span> <span class="n">ot</span><span class="o">.</span><span class="n">solve_sample</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">x2</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">'lowrank'</span><span class="p">,</span> <span class="n">rank</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span> <span class="c1"># compute lowrank sinkhorn OT</span>
<span class="n">value_bw</span> <span class="o">=</span> <span class="n">ot</span><span class="o">.</span><span class="n">solve_sample</span><span class="p">(</span><span class="n">xs</span><span class="p">,</span> <span class="n">xt</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">'gaussian'</span><span class="p">)</span><span class="o">.</span><span class="n">value</span> <span class="c1"># Bures-Wasserstein distance</span>
<span class="c1"># Solve GW problem</span>
<span class="n">Cs</span><span class="p">,</span> <span class="n">Ct</span> <span class="o">=</span> <span class="n">ot</span><span class="o">.</span><span class="n">dist</span><span class="p">(</span><span class="n">xs</span><span class="p">,</span> <span class="n">xs</span><span class="p">),</span> <span class="n">ot</span><span class="o">.</span><span class="n">dist</span><span class="p">(</span><span class="n">xt</span><span class="p">,</span> <span class="n">xt</span><span class="p">)</span> <span class="c1"># compute cost matrices</span>
<span class="n">sol</span> <span class="o">=</span> <span class="n">ot</span><span class="o">.</span><span class="n">solve_gromov</span><span class="p">(</span><span class="n">Cs</span><span class="p">,</span><span class="n">Ct</span><span class="p">)</span> <span class="c1"># Exact GW between samples with uniform weights</span>
<span class="c1"># Solve FGW problem</span>
<span class="n">M</span> <span class="o">=</span> <span class="n">ot</span><span class="o">.</span><span class="n">dist</span><span class="p">(</span><span class="n">xs</span><span class="p">,</span> <span class="n">xt</span><span class="p">)</span> <span class="c1"># compute cost matrix</span>
<span class="c1"># Exact FGW between samples with uniform weights</span>
<span class="n">sol</span> <span class="o">=</span> <span class="n">ot</span><span class="o">.</span><span class="n">solve_gromov</span><span class="p">(</span><span class="n">Cs</span><span class="p">,</span> <span class="n">Ct</span><span class="p">,</span> <span class="n">M</span><span class="p">,</span> <span class="n">loss</span><span class="o">=</span><span class="s1">'KL'</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.7</span><span class="p">)</span> <span class="c1"># FGW with KL data fitting</span>
<span class="c1"># recover solutions objects</span>
<span class="n">P</span> <span class="o">=</span> <span class="n">sol</span><span class="o">.</span><span class="n">plan</span> <span class="c1"># OT plan</span>
<span class="n">u</span><span class="p">,</span> <span class="n">v</span> <span class="o">=</span> <span class="n">sol</span><span class="o">.</span><span class="n">potentials</span> <span class="c1"># dual variables</span>
<span class="n">value</span> <span class="o">=</span> <span class="n">sol</span><span class="o">.</span><span class="n">value</span> <span class="c1"># OT value</span>
<span class="c1"># for GW and FGW</span>
<span class="n">value_linear</span> <span class="o">=</span> <span class="n">sol</span><span class="o">.</span><span class="n">value_linear</span> <span class="c1"># linear part of the loss</span>
<span class="n">value_quad</span> <span class="o">=</span> <span class="n">sol</span><span class="o">.</span><span class="n">value_quad</span> <span class="c1"># quadratic part of the loss</span>
</pre></div>
</div>
<p>Users are encouraged to use the new API (it is much simpler) but it might still be subjects to small changes before the release of POT 1.0 .</p>
<p>We also fixed a number of issues, the most pressing being a problem of GPU memory allocation when pytorch is installed that will not happen now thanks to Lazy initialization of the backends. We now also have the possibility to deactivate some backends using environment which prevents POT from importing them and can lead to large import speedup.</p>
<section id="id11">
<h3>New features<a class="headerlink" href="#id11" title="Link to this heading"></a></h3>
<ul class="simple">
<li><p>Added support for <a class="reference external" href="http://proceedings.mlr.press/v108/paty20a/paty20a.pdf">Nearest Brenier Potentials (SSNB)</a> (<a class="reference external" href="https://github.com/PythonOT/POT/pull/526">PR #526</a>) + minor fix (PR #535)</p></li>
<li><p>Tweaked <code class="docutils literal notranslate"><span class="pre">get_backend</span></code> to ignore <code class="docutils literal notranslate"><span class="pre">None</span></code> inputs (<a class="reference external" href="https://github.com/PythonOT/POT/pull/525">PR #525</a>)</p></li>
<li><p>Callbacks for generalized conditional gradient in <code class="docutils literal notranslate"><span class="pre">ot.da.sinkhorn_l1l2_gl</span></code> are now vectorized to improve performance (<a class="reference external" href="https://github.com/PythonOT/POT/pull/507">PR #507</a>)</p></li>
<li><p>The <code class="docutils literal notranslate"><span class="pre">linspace</span></code> method of the backends now has the <code class="docutils literal notranslate"><span class="pre">type_as</span></code> argument to convert to the same dtype and device. (<a class="reference external" href="https://github.com/PythonOT/POT/pull/533">PR #533</a>)</p></li>
<li><p>The <code class="docutils literal notranslate"><span class="pre">convolutional_barycenter2d</span></code> and <code class="docutils literal notranslate"><span class="pre">convolutional_barycenter2d_debiased</span></code> functions now work with different devices.. (<a class="reference external" href="https://github.com/PythonOT/POT/pull/533">PR #533</a>)</p></li>
<li><p>New API for Gromov-Wasserstein solvers with <code class="docutils literal notranslate"><span class="pre">ot.solve_gromov</span></code> function (<a class="reference external" href="https://github.com/PythonOT/POT/pull/536">PR #536</a>)</p></li>
<li><p>New LP solvers from scipy used by default for LP barycenter (<a class="reference external" href="https://github.com/PythonOT/POT/pull/537">PR #537</a>)</p></li>
<li><p>Update wheels to Python 3.12 and remove old i686 arch that do not have scipy wheels (<a class="reference external" href="https://github.com/PythonOT/POT/pull/543">PR #543</a>)</p></li>
<li><p>Upgraded unbalanced OT solvers for more flexibility (<a class="reference external" href="https://github.com/PythonOT/POT/pull/539">PR #539</a>)</p></li>
<li><p>Add LazyTensor for modeling plans and low rank tensor in large scale OT (<a class="reference external" href="https://github.com/PythonOT/POT/pull/544">PR #544</a>)</p></li>
<li><p>Add exact line-search for <code class="docutils literal notranslate"><span class="pre">gromov_wasserstein</span></code> and <code class="docutils literal notranslate"><span class="pre">fused_gromov_wasserstein</span></code> with KL loss (<a class="reference external" href="https://github.com/PythonOT/POT/pull/556">PR #556</a>)</p></li>
<li><p>Add KL loss to all semi-relaxed (Fused) Gromov-Wasserstein solvers (<a class="reference external" href="https://github.com/PythonOT/POT/pull/559">PR #559</a>)</p></li>
<li><p>Further upgraded unbalanced OT solvers for more flexibility and future use (<a class="reference external" href="https://github.com/PythonOT/POT/pull/551">PR #551</a>)</p></li>
<li><p>New API function <code class="docutils literal notranslate"><span class="pre">ot.solve_sample</span></code> for solving OT problems from empirical samples (<a class="reference external" href="https://github.com/PythonOT/POT/pull/563">PR #563</a>)</p></li>
<li><p>Wrapper for `geomloss`` solver on empirical samples (<a class="reference external" href="https://github.com/PythonOT/POT/pull/571">PR #571</a>)</p></li>
<li><p>Add <code class="docutils literal notranslate"><span class="pre">stop_criterion</span></code> feature to (un)regularized (f)gw barycenter solvers (<a class="reference external" href="https://github.com/PythonOT/POT/pull/578">PR #578</a>)</p></li>
<li><p>Add <code class="docutils literal notranslate"><span class="pre">fixed_structure</span></code> and <code class="docutils literal notranslate"><span class="pre">fixed_features</span></code> to entropic fgw barycenter solver (<a class="reference external" href="https://github.com/PythonOT/POT/pull/578">PR #578</a>)</p></li>
<li><p>Add new BAPG solvers with KL projections for GW and FGW (<a class="reference external" href="https://github.com/PythonOT/POT/pull/581">PR #581</a>)</p></li>
<li><p>Add Bures-Wasserstein barycenter in <code class="docutils literal notranslate"><span class="pre">ot.gaussian</span></code> and example (<a class="reference external" href="https://github.com/PythonOT/POT/pull/582">PR #582</a>, PR #584)</p></li>
<li><p>Domain adaptation method <code class="docutils literal notranslate"><span class="pre">SinkhornL1l2Transport</span></code> now supports JAX backend (<a class="reference external" href="https://github.com/PythonOT/POT/pull/587">PR #587</a>)</p></li>
<li><p>Added support for <a class="reference external" href="https://arxiv.org/pdf/2103.04737.pdf">Low-Rank Sinkhorn Factorization</a> (<a class="reference external" href="https://github.com/PythonOT/POT/pull/568">PR #568</a>)</p></li>
</ul>
</section>
<section id="id12">
<h3>Closed issues<a class="headerlink" href="#id12" title="Link to this heading"></a></h3>
<ul class="simple">
<li><p>Fix line search evaluating cost outside of the interpolation range (<a class="reference external" href="https://github.com/PythonOT/POT/issues/502">Issue #502</a>, <a class="reference external" href="https://github.com/PythonOT/POT/pull/504">PR #504</a>)</p></li>
<li><p>Lazily instantiate backends to avoid unnecessary GPU memory pre-allocations on package import (<a class="reference external" href="https://github.com/PythonOT/POT/issues/516">Issue #516</a>, <a class="reference external" href="https://github.com/PythonOT/POT/pull/520">PR #520</a>)</p></li>
<li><p>Handle documentation and warnings when integers are provided to (f)gw solvers based on cg (<a class="reference external" href="https://github.com/PythonOT/POT/issues/530">Issue #530</a>, <a class="reference external" href="https://github.com/PythonOT/POT/pull/559">PR #559</a>)</p></li>
<li><p>Correct independence of <code class="docutils literal notranslate"><span class="pre">fgw_barycenters</span></code> to <code class="docutils literal notranslate"><span class="pre">init_C</span></code> and <code class="docutils literal notranslate"><span class="pre">init_X</span></code> (<a class="reference external" href="https://github.com/PythonOT/POT/issues/547">Issue #547</a>, <a class="reference external" href="https://github.com/PythonOT/POT/pull/566">PR #566</a>)</p></li>
<li><p>Avoid precision change when computing norm using PyTorch backend (Discussion #570, <a class="reference external" href="https://github.com/PythonOT/POT/pull/572">PR #572</a>)</p></li>
<li><p>Create <code class="docutils literal notranslate"><span class="pre">ot/bregman/</span></code>repository (<a class="reference external" href="https://github.com/PythonOT/POT/issues/567">Issue #567</a>, <a class="reference external" href="https://github.com/PythonOT/POT/pull/569">PR #569</a>)</p></li>
<li><p>Fix matrix feature shape in <code class="docutils literal notranslate"><span class="pre">entropic_fused_gromov_barycenters</span></code>(<a class="reference external" href="https://github.com/PythonOT/POT/issues/574">Issue #574</a>, <a class="reference external" href="https://github.com/PythonOT/POT/pull/573">PR #573</a>)</p></li>
<li><p>Fix (fused) gromov-wasserstein barycenter solvers to support <code class="docutils literal notranslate"><span class="pre">kl_loss</span></code>(<a class="reference external" href="https://github.com/PythonOT/POT/pull/576">PR #576</a>)</p></li>
</ul>
</section>
</section>
<section id="id13">
<h2>0.9.1<a class="headerlink" href="#id13" title="Link to this heading"></a></h2>
<p><em>August 2023</em></p>
<p>This new release contains several new features and bug fixes.</p>
<p>New features include a new submodule <code class="docutils literal notranslate"><span class="pre">ot.gnn</span></code> that contains two new Graph neural network layers (compatible with <a class="reference external" href="https://pytorch-geometric.readthedocs.io/">Pytorch Geometric</a>) for template-based pooling of graphs with an example on <a class="reference external" href="https://pythonot.github.io/master/auto_examples/gromov/plot_gnn_TFGW.html">graph classification</a>. Related to this, we also now provide FGW and semi relaxed FGW solvers for which the resulting loss is differentiable w.r.t. the parameter <code class="docutils literal notranslate"><span class="pre">alpha</span></code>. Other contributions on the (F)GW front include a new solver for the Proximal Point algorithm <a class="reference external" href="https://pythonot.github.io/master/auto_examples/gromov/plot_fgw_solvers.html">that can be used to solve entropic GW problems</a> (using the parameter <code class="docutils literal notranslate"><span class="pre">solver="PPA"</span></code>), new solvers for entropic FGW barycenters, novels Sinkhorn-based solvers for entropic semi-relaxed (F)GW, the possibility to provide a warm-start to the solvers, and optional marginal weights of the samples (uniform weights ar used by default). Finally we added in the submodule <code class="docutils literal notranslate"><span class="pre">ot.gaussian</span></code> and <code class="docutils literal notranslate"><span class="pre">ot.da</span></code> new loss and mapping estimators for the Gaussian Gromov-Wasserstein that can be used as a fast alternative to GW and estimates linear mappings between unregistered spaces that can potentially have different size (See the update <a class="reference external" href="https://pythonot.github.io/master/auto_examples/domain-adaptation/plot_otda_linear_mapping.html">linear mapping example</a> for an illustration).</p>
<p>We also provide a new solver for the <a class="reference external" href="https://pythonot.github.io/master/auto_examples/others/plot_EWCA.html">Entropic Wasserstein Component Analysis</a> that is a generalization of the celebrated PCA taking into account the local neighborhood of the samples. We also now have a new solver in <code class="docutils literal notranslate"><span class="pre">ot.smooth</span></code> for the <a class="reference external" href="https://pythonot.github.io/master/auto_examples/plot_OT_1D_smooth.html">sparsity-constrained OT (last plot)</a> that can be used to find regularized OT plans with sparsity constraints. Finally we have a first multi-marginal solver for regular 1D distributions with a Monge loss (see <a class="reference external" href="https://pythonot.github.io/master/auto_examples/others/plot_dmmot.html">here</a>).</p>
<p>The documentation and testings have also been updated. We now have nearly 95% code coverage with the tests. The documentation has been updated and some examples have been streamlined to build more quickly and avoid timeout problems with CircleCI. We also added an optional CI on GPU for the master branch and approved PRs that can be used when a GPU runner is online.</p>
<p>Many other bugs and issues have been fixed and we want to thank all the contributors, old and new, who made this release possible. More details below.</p>
<section id="id14">
<h3>New features<a class="headerlink" href="#id14" title="Link to this heading"></a></h3>
<ul class="simple">
<li><p>Added Bures Wasserstein distance in <code class="docutils literal notranslate"><span class="pre">ot.gaussian</span></code> (<a class="reference external" href="https://github.com/PythonOT/POT/pull/">PR #</a>#428)</p></li>
<li><p>Added Generalized Wasserstein Barycenter solver + example (<a class="reference external" href="https://github.com/PythonOT/POT/pull/372">PR #372</a>), fixed graphical details on the example (PR #376)</p></li>
<li><p>Added Free Support Sinkhorn Barycenter + example (<a class="reference external" href="https://github.com/PythonOT/POT/pull/387">PR #387</a>)</p></li>
<li><p>New API for OT solver using function <code class="docutils literal notranslate"><span class="pre">ot.solve</span></code> (<a class="reference external" href="https://github.com/PythonOT/POT/pull/388">PR #388</a>)</p></li>
<li><p>Backend version of <code class="docutils literal notranslate"><span class="pre">ot.partial</span></code> and <code class="docutils literal notranslate"><span class="pre">ot.smooth</span></code> (<a class="reference external" href="https://github.com/PythonOT/POT/pull/388">PR #388</a>)</p></li>
<li><p>Added argument for warmstart of dual vectors in Sinkhorn-based methods in <code class="docutils literal notranslate"><span class="pre">ot.bregman</span></code> (<a class="reference external" href="https://github.com/PythonOT/POT/pull/437">PR #437</a>)</p></li>
</ul>
</section>
<section id="id15">
<h3>Closed issues<a class="headerlink" href="#id15" title="Link to this heading"></a></h3>
<ul class="simple">
<li><p>Fixed an issue with the documentation gallery sections (<a class="reference external" href="https://github.com/PythonOT/POT/pull/395">PR #395</a>)</p></li>
<li><p>Fixed an issue where sinkhorn divergence did not have a gradients (<a class="reference external" href="https://github.com/PythonOT/POT/issues/393">Issue #393</a>, <a class="reference external" href="https://github.com/PythonOT/POT/pull/394">PR #394</a>)</p></li>
<li><p>Fixed an issue where we could not ask TorchBackend to place a random tensor on GPU
(<a class="reference external" href="https://github.com/PythonOT/POT/issues/371">Issue #371</a>, <a class="reference external" href="https://github.com/PythonOT/POT/pull/373">PR #373</a>)</p></li>
<li><p>Fixed an issue where Sinkhorn solver assumed a symmetric cost matrix (<a class="reference external" href="https://github.com/PythonOT/POT/issues/374">Issue #374</a>, <a class="reference external" href="https://github.com/PythonOT/POT/pull/375">PR #375</a>)</p></li>
<li><p>Fixed an issue where hitting iteration limits would be reported to stderr by std::cerr regardless of Python’s stderr stream status (<a class="reference external" href="https://github.com/PythonOT/POT/pull/377">PR #377</a>)</p></li>
<li><p>Fixed an issue where the metric argument in ot.dist did not allow a callable parameter (<a class="reference external" href="https://github.com/PythonOT/POT/issues/378">Issue #378</a>, <a class="reference external" href="https://github.com/PythonOT/POT/pull/379">PR #379</a>)</p></li>
<li><p>Fixed an issue where the max number of iterations in ot.emd was not allowed to go beyond 2^31 (<a class="reference external" href="https://github.com/PythonOT/POT/pull/380">PR #380</a>)</p></li>
<li><p>Fixed an issue where pointers would overflow in the EMD solver, returning an
incomplete transport plan above a certain size (slightly above 46k, its square being
roughly 2^31) (<a class="reference external" href="https://github.com/PythonOT/POT/pull/381">PR #381</a>)</p></li>
<li><p>Error raised when mass mismatch in emd2 (<a class="reference external" href="https://github.com/PythonOT/POT/pull/386">PR #386</a>)</p></li>
<li><p>Fixed an issue where a pytorch example would throw an error if executed on a GPU (<a class="reference external" href="https://github.com/PythonOT/POT/issues/389">Issue #389</a>, <a class="reference external" href="https://github.com/PythonOT/POT/pull/391">PR #391</a>)</p></li>
<li><p>Added a work-around for scipy’s bug, where you cannot compute the Hamming distance with a “None” weight attribute. (<a class="reference external" href="https://github.com/PythonOT/POT/issues/400">Issue #400</a>, <a class="reference external" href="https://github.com/PythonOT/POT/pull/402">PR #402</a>)</p></li>
<li><p>Fixed an issue where the doc could not be built due to some changes in matplotlib’s API (<a class="reference external" href="https://github.com/PythonOT/POT/issues/403">Issue #403</a>, <a class="reference external" href="https://github.com/PythonOT/POT/pull/402">PR #402</a>)</p></li>
<li><p>Replaced Numpy C Compiler with Setuptools C Compiler due to deprecation issues (<a class="reference external" href="https://github.com/PythonOT/POT/issues/408">Issue #408</a>, <a class="reference external" href="https://github.com/PythonOT/POT/pull/409">PR #409</a>)</p></li>
<li><p>Fixed weak optimal transport docstring (<a class="reference external" href="https://github.com/PythonOT/POT/issues/404">Issue #404</a>, <a class="reference external" href="https://github.com/PythonOT/POT/pull/410">PR #410</a>)</p></li>
<li><p>Fixed error with parameter <code class="docutils literal notranslate"><span class="pre">log=True</span></code>for <code class="docutils literal notranslate"><span class="pre">SinkhornLpl1Transport</span></code> (<a class="reference external" href="https://github.com/PythonOT/POT/issues/412">Issue #412</a>,
<a class="reference external" href="https://github.com/PythonOT/POT/pull/413">PR #413</a>)</p></li>
<li><p>Fixed an issue about <code class="docutils literal notranslate"><span class="pre">warn</span></code> parameter in <code class="docutils literal notranslate"><span class="pre">sinkhorn2</span></code> (<a class="reference external" href="https://github.com/PythonOT/POT/pull/417">PR #417</a>)</p></li>
<li><p>Fix an issue where the parameter <code class="docutils literal notranslate"><span class="pre">stopThr</span></code> in <code class="docutils literal notranslate"><span class="pre">empirical_sinkhorn_divergence</span></code> was rendered useless by subcalls
that explicitly specified <code class="docutils literal notranslate"><span class="pre">stopThr=1e-9</span></code> (<a class="reference external" href="https://github.com/PythonOT/POT/issues/421">Issue #421</a>, <a class="reference external" href="https://github.com/PythonOT/POT/pull/422">PR #422</a>).</p></li>
<li><p>Fixed a bug breaking an example where we would try to make an array of arrays of different shapes (<a class="reference external" href="https://github.com/PythonOT/POT/issues/424">Issue #424</a>, <a class="reference external" href="https://github.com/PythonOT/POT/pull/425">PR #425</a>)</p></li>
</ul>
</section>
</section>
<section id="id16">
<h2>0.8.2<a class="headerlink" href="#id16" title="Link to this heading"></a></h2>
<p>This releases introduces several new notable features. The less important
but most exiting one being that we now have a logo for the toolbox (color
and dark background) :</p>
<p><img alt="" src="https://pythonot.github.io/master/_images/logo.svg" /><img alt="" src="https://pythonot.github.io/master/_static/logo_dark.svg" /></p>
<p>This logo is generated using with matplotlib and using the solution of an OT
problem provided by POT (with <code class="docutils literal notranslate"><span class="pre">ot.emd</span></code>). Generating the logo can be done with a
simple python script also provided in the <a class="reference external" href="https://pythonot.github.io/auto_examples/others/plot_logo.html#sphx-glr-auto-examples-others-plot-logo-py">documentation gallery</a>.</p>
<p>New OT solvers include <a class="reference external" href="https://pythonot.github.io/gen_modules/ot.weak.html#ot.weak.weak_optimal_transport">Weak
OT</a>
and <a class="reference external" href="https://pythonot.github.io/gen_modules/ot.factored.html#ot.factored.factored_optimal_transport">OT with factored
coupling</a>
that can be used on large datasets. The <a class="reference external" href="https://pythonot.github.io/gen_modules/ot.unbalanced.html?highlight=mm_#ot.unbalanced.mm_unbalanced">Majorization Minimization</a> solvers for
non-regularized Unbalanced OT are now also available. We also now provide an
implementation of <a class="reference external" href="https://pythonot.github.io/gen_modules/ot.gromov.html#ot.gromov.gromov_wasserstein_linear_unmixing">GW and FGW unmixing</a> and <a class="reference external" href="https://pythonot.github.io/gen_modules/ot.gromov.html#ot.gromov.gromov_wasserstein_dictionary_learning">dictionary learning</a>. It is now
possible to use autodiff to solve entropic an quadratic regularized OT in the
dual for full or stochastic optimization thanks to the new functions to compute
the dual loss for <a class="reference external" href="https://pythonot.github.io/gen_modules/ot.stochastic.html#ot.stochastic.loss_dual_entropic">entropic</a> and <a class="reference external" href="https://pythonot.github.io/gen_modules/ot.stochastic.html#ot.stochastic.loss_dual_quadratic">quadratic</a> regularized OT and reconstruct the <a class="reference external" href="https://pythonot.github.io/gen_modules/ot.stochastic.html#ot.stochastic.plan_dual_entropic">OT
plan</a> on part or all of the data. They can be used for instance to solve OT
problems with stochastic gradient or for estimating the <a class="reference external" href="https://pythonot.github.io/auto_examples/backends/plot_stoch_continuous_ot_pytorch.html#sphx-glr-auto-examples-backends-plot-stoch-continuous-ot-pytorch-py">dual potentials as
neural networks</a>.</p>
<p>On the backend front, we now have backend compatible functions and classes in
the domain adaptation <a class="reference external" href="https://pythonot.github.io/gen_modules/ot.da.html#module-ot.da"><code class="docutils literal notranslate"><span class="pre">ot.da</span></code></a> and unbalanced OT <a class="reference external" href="https://pythonot.github.io/gen_modules/ot.unbalanced.html"><code class="docutils literal notranslate"><span class="pre">ot.unbalanced</span></code></a> modules. This
means that the DA classes can be used on tensors from all compatible backends.
The <a class="reference external" href="https://pythonot.github.io/gen_modules/ot.lp.html?highlight=free%20support#ot.lp.free_support_barycenter">free support Wasserstein barycenter</a> solver is now also backend compatible.</p>
<p>Finally we have worked on the documentation to provide an update of existing
examples in the gallery and and several new examples including <a class="reference external" href="https://pythonot.github.io/auto_examples/gromov/plot_gromov_wasserstein_dictionary_learning.html#sphx-glr-auto-examples-gromov-plot-gromov-wasserstein-dictionary-learning-py">GW dictionary
learning</a>
<a class="reference external" href="https://pythonot.github.io/auto_examples/others/plot_WeakOT_VS_OT.html#sphx-glr-auto-examples-others-plot-weakot-vs-ot-py">weak Optimal
Transport</a>,
<a class="reference external" href="https://pythonot.github.io/auto_examples/backends/plot_stoch_continuous_ot_pytorch.html#sphx-glr-auto-examples-backends-plot-stoch-continuous-ot-pytorch-py">NN based dual potentials
estimation</a>
and <a class="reference external" href="https://pythonot.github.io/auto_examples/others/plot_factored_coupling.html#sphx-glr-auto-examples-others-plot-factored-coupling-py">Factored coupling OT</a>.
.</p>
<section id="id17">
<h3>New features<a class="headerlink" href="#id17" title="Link to this heading"></a></h3>
<ul class="simple">
<li><p>Remove deprecated <code class="docutils literal notranslate"><span class="pre">ot.gpu</span></code> submodule (<a class="reference external" href="https://github.com/PythonOT/POT/pull/361">PR #361</a>)</p></li>
<li><p>Update examples in the gallery (<a class="reference external" href="https://github.com/PythonOT/POT/pull/359">PR #359</a>)</p></li>
<li><p>Add stochastic loss and OT plan computation for regularized OT and
backend examples(<a class="reference external" href="https://github.com/PythonOT/POT/pull/360">PR #360</a>)</p></li>
<li><p>Implementation of factored OT with emd and sinkhorn (<a class="reference external" href="https://github.com/PythonOT/POT/pull/358">PR #358</a>)</p></li>
<li><p>A brand new logo for POT (<a class="reference external" href="https://github.com/PythonOT/POT/pull/357">PR #357</a>)</p></li>
<li><p>Better list of related examples in quick start guide with <code class="docutils literal notranslate"><span class="pre">minigallery</span></code> (<a class="reference external" href="https://github.com/PythonOT/POT/pull/334">PR #334</a>)</p></li>
<li><p>Add optional log-domain Sinkhorn implementation in WDA to support smaller values
of the regularization parameter (<a class="reference external" href="https://github.com/PythonOT/POT/pull/336">PR #336</a>)</p></li>
<li><p>Backend implementation for <code class="docutils literal notranslate"><span class="pre">ot.lp.free_support_barycenter</span></code> (<a class="reference external" href="https://github.com/PythonOT/POT/pull/340">PR #340</a>)</p></li>
<li><p>Add weak OT solver + example (<a class="reference external" href="https://github.com/PythonOT/POT/pull/341">PR #341</a>)</p></li>
<li><p>Add backend support for Domain Adaptation and Unbalanced solvers (<a class="reference external" href="https://github.com/PythonOT/POT/pull/343">PR #343</a>)</p></li>
<li><p>Add (F)GW linear dictionary learning solvers + example (<a class="reference external" href="https://github.com/PythonOT/POT/pull/319">PR #319</a>)</p></li>
<li><p>Add links to related PR and Issues in the doc release page (<a class="reference external" href="https://github.com/PythonOT/POT/pull/350">PR #350</a>)</p></li>
<li><p>Add new minimization-maximization algorithms for solving exact Unbalanced OT + example (<a class="reference external" href="https://github.com/PythonOT/POT/pull/362">PR #362</a>)</p></li>
</ul>
</section>
<section id="id18">
<h3>Closed issues<a class="headerlink" href="#id18" title="Link to this heading"></a></h3>
<ul class="simple">
<li><p>Fix mass gradient of <code class="docutils literal notranslate"><span class="pre">ot.emd2</span></code> and <code class="docutils literal notranslate"><span class="pre">ot.gromov_wasserstein2</span></code> so that they are
centered (<a class="reference external" href="https://github.com/PythonOT/POT/issues/364">Issue #364</a>, <a class="reference external" href="https://github.com/PythonOT/POT/pull/363">PR #363</a>)</p></li>
<li><p>Fix bug in instantiating an <code class="docutils literal notranslate"><span class="pre">autograd</span></code> function <code class="docutils literal notranslate"><span class="pre">ValFunction</span></code> (<a class="reference external" href="https://github.com/PythonOT/POT/issues/337">Issue #337</a>,
<a class="reference external" href="https://github.com/PythonOT/POT/pull/338">PR #338</a>)</p></li>
<li><p>Fix POT ABI compatibility with old and new numpy (<a class="reference external" href="https://github.com/PythonOT/POT/issues/346">Issue #346</a>, <a class="reference external" href="https://github.com/PythonOT/POT/pull/349">PR #349</a>)</p></li>
<li><p>Warning when feeding integer cost matrix to EMD solver resulting in an integer transport plan (<a class="reference external" href="https://github.com/PythonOT/POT/issues/345">Issue #345</a>, <a class="reference external" href="https://github.com/PythonOT/POT/pull/343">PR #343</a>)</p></li>
<li><p>Fix bug where gromov_wasserstein2 does not perform backpropagation with CUDA
tensors (<a class="reference external" href="https://github.com/PythonOT/POT/issues/351">Issue #351</a>, <a class="reference external" href="https://github.com/PythonOT/POT/pull/352">PR #352</a>)</p></li>
</ul>
</section>
</section>
<section id="id19">
<h2>0.8.1.0<a class="headerlink" href="#id19" title="Link to this heading"></a></h2>
<p><em>December 2021</em></p>
<p>This is a bug fix release that will remove the <code class="docutils literal notranslate"><span class="pre">benchmarks</span></code> module form the
installation and correct the documentation generation.</p>
<section id="id20">
<h3>Closed issues<a class="headerlink" href="#id20" title="Link to this heading"></a></h3>
<ul class="simple">
<li><p>Bug in documentation generation (tag VS master push, <a class="reference external" href="https://github.com/PythonOT/POT/pull/332">PR #332</a>)</p></li>
<li><p>Remove installation of the benchmarks in global namespace (<a class="reference external" href="https://github.com/PythonOT/POT/issues/331">Issue #331</a>, <a class="reference external" href="https://github.com/PythonOT/POT/pull/333">PR #333</a>)</p></li>
</ul>
</section>
</section>
<section id="id21">
<h2>0.8.1<a class="headerlink" href="#id21" title="Link to this heading"></a></h2>
<p><em>December 2021</em></p>
<p>This release fixes several bugs and introduces two new backends: Cupy
and Tensorflow. Note that the tensorflow backend will work only when tensorflow
has enabled the Numpy behavior (for transpose that is not by default in
tensorflow). We also introduce a simple benchmark on CPU GPU for the sinkhorn
solver that will be provided in the
<a class="reference external" href="https://pythonot.github.io/gen_modules/ot.backend.html">backend</a> documentation.</p>
<p>This release also brings a few changes in dependencies and compatibility. First
we removed tests for Python 3.6 that will not be updated in the future.
Also note that POT now depends on Numpy (>= 1.20) because a recent change in ABI is making the
wheels non-compatible with older numpy versions. If you really need an older
numpy POT will work with no problems but you will need to build it from source.</p>
<p>As always we want to that the contributors who helped make POT better (and bug free).</p>
<section id="id22">
<h3>New features<a class="headerlink" href="#id22" title="Link to this heading"></a></h3>
<ul class="simple">
<li><p>New benchmark for sinkhorn solver on CPU/GPU and between backends (<a class="reference external" href="https://github.com/PythonOT/POT/pull/316">PR #316</a>)</p></li>
<li><p>New tensorflow backend (<a class="reference external" href="https://github.com/PythonOT/POT/pull/316">PR #316</a>)</p></li>
<li><p>New Cupy backend (<a class="reference external" href="https://github.com/PythonOT/POT/pull/315">PR #315</a>)</p></li>
<li><p>Documentation always up-to-date with README, RELEASES, CONTRIBUTING and
CODE_OF_CONDUCT files (<a class="reference external" href="https://github.com/PythonOT/POT/pull/316">PR #316</a>, PR #322).</p></li>
</ul>
</section>
<section id="id23">
<h3>Closed issues<a class="headerlink" href="#id23" title="Link to this heading"></a></h3>
<ul class="simple">
<li><p>Fix bug in older Numpy ABI (<1.20) (<a class="reference external" href="https://github.com/PythonOT/POT/issues/308">Issue #308</a>, <a class="reference external" href="https://github.com/PythonOT/POT/pull/326">PR #326</a>)</p></li>
<li><p>Fix bug in <code class="docutils literal notranslate"><span class="pre">ot.dist</span></code> function when non euclidean distance (<a class="reference external" href="https://github.com/PythonOT/POT/issues/305">Issue #305</a>, <a class="reference external" href="https://github.com/PythonOT/POT/pull/306">PR #306</a>)</p></li>
<li><p>Fix gradient scaling for functions using <code class="docutils literal notranslate"><span class="pre">nx.set_gradients</span></code> (<a class="reference external" href="https://github.com/PythonOT/POT/issues/309">Issue #309</a>,
<a class="reference external" href="https://github.com/PythonOT/POT/pull/310">PR #310</a>)</p></li>
<li><p>Fix bug in generalized Conditional gradient solver and SinkhornL1L2
(<a class="reference external" href="https://github.com/PythonOT/POT/issues/311">Issue #311</a>, <a class="reference external" href="https://github.com/PythonOT/POT/pull/313">PR #313</a>)</p></li>
<li><p>Fix log error in <code class="docutils literal notranslate"><span class="pre">gromov_barycenters</span></code> (<a class="reference external" href="https://github.com/PythonOT/POT/issues/317">Issue #317</a>, <a class="reference external" href="https://github.com/PythonOT/POT/pull/3018">PR #3018</a>)</p></li>
</ul>
</section>
</section>
<section id="id24">
<h2>0.8.0<a class="headerlink" href="#id24" title="Link to this heading"></a></h2>
<p><em>November 2021</em></p>
<p>This new stable release introduces several important features.</p>
<p>First we now have
an OpenMP compatible exact ot solver in <code class="docutils literal notranslate"><span class="pre">ot.emd</span></code>. The OpenMP version is used
when the parameter <code class="docutils literal notranslate"><span class="pre">numThreads</span></code> is greater than one and can lead to nice
speedups on multi-core machines.</p>
<p>Second we have introduced a backend mechanism that allows to use standard POT
function seamlessly on Numpy, Pytorch and Jax arrays. Other backends are coming
but right now POT can be used seamlessly for training neural networks in
Pytorch. Notably we propose the first differentiable computation of the exact OT
loss with <code class="docutils literal notranslate"><span class="pre">ot.emd2</span></code> (can be differentiated w.r.t. both cost matrix and sample
weights), but also for the classical Sinkhorn loss with <code class="docutils literal notranslate"><span class="pre">ot.sinkhorn2</span></code>, the
Wasserstein distance in 1D with <code class="docutils literal notranslate"><span class="pre">ot.wasserstein_1d</span></code>, sliced Wasserstein with
<code class="docutils literal notranslate"><span class="pre">ot.sliced_wasserstein_distance</span></code> and Gromov-Wasserstein with <code class="docutils literal notranslate"><span class="pre">ot.gromov_wasserstein2</span></code>. Examples of how
this new feature can be used are now available in the documentation where the
Pytorch backend is used to estimate a <a class="reference external" href="https://PythonOT.github.io/auto_examples/backends/plot_unmix_optim_torch.html">minimal Wasserstein
estimator</a>,
a <a class="reference external" href="https://PythonOT.github.io/auto_examples/backends/plot_wass2_gan_torch.html">Generative Network
(GAN)</a>,
for a <a class="reference external" href="https://PythonOT.github.io/auto_examples/backends/plot_sliced_wass_grad_flow_pytorch.html">sliced Wasserstein gradient
flow</a>
and <a class="reference external" href="https://PythonOT.github.io/auto_examples/backends/plot_optim_gromov_pytorch.html">optimizing the Gromov-Wassersein distance</a>. Note that the Jax backend is still in early development and quite
slow at the moment, we strongly recommend for Jax users to use the <a class="reference external" href="https://github.com/google-research/ott">OTT
toolbox</a> when possible.
As a result of this new feature,
the old <code class="docutils literal notranslate"><span class="pre">ot.gpu</span></code> submodule is now deprecated since GPU
implementations can be done using GPU arrays on the torch backends.</p>
<p>Other novel features include implementation for <a class="reference external" href="https://PythonOT.github.io/auto_examples/gromov/plot_gromov.html#compute-gw-with-a-scalable-stochastic-method-with-any-loss-function">Sampled Gromov Wasserstein and
Pointwise Gromov
Wasserstein</a>,
Sinkhorn in log space with <code class="docutils literal notranslate"><span class="pre">method='sinkhorn_log'</span></code>, <a class="reference external" href="https://PythonOT.github.io/gen_modules/ot.dr.html?highlight=robust#ot.dr.projection_robust_wasserstein">Projection Robust
Wasserstein</a>,
ans <a class="reference external" href="https://PythonOT.github.ioauto_examples/barycenters/plot_debiased_barycenter.html">deviased Sinkorn barycenters</a>.</p>
<p>This release will also simplify the installation process. We have now a
<code class="docutils literal notranslate"><span class="pre">pyproject.toml</span></code> that defines the build dependency and POT should now build even
when cython is not installed yet. Also we now provide pe-compiled wheels for
linux <code class="docutils literal notranslate"><span class="pre">aarch64</span></code> that is used on Raspberry PI and android phones and for MacOS on
ARM processors.</p>
<p>Finally POT was accepted for publication in the Journal of Machine Learning
Research (JMLR) open source software track and we ask the POT users to cite <a class="reference external" href="https://www.jmlr.org/papers/v22/20-451.html">this
paper</a> from now on. The documentation has been improved in particular by adding a
“Why OT?” section to the quick start guide and several new examples illustrating
the new features. The documentation now has two version : the stable version
<a class="reference external" href="https://pythonot.github.io/">https://pythonot.github.io/</a>
corresponding to the last release and the master version <a class="reference external" href="https://pythonot.github.io/master">https://pythonot.github.io/master</a> that corresponds to the
current master branch on GitHub.</p>
<p>As usual, we want to thank all the POT contributors (now 37 people have
contributed to the toolbox). But for this release we thank in particular Nathan
Cassereau and Kamel Guerda from the AI support team at
<a class="reference external" href="http://www.idris.fr/">IDRIS</a> for their support to the development of the
backend and OpenMP implementations.</p>
<section id="id25">
<h3>New features<a class="headerlink" href="#id25" title="Link to this heading"></a></h3>
<ul class="simple">
<li><p>OpenMP support for exact OT solvers (<a class="reference external" href="https://github.com/PythonOT/POT/pull/260">PR #260</a>)</p></li>
<li><p>Backend for running POT in numpy/torch + exact solver (<a class="reference external" href="https://github.com/PythonOT/POT/pull/249">PR #249</a>)</p></li>
<li><p>Backend implementation of most functions in <code class="docutils literal notranslate"><span class="pre">ot.bregman</span></code> (<a class="reference external" href="https://github.com/PythonOT/POT/pull/280">PR #280</a>)</p></li>
<li><p>Backend implementation of most functions in <code class="docutils literal notranslate"><span class="pre">ot.optim</span></code> (<a class="reference external" href="https://github.com/PythonOT/POT/pull/282">PR #282</a>)</p></li>
<li><p>Backend implementation of most functions in <code class="docutils literal notranslate"><span class="pre">ot.gromov</span></code> (<a class="reference external" href="https://github.com/PythonOT/POT/pull/294">PR #294</a>, PR #302)</p></li>
<li><p>Test for arrays of different type and device (CPU/GPU) (<a class="reference external" href="https://github.com/PythonOT/POT/pull/304">PR #304</a>, #303)</p></li>
<li><p>Implementation of Sinkhorn in log space with <code class="docutils literal notranslate"><span class="pre">method='sinkhorn_log'</span></code> (<a class="reference external" href="https://github.com/PythonOT/POT/pull/290">PR #290</a>)</p></li>
<li><p>Implementation of regularization path for L2 Unbalanced OT (<a class="reference external" href="https://github.com/PythonOT/POT/pull/274">PR #274</a>)</p></li>
<li><p>Implementation of Projection Robust Wasserstein (<a class="reference external" href="https://github.com/PythonOT/POT/pull/267">PR #267</a>)</p></li>
<li><p>Implementation of Debiased Sinkhorn Barycenters (<a class="reference external" href="https://github.com/PythonOT/POT/pull/291">PR #291</a>)</p></li>
<li><p>Implementation of Sampled Gromov Wasserstein and Pointwise Gromov Wasserstein
(<a class="reference external" href="https://github.com/PythonOT/POT/pull/275">PR #275</a>)</p></li>
<li><p>Add <code class="docutils literal notranslate"><span class="pre">pyproject.toml</span></code> and build POT without installing cython first (<a class="reference external" href="https://github.com/PythonOT/POT/pull/293">PR #293</a>)</p></li>
<li><p>Lazy implementation in log space for sinkhorn on samples (<a class="reference external" href="https://github.com/PythonOT/POT/pull/259">PR #259</a>)</p></li>
<li><p>Documentation cleanup (<a class="reference external" href="https://github.com/PythonOT/POT/pull/298">PR #298</a>)</p></li>
<li><p>Two up-to-date documentations <a class="reference external" href="https://PythonOT.github.io/">for stable
release</a> and for <a class="reference external" href="https://pythonot.github.io/master/">master branch</a>.</p></li>
<li><p>Building wheels on ARM for Raspberry PI and smartphones (<a class="reference external" href="https://github.com/PythonOT/POT/pull/238">PR #238</a>)</p></li>
<li><p>Update build wheels to new version and new pythons (<a class="reference external" href="https://github.com/PythonOT/POT/pull/236">PR #236</a>, #253)</p></li>
<li><p>Implementation of sliced Wasserstein distance (<a class="reference external" href="https://github.com/PythonOT/POT/issues/202">Issue #202</a>, <a class="reference external" href="https://github.com/PythonOT/POT/pull/203">PR #203</a>)</p></li>
<li><p>Add minimal build to CI and perform pep8 test separately (<a class="reference external" href="https://github.com/PythonOT/POT/pull/210">PR #210</a>)</p></li>
<li><p>Speedup of tests and return run time (<a class="reference external" href="https://github.com/PythonOT/POT/pull/262">PR #262</a>)</p></li>
<li><p>Add “Why OT” discussion to the documentation (<a class="reference external" href="https://github.com/PythonOT/POT/pull/220">PR #220</a>)</p></li>
<li><p>New introductory example to discrete OT in the documentation (<a class="reference external" href="https://github.com/PythonOT/POT/pull/191">PR #191</a>)</p></li>
<li><p>Add templates for Issues/PR on Github (PR#181)</p></li>
</ul>
</section>
<section id="id26">
<h3>Closed issues<a class="headerlink" href="#id26" title="Link to this heading"></a></h3>
<ul class="simple">
<li><p>Debug Memory leak in GAN example (#254)</p></li>
<li><p>DEbug GPU bug (<a class="reference external" href="https://github.com/PythonOT/POT/issues/284">Issue #284</a>, #287, <a class="reference external" href="https://github.com/PythonOT/POT/pull/288">PR #288</a>)</p></li>
<li><p>set_gradients method for JAX backend (<a class="reference external" href="https://github.com/PythonOT/POT/pull/278">PR #278</a>)</p></li>
<li><p>Quicker GAN example for CircleCI build (<a class="reference external" href="https://github.com/PythonOT/POT/pull/258">PR #258</a>)</p></li>
<li><p>Better formatting in Readme (<a class="reference external" href="https://github.com/PythonOT/POT/pull/234">PR #234</a>)</p></li>
<li><p>Debug CI tests (<a class="reference external" href="https://github.com/PythonOT/POT/pull/240">PR #240</a>, #241, #242)</p></li>
<li><p>Bug in Partial OT solver dummy points (<a class="reference external" href="https://github.com/PythonOT/POT/pull/215">PR #215</a>)</p></li>
<li><p>Bug when Armijo linesearch (<a class="reference external" href="https://github.com/PythonOT/POT/issues/184">Issue #184</a>, #198, #281, <a class="reference external" href="https://github.com/PythonOT/POT/pull/189">PR #189</a>, #199, #286)</p></li>
<li><p>Bug Barycenter Sinkhorn (Issue 134, <a class="reference external" href="https://github.com/PythonOT/POT/pull/195">PR #195</a>)</p></li>
<li><p>Infeasible solution in exact OT (Issues #126,#93, <a class="reference external" href="https://github.com/PythonOT/POT/pull/217">PR #217</a>)</p></li>
<li><p>Doc for SUpport Barycenters (<a class="reference external" href="https://github.com/PythonOT/POT/issues/200">Issue #200</a>, <a class="reference external" href="https://github.com/PythonOT/POT/pull/201">PR #201</a>)</p></li>
<li><p>Fix labels transport in BaseTransport (<a class="reference external" href="https://github.com/PythonOT/POT/issues/207">Issue #207</a>, <a class="reference external" href="https://github.com/PythonOT/POT/pull/208">PR #208</a>)</p></li>
<li><p>Bug in <code class="docutils literal notranslate"><span class="pre">emd_1d</span></code>, non respected bounds (<a class="reference external" href="https://github.com/PythonOT/POT/issues/169">Issue #169</a>, <a class="reference external" href="https://github.com/PythonOT/POT/pull/170">PR #170</a>)</p></li>
<li><p>Removed Python 2.7 support and update codecov file (<a class="reference external" href="https://github.com/PythonOT/POT/pull/178">PR #178</a>)</p></li>
<li><p>Add normalization for WDA and test it (<a class="reference external" href="https://github.com/PythonOT/POT/pull/172">PR #172</a>, #296)</p></li>
<li><p>Cleanup code for new version of <code class="docutils literal notranslate"><span class="pre">flake8</span></code> (<a class="reference external" href="https://github.com/PythonOT/POT/pull/176">PR #176</a>)</p></li>
<li><p>Fixed requirements in <code class="docutils literal notranslate"><span class="pre">setup.py</span></code> (<a class="reference external" href="https://github.com/PythonOT/POT/pull/174">PR #174</a>)</p></li>
<li><p>Removed specific MacOS flags (<a class="reference external" href="https://github.com/PythonOT/POT/pull/175">PR #175</a>)</p></li>
</ul>
</section>
</section>
<section id="id27">
<h2>0.7.0<a class="headerlink" href="#id27" title="Link to this heading"></a></h2>
<p><em>May 2020</em></p>
<p>This is the new stable release for POT. We made a lot of changes in the
documentation and added several new features such as Partial OT, Unbalanced and
Multi Sources OT Domain Adaptation and several bug fixes. One important change
is that we have created the GitHub organization
<a class="reference external" href="https://github.com/PythonOT">PythonOT</a> that now owns the main POT repository
<a class="reference external" href="https://github.com/PythonOT/POT">https://github.com/PythonOT/POT</a> and the
repository for the new documentation is now hosted at
<a class="reference external" href="https://PythonOT.github.io/">https://PythonOT.github.io/</a>.</p>
<p>This is the first release where the Python 2.7 tests have been removed. Most of
the toolbox should still work but we do not offer support for Python 2.7 and
will close related Issues.</p>
<p>A lot of changes have been done to the documentation that is now hosted on
<a class="reference external" href="https://PythonOT.github.io/">https://PythonOT.github.io/</a> instead of
readthedocs. It was a hard choice but readthedocs did not allow us to run
sphinx-gallery to update our beautiful examples and it was a huge amount of work
to maintain. The documentation is now automatically compiled and updated on
merge. We also removed the notebooks from the repository for space reason and
also because they are all available in the <a class="reference external" href="https://pythonot.github.io/auto_examples/index.html">example
gallery</a>. Note that now the
output of the documentation build for each commit in the PR is available to
check that the doc builds correctly before merging which was not possible with
readthedocs.</p>
<p>The CI framework has also been changed with a move from Travis to Github Action
which allows to get faster tests on Windows, MacOS and Linux. We also now report
our coverage on <a class="reference external" href="https://codecov.io/gh/PythonOT/POT">Codecov.io</a> and we have a
reasonable 92% coverage. We also now generate wheels for a number of OS and
Python versions at each merge in the master branch. They are available as
outputs of this
<a class="reference external" href="https://github.com/PythonOT/POT/actions?query=workflow%3A%22Build+dist+and+wheels%22">action</a>.
This will allow simpler multi-platform releases from now on.</p>
<p>In terms of new features we now have <a class="reference external" href="https://pythonot.github.io/gen_modules/ot.da.html#ot.da.UnbalancedSinkhornTransport">OTDA Classes for unbalanced
OT</a>,
a new Domain adaptation class form <a class="reference external" href="https://pythonot.github.io/auto_examples/domain-adaptation/plot_otda_jcpot.html#sphx-glr-auto-examples-domain-adaptation-plot-otda-jcpot-py">multi domain problems
(JCPOT)</a>,
and several solvers to solve the <a class="reference external" href="https://pythonot.github.io/auto_examples/unbalanced-partial/plot_partial_wass_and_gromov.html#sphx-glr-auto-examples-unbalanced-partial-plot-partial-wass-and-gromov-py">Partial Optimal
Transport</a>
problems.</p>
<p>This release is also the moment to thank all the POT contributors (old and new)
for helping making POT such a nice toolbox. A lot of changes (also in the API)
are coming for the next versions.</p>
<section id="features">
<h3>Features<a class="headerlink" href="#features" title="Link to this heading"></a></h3>
<ul class="simple">
<li><p>New documentation on <a class="reference external" href="https://PythonOT.github.io/">https://PythonOT.github.io/</a> (<a class="reference external" href="https://github.com/PythonOT/POT/pull/160">PR #160</a>, PR #143, PR #144)</p></li>
<li><p>Documentation build on CircleCI with sphinx-gallery (<a class="reference external" href="https://github.com/PythonOT/POT/pull/145">PR #145</a>,PR #146, #155)</p></li>
<li><p>Run sphinx gallery in CI (<a class="reference external" href="https://github.com/PythonOT/POT/pull/146">PR #146</a>)</p></li>
<li><p>Remove notebooks from repo because available in doc (<a class="reference external" href="https://github.com/PythonOT/POT/pull/156">PR #156</a>)</p></li>
<li><p>Build wheels in CI (#157)</p></li>
<li><p>Move from travis to GitHub Action for Windows, MacOS and Linux (<a class="reference external" href="https://github.com/PythonOT/POT/pull/148">PR #148</a>, PR #150)</p></li>
<li><p>Partial Optimal Transport (PR#141 and <a class="reference external" href="https://github.com/PythonOT/POT/pull/142">PR #142</a>)</p></li>
<li><p>Laplace regularized OTDA (<a class="reference external" href="https://github.com/PythonOT/POT/pull/140">PR #140</a>)</p></li>
<li><p>Multi source DA with target shift (<a class="reference external" href="https://github.com/PythonOT/POT/pull/137">PR #137</a>)</p></li>
<li><p>Screenkhorn algorithm (<a class="reference external" href="https://github.com/PythonOT/POT/pull/121">PR #121</a>)</p></li>
</ul>
</section>
<section id="id28">
<h3>Closed issues<a class="headerlink" href="#id28" title="Link to this heading"></a></h3>
<ul class="simple">
<li><p>Add JMLR paper to the readme and Mathieu Blondel to the Acknoledgments (PR
#231, #232)</p></li>
<li><p>Bug in Unbalanced OT example (<a class="reference external" href="https://github.com/PythonOT/POT/issues/127">Issue #127</a>)</p></li>
<li><p>Clean Cython output when calling setup.py clean (<a class="reference external" href="https://github.com/PythonOT/POT/issues/122">Issue #122</a>)</p></li>
<li><p>Various Macosx compilation problems (<a class="reference external" href="https://github.com/PythonOT/POT/issues/113">Issue #113</a>, Issue #118, PR#130)</p></li>
<li><p>EMD dimension mismatch (<a class="reference external" href="https://github.com/PythonOT/POT/issues/114">Issue #114</a>, Fixed in <a class="reference external" href="https://github.com/PythonOT/POT/pull/116">PR #116</a>)</p></li>
<li><p>2D barycenter bug for non square images (<a class="reference external" href="https://github.com/PythonOT/POT/issues/124">Issue #124</a>, fixed in <a class="reference external" href="https://github.com/PythonOT/POT/pull/132">PR #132</a>)</p></li>
<li><p>Bad value in EMD 1D (<a class="reference external" href="https://github.com/PythonOT/POT/issues/138">Issue #138</a>, fixed in <a class="reference external" href="https://github.com/PythonOT/POT/pull/139">PR #139</a>)</p></li>
<li><p>Log bugs for Gromov-Wassertein solver (<a class="reference external" href="https://github.com/PythonOT/POT/issues/107">Issue #107</a>, fixed in <a class="reference external" href="https://github.com/PythonOT/POT/pull/108">PR #108</a>)</p></li>
<li><p>Weight issues in barycenter function (<a class="reference external" href="https://github.com/PythonOT/POT/pull/106">PR #106</a>)</p></li>
</ul>
</section>
</section>
<section id="id29">
<h2>0.6.0<a class="headerlink" href="#id29" title="Link to this heading"></a></h2>
<p><em>July 2019</em></p>
<p>This is the first official stable release of POT and this means a jump to 0.6!
The library has been used in
the wild for a while now and we have reached a state where a lot of fundamental
OT solvers are available and tested. It has been quite stable in the last months
but kept the beta flag in its Pypi classifiers until now.</p>
<p>Note that this release will be the last one supporting officially Python 2.7 (See
https://python3statement.org/ for more reasons). For next release we will keep
the travis tests for Python 2 but will make them non necessary for merge in 2020.</p>
<p>The features are never complete in a toolbox designed for solving mathematical
problems and research but with the new contributions we now implement algorithms
and solvers from 24 scientific papers (listed in the README.md file). New
features include a direct implementation of the <a class="reference external" href="https://pot.readthedocs.io/en/latest/all.html#ot.bregman.empirical_sinkhorn_divergence">empirical Sinkhorn
divergence</a>,
a new efficient (Cython implementation) solver for <a class="reference external" href="https://pot.readthedocs.io/en/latest/all.html#ot.lp.emd_1d">EMD in
1D</a> and
corresponding <a class="reference external" href="https://pot.readthedocs.io/en/latest/all.html#ot.lp.wasserstein_1d">Wasserstein
1D</a>. We now
also have implementations for <a class="reference external" href="https://github.com/rflamary/POT/blob/master/notebooks/plot_UOT_1D.ipynb">Unbalanced
OT</a> and
a solver for <a class="reference external" href="https://github.com/rflamary/POT/blob/master/notebooks/plot_UOT_barycenter_1D.ipynb">Unbalanced OT
barycenters</a>.
A new variant of Gromov-Wasserstein divergence called <a class="reference external" href="https://pot.readthedocs.io/en/latest/all.html?highlight=fused_#ot.gromov.fused_gromov_wasserstein">Fused
Gromov-Wasserstein</a>
has been also contributed with exemples of use on <a class="reference external" href="https://github.com/rflamary/POT/blob/master/notebooks/plot_fgw.ipynb">structured
data</a> and
computing <a class="reference external" href="https://github.com/rflamary/POT/blob/master/notebooks/plot_barycenter_fgw.ipynb">barycenters of labeld
graphs</a>.</p>
<p>A lot of work has been done on the documentation with several new
examples corresponding to the new features and a lot of corrections for the
docstrings. But the most visible change is a new
<a class="reference external" href="https://pot.readthedocs.io/en/latest/quickstart.html">quick start guide</a> for
POT that gives several pointers about which function or classes allow to solve which
specific OT problem. When possible a link is provided to relevant examples.</p>
<p>We will also provide with this release some pre-compiled Python wheels for Linux
64bit on
github and pip. This will simplify the install process that before required a C
compiler and numpy/cython already installed.</p>
<p>Finally we would like to acknowledge and thank the numerous contributors of POT
that has helped in the past build the foundation and are still contributing to
bring new features and solvers to the library.</p>
<section id="id30">
<h3>Features<a class="headerlink" href="#id30" title="Link to this heading"></a></h3>
<ul class="simple">
<li><p>Add compiled manylinux 64bits wheels to pip releases (<a class="reference external" href="https://github.com/PythonOT/POT/pull/91">PR #91</a>)</p></li>
<li><p>Add quick start guide (<a class="reference external" href="https://github.com/PythonOT/POT/pull/88">PR #88</a>)</p></li>
<li><p>Make doctest work on travis (<a class="reference external" href="https://github.com/PythonOT/POT/pull/90">PR #90</a>)</p></li>
<li><p>Update documentation (<a class="reference external" href="https://github.com/PythonOT/POT/pull/79">PR #79</a>, PR #84)</p></li>
<li><p>Solver for EMD in 1D (<a class="reference external" href="https://github.com/PythonOT/POT/pull/89">PR #89</a>)</p></li>
<li><p>Solvers for regularized unbalanced OT (<a class="reference external" href="https://github.com/PythonOT/POT/pull/87">PR #87</a>, PR#99)</p></li>
<li><p>Solver for Fused Gromov-Wasserstein (<a class="reference external" href="https://github.com/PythonOT/POT/pull/86">PR #86</a>)</p></li>
<li><p>Add empirical Sinkhorn and empirical Sinkhorn divergences (<a class="reference external" href="https://github.com/PythonOT/POT/pull/80">PR #80</a>)</p></li>
</ul>
</section>
<section id="id31">
<h3>Closed issues<a class="headerlink" href="#id31" title="Link to this heading"></a></h3>
<ul class="simple">
<li><p><a class="reference external" href="https://github.com/PythonOT/POT/issues/59">Issue #59</a> fail when using “pip install POT” (new details in doc+ hopefully
wheels)</p></li>
<li><p><a class="reference external" href="https://github.com/PythonOT/POT/issues/85">Issue #85</a> Cannot run gpu modules</p></li>
<li><p><a class="reference external" href="https://github.com/PythonOT/POT/issues/75">Issue #75</a> Greenkhorn do not return log (solved in <a class="reference external" href="https://github.com/PythonOT/POT/pull/76">PR #76</a>)</p></li>
<li><p><a class="reference external" href="https://github.com/PythonOT/POT/issues/82">Issue #82</a> Gromov-Wasserstein fails when the cost matrices are slightly different</p></li>
<li><p><a class="reference external" href="https://github.com/PythonOT/POT/issues/72">Issue #72</a> Macosx build problem</p></li>
</ul>
</section>
</section>
<section id="id32">
<h2>0.5.0<a class="headerlink" href="#id32" title="Link to this heading"></a></h2>
<p><em>Sep 2018</em></p>
<p>POT is 2 years old! This release brings numerous new features to the
toolbox as listed below but also several bug correction.</p>
<p>Among the new features, we can highlight a <a class="reference external" href="https://github.com/rflamary/POT/blob/master/notebooks/plot_gromov.ipynb">non-regularized Gromov-Wasserstein
solver</a>,
a new <a class="reference external" href="https://pot.readthedocs.io/en/latest/all.html#ot.bregman.greenkhorn">greedy variant of sinkhorn</a>,
<a class="reference external" href="https://pot.readthedocs.io/en/latest/all.html#ot.lp.barycenter">non-regularized</a>,
<a class="reference external" href="https://github.com/rflamary/POT/blob/master/notebooks/plot_convolutional_barycenter.ipynb">convolutional (2D)</a>
and <a class="reference external" href="https://github.com/rflamary/POT/blob/master/notebooks/plot_free_support_barycenter.ipynb">free support</a>
Wasserstein barycenters and <a class="reference external" href="https://github.com/rflamary/POT/blob/prV0.5/notebooks/plot_OT_1D_smooth.ipynb">smooth</a>
and <a class="reference external" href="https://pot.readthedocs.io/en/latest/all.html#ot.stochastic.sgd_entropic_regularization">stochastic</a>
implementation of entropic OT.</p>
<p>POT 0.5 also comes with a rewriting of ot.gpu using the cupy framework instead of
the unmaintained cudamat. Note that while we tried to keed changes to the
minimum, the OTDA classes were deprecated. If you are happy with the cudamat
implementation, we recommend you stay with stable release 0.4 for now.</p>
<p>The code quality has also improved with 92% code coverage in tests that is now
printed to the log in the Travis builds. The documentation has also been
greatly improved with new modules and examples/notebooks.</p>
<p>This new release is so full of new stuff and corrections thanks to the old
and new POT contributors (you can see the list in the <a class="reference external" href="https://github.com/rflamary/POT/blob/master/README.md">readme</a>).</p>
<section id="id33">
<h3>Features<a class="headerlink" href="#id33" title="Link to this heading"></a></h3>
<ul class="simple">
<li><p>Add non regularized Gromov-Wasserstein solver (<a class="reference external" href="https://github.com/PythonOT/POT/pull/41">PR #41</a>)</p></li>
<li><p>Linear OT mapping between empirical distributions and 90% test coverage (<a class="reference external" href="https://github.com/PythonOT/POT/pull/42">PR #42</a>)</p></li>
<li><p>Add log parameter in class EMDTransport and SinkhornLpL1Transport (<a class="reference external" href="https://github.com/PythonOT/POT/pull/44">PR #44</a>)</p></li>
<li><p>Add Markdown format for Pipy (<a class="reference external" href="https://github.com/PythonOT/POT/pull/45">PR #45</a>)</p></li>
<li><p>Test for Python 3.5 and 3.6 on Travis (<a class="reference external" href="https://github.com/PythonOT/POT/pull/46">PR #46</a>)</p></li>
<li><p>Non regularized Wasserstein barycenter with scipy linear solver and/or cvxopt (<a class="reference external" href="https://github.com/PythonOT/POT/pull/47">PR #47</a>)</p></li>
<li><p>Rename dataset functions to be more sklearn compliant (<a class="reference external" href="https://github.com/PythonOT/POT/pull/49">PR #49</a>)</p></li>
<li><p>Smooth and sparse Optimal transport implementation with entropic and quadratic regularization (<a class="reference external" href="https://github.com/PythonOT/POT/pull/50">PR #50</a>)</p></li>
<li><p>Stochastic OT in the dual and semi-dual (<a class="reference external" href="https://github.com/PythonOT/POT/pull/52">PR #52</a> and PR #62)</p></li>
<li><p>Free support barycenters (<a class="reference external" href="https://github.com/PythonOT/POT/pull/56">PR #56</a>)</p></li>
<li><p>Speed-up Sinkhorn function (<a class="reference external" href="https://github.com/PythonOT/POT/pull/57">PR #57</a> and PR #58)</p></li>
<li><p>Add convolutional Wassersein barycenters for 2D images (<a class="reference external" href="https://github.com/PythonOT/POT/pull/64">PR #64</a>)</p></li>
<li><p>Add Greedy Sinkhorn variant (Greenkhorn) (<a class="reference external" href="https://github.com/PythonOT/POT/pull/66">PR #66</a>)</p></li>
<li><p>Big ot.gpu update with cupy implementation (instead of un-maintained cudamat) (<a class="reference external" href="https://github.com/PythonOT/POT/pull/67">PR #67</a>)</p></li>
</ul>
</section>
<section id="deprecation">
<h3>Deprecation<a class="headerlink" href="#deprecation" title="Link to this heading"></a></h3>
<p>Deprecated OTDA Classes were removed from ot.da and ot.gpu for version 0.5
(<a class="reference external" href="https://github.com/PythonOT/POT/pull/48">PR #48</a> and PR #67). The deprecation message has been for a year here since
0.4 and it is time to pull the plug.</p>
</section>
<section id="id34">
<h3>Closed issues<a class="headerlink" href="#id34" title="Link to this heading"></a></h3>
<ul class="simple">
<li><p><a class="reference external" href="https://github.com/PythonOT/POT/issues/35">Issue #35</a> : remove import plot from ot/<strong>init</strong>.py (See <a class="reference external" href="https://github.com/PythonOT/POT/pull/41">PR #41</a>)</p></li>
<li><p><a class="reference external" href="https://github.com/PythonOT/POT/issues/43">Issue #43</a> : Unusable parameter log for EMDTransport (See <a class="reference external" href="https://github.com/PythonOT/POT/pull/44">PR #44</a>)</p></li>
<li><p><a class="reference external" href="https://github.com/PythonOT/POT/issues/55">Issue #55</a> : UnicodeDecodeError: ‘ascii’ while installing with pip</p></li>
</ul>
</section>
</section>
<section id="id35">
<h2>0.4<a class="headerlink" href="#id35" title="Link to this heading"></a></h2>
<p><em>15 Sep 2017</em></p>
<p>This release contains a lot of contribution from new contributors.</p>
<section id="id36">
<h3>Features<a class="headerlink" href="#id36" title="Link to this heading"></a></h3>
<ul class="simple">
<li><p>Automatic notebooks and doc update (<a class="reference external" href="https://github.com/PythonOT/POT/pull/27">PR #27</a>)</p></li>
<li><p>Add gromov Wasserstein solver and Gromov Barycenters (<a class="reference external" href="https://github.com/PythonOT/POT/pull/23">PR #23</a>)</p></li>
<li><p>emd and emd2 can now return dual variables and have max_iter (<a class="reference external" href="https://github.com/PythonOT/POT/pull/29">PR #29</a> and PR #25)</p></li>
<li><p>New domain adaptation classes compatible with scikit-learn (<a class="reference external" href="https://github.com/PythonOT/POT/pull/22">PR #22</a>)</p></li>
<li><p>Proper tests with pytest on travis (<a class="reference external" href="https://github.com/PythonOT/POT/pull/19">PR #19</a>)</p></li>
<li><p>PEP 8 tests (<a class="reference external" href="https://github.com/PythonOT/POT/pull/13">PR #13</a>)</p></li>
</ul>
</section>
<section id="id37">
<h3>Closed issues<a class="headerlink" href="#id37" title="Link to this heading"></a></h3>
<ul class="simple">
<li><p>emd convergence problem du to fixed max iterations (#24)</p></li>
<li><p>Semi supervised DA error (#26)</p></li>
</ul>
</section>
</section>
<section id="id38">
<h2>0.3.1<a class="headerlink" href="#id38" title="Link to this heading"></a></h2>
<p><em>11 Jul 2017</em></p>
<ul class="simple">
<li><p>Correct bug in emd on windows</p></li>
</ul>
</section>
<section id="id39">
<h2>0.3<a class="headerlink" href="#id39" title="Link to this heading"></a></h2>
<p><em>7 Jul 2017</em></p>
<ul class="simple">
<li><p>emd* and sinkhorn* are now performed in parallel for multiple target distributions</p></li>
<li><p>emd and sinkhorn are for OT matrix computation</p></li>
<li><p>emd2 and sinkhorn2 are for OT loss computation</p></li>
<li><p>new notebooks for emd computation and Wasserstein Discriminant Analysis</p></li>
<li><p>relocate notebooks</p></li>
<li><p>update documentation</p></li>
<li><p>clean_zeros(a,b,M) for removimg zeros in sparse distributions</p></li>
<li><p>GPU implementations for sinkhorn and group lasso regularization</p></li>
</ul>
</section>
<section id="v0-2">
<h2>V0.2<a class="headerlink" href="#v0-2" title="Link to this heading"></a></h2>
<p><em>7 Apr 2017</em></p>
<ul class="simple">
<li><p>New dimensionality reduction method (WDA)</p></li>
<li><p>Efficient method emd2 returns only tarnsport (in paralell if several histograms given)</p></li>
</ul>
</section>
<section id="id40">
<h2>0.1.11<a class="headerlink" href="#id40" title="Link to this heading"></a></h2>
<p><em>5 Jan 2017</em></p>
<ul class="simple">
<li><p>Add sphinx gallery for better documentation</p></li>
<li><p>Small efficiency tweak in sinkhorn</p></li>
<li><p>Add simple tic() toc() functions for timing</p></li>
</ul>
</section>
<section id="id41">
<h2>0.1.10<a class="headerlink" href="#id41" title="Link to this heading"></a></h2>
<p><em>7 Nov 2016</em></p>
<ul class="simple">
<li><p>numerical stabilization for sinkhorn (log domain and epsilon scaling)</p></li>
</ul>
</section>
<section id="id42">
<h2>0.1.9<a class="headerlink" href="#id42" title="Link to this heading"></a></h2>
<p><em>4 Nov 2016</em></p>
<ul class="simple">
<li><p>Update classes and examples for domain adaptation</p></li>
<li><p>Joint OT matrix and mapping estimation</p></li>
</ul>
</section>
<section id="id43">
<h2>0.1.7<a class="headerlink" href="#id43" title="Link to this heading"></a></h2>
<p><em>31 Oct 2016</em></p>
<ul class="simple">
<li><p>Original Domain adaptation classes</p></li>
</ul>
</section>
<section id="id44">
<h2>0.1.3<a class="headerlink" href="#id44" title="Link to this heading"></a></h2>
<ul class="simple">
<li><p>pipy works</p></li>
</ul>
</section>
<section id="first-pre-release">
<h2>First pre-release<a class="headerlink" href="#first-pre-release" title="Link to this heading"></a></h2>
<p><em>28 Oct 2016</em></p>
<p>It provides the following solvers:</p>
<ul class="simple">
<li><p>OT solver for the linear program/ Earth Movers Distance.</p></li>
<li><p>Entropic regularization OT solver with Sinkhorn Knopp Algorithm.</p></li>
<li><p>Bregman projections for Wasserstein barycenter [3] and unmixing.</p></li>
<li><p>Optimal transport for domain adaptation with group lasso regularization</p></li>
<li><p>Conditional gradient and Generalized conditional gradient for regularized OT.</p></li>
</ul>
<p>Some demonstrations (both in Python and Jupyter Notebook format) are available in the examples folder.</p>
</section>
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