Merge tag '0.8.2' into dfsg/latest

1 files changed, 76 insertions, 5 deletions

diff --git a/RELEASES.md b/RELEASES.md
index 00af0fb..be2192e 100644
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+++ b/RELEASES.md
@@ -1,6 +1,77 @@
 # Releases
 
 
+## 0.8.2
+
+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) :
+
+![](https://pythonot.github.io/master/_images/logo.svg)![](https://pythonot.github.io/master/_static/logo_dark.svg)
+
+This logo is generated using with matplotlib and using the solution of an OT
+problem provided by POT (with `ot.emd`). Generating the logo can be done with a
+simple python script also provided in the [documentation gallery](https://pythonot.github.io/auto_examples/others/plot_logo.html#sphx-glr-auto-examples-others-plot-logo-py).
+
+New OT solvers include [Weak
+OT](https://pythonot.github.io/gen_modules/ot.weak.html#ot.weak.weak_optimal_transport)
+ and [OT with factored
+coupling](https://pythonot.github.io/gen_modules/ot.factored.html#ot.factored.factored_optimal_transport)
+that can be used on large datasets. The [Majorization Minimization](https://pythonot.github.io/gen_modules/ot.unbalanced.html?highlight=mm_#ot.unbalanced.mm_unbalanced) solvers for
+non-regularized Unbalanced OT are now also available. We also now provide an
+implementation of [GW and FGW unmixing](https://pythonot.github.io/gen_modules/ot.gromov.html#ot.gromov.gromov_wasserstein_linear_unmixing) and [dictionary learning](https://pythonot.github.io/gen_modules/ot.gromov.html#ot.gromov.gromov_wasserstein_dictionary_learning). 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 [entropic](https://pythonot.github.io/gen_modules/ot.stochastic.html#ot.stochastic.loss_dual_entropic) and [quadratic](https://pythonot.github.io/gen_modules/ot.stochastic.html#ot.stochastic.loss_dual_quadratic) regularized OT and reconstruct the [OT
+plan](https://pythonot.github.io/gen_modules/ot.stochastic.html#ot.stochastic.plan_dual_entropic) on part or all of the data. They can be used for instance to solve OT
+problems with stochastic gradient or for estimating the [dual potentials as
+neural networks](https://pythonot.github.io/auto_examples/backends/plot_stoch_continuous_ot_pytorch.html#sphx-glr-auto-examples-backends-plot-stoch-continuous-ot-pytorch-py).
+
+On the backend front, we now have backend compatible functions and classes in
+the domain adaptation [`ot.da`](https://pythonot.github.io/gen_modules/ot.da.html#module-ot.da) and unbalanced OT [`ot.unbalanced`](https://pythonot.github.io/gen_modules/ot.unbalanced.html) modules. This
+means that the DA classes can be used on tensors from all compatible backends.
+The [free support Wasserstein barycenter](https://pythonot.github.io/gen_modules/ot.lp.html?highlight=free%20support#ot.lp.free_support_barycenter) solver is now also backend compatible.
+
+Finally we have worked on the documentation to provide an update of existing
+examples in the gallery and and several new examples including [GW dictionary
+learning](https://pythonot.github.io/auto_examples/gromov/plot_gromov_wasserstein_dictionary_learning.html#sphx-glr-auto-examples-gromov-plot-gromov-wasserstein-dictionary-learning-py)
+[weak Optimal
+Transport](https://pythonot.github.io/auto_examples/others/plot_WeakOT_VS_OT.html#sphx-glr-auto-examples-others-plot-weakot-vs-ot-py),
+[NN based dual potentials
+estimation](https://pythonot.github.io/auto_examples/backends/plot_stoch_continuous_ot_pytorch.html#sphx-glr-auto-examples-backends-plot-stoch-continuous-ot-pytorch-py)
+and [Factored coupling OT](https://pythonot.github.io/auto_examples/others/plot_factored_coupling.html#sphx-glr-auto-examples-others-plot-factored-coupling-py).
+.
+
+#### New features
+
+- Remove deprecated `ot.gpu` submodule (PR #361)
+- Update examples in the gallery (PR #359)
+- Add stochastic loss and OT plan computation for regularized OT and 
+  backend examples(PR #360)
+- Implementation of factored OT with emd and sinkhorn (PR #358)
+- A brand new logo for POT (PR #357)
+- Better list of related examples in quick start guide with `minigallery` (PR #334)
+- Add optional log-domain Sinkhorn implementation in WDA to support smaller values
+  of the regularization parameter (PR #336)
+- Backend implementation for `ot.lp.free_support_barycenter` (PR #340)
+- Add weak OT solver + example  (PR #341)
+- Add backend support for Domain Adaptation and Unbalanced solvers (PR #343)
+- Add (F)GW linear dictionary learning solvers + example  (PR #319)
+- Add links to related PR and Issues in the doc release page (PR #350)
+- Add new minimization-maximization algorithms for solving exact Unbalanced OT + example (PR #362)
+
+#### Closed issues
+
+- Fix mass gradient of `ot.emd2` and `ot.gromov_wasserstein2` so that they are 
+  centered (Issue #364, PR #363)
+- Fix bug in instantiating an `autograd` function `ValFunction` (Issue #337, 
+  PR #338)
+- Fix POT ABI compatibility with old and new numpy (Issue #346, PR #349)
+- Warning when feeding integer cost matrix to EMD solver resulting in an integer transport plan (Issue #345, PR #343)
+- Fix bug where gromov_wasserstein2 does not perform backpropagation with CUDA
+  tensors (Issue #351, PR #352)
+
+
 ## 0.8.1.0
 *December 2021*
 
@@ -43,10 +114,10 @@ As always we want to that the contributors who helped make POT better (and bug f
 
 - Fix bug in older Numpy ABI (<1.20) (Issue #308, PR #326)
 - Fix bug  in `ot.dist` function when non euclidean distance (Issue #305, PR #306)
-- Fix gradient scaling for functions using `nx.set_gradients` (Issue #309, PR
-  #310)
-- Fix bug in generalized Conditional gradient solver and SinkhornL1L2 (Issue
-  #311, PR #313)
+- Fix gradient scaling for functions using `nx.set_gradients` (Issue #309, 
+  PR #310)
+- Fix bug in generalized Conditional gradient solver and SinkhornL1L2 
+  (Issue #311, PR #313)
 - Fix log error in `gromov_barycenters` (Issue #317, PR #3018)
 
 ## 0.8.0
@@ -227,7 +298,7 @@ are coming for the next versions.
 
 #### Closed issues
 
-- Add JMLR paper to teh readme ad Mathieu Blondel to the Acknoledgments (PR
+- Add JMLR paper to the readme and Mathieu Blondel to the Acknoledgments (PR
   #231, #232)
 - Bug in Unbalanced OT example (Issue #127)
 - Clean Cython output when calling setup.py clean (Issue #122)