Merge branch 'master' into dev

Author: eloitanguy

README.md

@@ -40,8 +40,7 @@ POT provides the following generic OT solvers (links to examples):
 * [Sampled solver of Gromov Wasserstein](https://pythonot.github.io/auto_examples/gromov/plot_gromov.html) for large-scale problem with any loss functions [33]
 * Non regularized [free support Wasserstein barycenters](https://pythonot.github.io/auto_examples/barycenters/plot_free_support_barycenter.html) [20].
 * [One dimensional Unbalanced OT](https://pythonot.github.io/auto_examples/unbalanced-partial/plot_UOT_1D.html) with KL relaxation and [barycenter](https://pythonot.github.io/auto_examples/unbalanced-partial/plot_UOT_barycenter_1D.html) [10, 25]. Also [exact unbalanced OT](https://pythonot.github.io/auto_examples/unbalanced-partial/plot_unbalanced_ot.html) with KL and quadratic regularization and the [regularization path of UOT](https://pythonot.github.io/auto_examples/unbalanced-partial/plot_regpath.html) [41]
-* [Partial Wasserstein and Gromov-Wasserstein](https://pythonot.github.io/auto_examples/unbalanced-partial/plot_partial_wass_and_gromov.html) (exact [29] and entropic [3]
-  formulations).
+* [Partial Wasserstein and Gromov-Wasserstein](https://pythonot.github.io/auto_examples/unbalanced-partial/plot_partial_wass_and_gromov.html) and [Partial Fused Gromov-Wasserstein](https://pythonot.github.io/auto_examples/gromov/plot_partial_fgw.html) (exact [29] and entropic [3] formulations).
 * [Sliced Wasserstein](https://pythonot.github.io/auto_examples/sliced-wasserstein/plot_variance.html) [31, 32] and Max-sliced Wasserstein [35] that can be used for gradient flows [36].
 * [Wasserstein distance on the circle](https://pythonot.github.io/auto_examples/plot_compute_wasserstein_circle.html) [44, 45]
 * [Spherical Sliced Wasserstein](https://pythonot.github.io/auto_examples/sliced-wasserstein/plot_variance_ssw.html) [46]
@@ -394,4 +393,8 @@ Artificial Intelligence.
 
 [73] Séjourné, T., Vialard, F. X., & Peyré, G. (2022). [Faster Unbalanced Optimal Transport: Translation Invariant Sinkhorn and 1-D Frank-Wolfe](https://proceedings.mlr.press/v151/sejourne22a.html). In International Conference on Artificial Intelligence and Statistics (pp. 4995-5021). PMLR.
 
-[74] Tanguy, Eloi and Delon, Julie and Gozlan, Nathaël (2024). [Computing Barycentres of Measures for Generic Transport Costs](https://arxiv.org/abs/2501.04016). arXiv preprint 2501.04016 (2024)
+[74] Chewi, S., Maunu, T., Rigollet, P., & Stromme, A. J. (2020). [Gradient descent algorithms for Bures-Wasserstein barycenters](https://proceedings.mlr.press/v125/chewi20a.html). In Conference on Learning Theory (pp. 1276-1304). PMLR.
+
+[75] Altschuler, J., Chewi, S., Gerber, P. R., & Stromme, A. (2021). [Averaging on the Bures-Wasserstein manifold: dimension-free convergence of gradient descent](https://papers.neurips.cc/paper_files/paper/2021/hash/b9acb4ae6121c941324b2b1d3fac5c30-Abstract.html). Advances in Neural Information Processing Systems, 34, 22132-22145.
+
+[76] Tanguy, Eloi and Delon, Julie and Gozlan, Nathaël (2024). [Computing Barycentres of Measures for Generic Transport Costs](https://arxiv.org/abs/2501.04016). arXiv preprint 2501.04016 (2024)

RELEASES.md

@@ -10,14 +10,20 @@
 - Implement fixed-point solver for OT barycenters with generic cost functions
   (generalizes `ot.lp.free_support_barycenter`), with example. (PR #715)
 - Implement fixed-point solver for barycenters between GMMs (PR #715), with example.
+- Implement projected gradient descent solvers for entropic partial FGW (PR #702)
 - Fix documentation in the module `ot.gaussian` (PR #718)
+- Refactored `ot.bregman._convolutional` to improve readability (PR #709)
+- Added `ot.gaussian.bures_barycenter_gradient_descent` (PR #680)
+- Added `ot.gaussian.bures_wasserstein_distance` (PR #680)
+- `ot.gaussian.bures_wasserstein_distance` can be batched (PR #680)
 
 #### Closed issues
 - Fixed `ot.mapping` solvers which depended on deprecated `cvxpy` `ECOS` solver (PR #692, Issue #668)
 - Fixed numerical errors in `ot.gmm` (PR #690, Issue #689)
 - Add version number to the documentation (PR #696)
 - Update doc for default regularization in `ot.unbalanced` sinkhorn solvers (Issue #691, PR #700)
 - Clean documentation for `gromov`, `lp` and `unbalanced` folders (PR #710)
+- Clean references in documentation (PR #722)
 
 ## 0.9.5
 
@@ -46,7 +52,6 @@ This release also contains few bug fixes, concerning the support of any metric i
 - Notes before depreciating partial Gromov-Wasserstein function in `ot.partial` moved to ot.gromov  (PR #663)
 - Create `ot.gromov._partial` add new features `loss_fun = "kl_loss"` and `symmetry=False` to all solvers while increasing speed + updating adequatly `ot.solvers` (PR #663)
 - Added `ot.unbalanced.sinkhorn_unbalanced_translation_invariant` (PR #676)
-- Refactored `ot.bregman._convolutional` to improve readability (PR #709)
 
 #### Closed issues
 - Fixed `ot.gaussian` ignoring weights when computing means (PR #649, Issue #648)

examples/gromov/plot_barycenter_fgw.py

@@ -91,7 +91,7 @@ def build_noisy_circular_graph(
     g = nx.Graph()
     g.add_nodes_from(list(range(N)))
     for i in range(N):
-        noise = float(np.random.normal(mu, sigma, 1))
+        noise = np.random.normal(mu, sigma, 1)[0]
         if with_noise:
             g.add_node(i, attr_name=math.sin((2 * i * math.pi / N)) + noise)
         else:
@@ -107,7 +107,7 @@ def build_noisy_circular_graph(
                 if i == N - 1:
                     g.add_edge(i, 1)
     g.add_edge(N, 0)
-    noise = float(np.random.normal(mu, sigma, 1))
+    noise = np.random.normal(mu, sigma, 1)[0]
     if with_noise:
         g.add_node(N, attr_name=math.sin((2 * N * math.pi / N)) + noise)
     else:
@@ -157,7 +157,7 @@ def graph_colors(nx_graph, vmin=0, vmax=7):
     plt.subplot(3, 3, i + 1)
     g = X0[i]
     pos = nx.kamada_kawai_layout(g)
-    nx.draw(
+    nx.draw_networkx(
         g,
         pos=pos,
         node_color=graph_colors(g, vmin=-1, vmax=1),
@@ -173,7 +173,7 @@ def graph_colors(nx_graph, vmin=0, vmax=7):
 
 # %% We compute the barycenter using FGW. Structure matrices are computed using the shortest_path distance in the graph
 # Features distances are the euclidean distances
-Cs = [shortest_path(nx.adjacency_matrix(x).todense()) for x in X0]
+Cs = [shortest_path(nx.adjacency_matrix(x).toarray()) for x in X0]
 ps = [np.ones(len(x.nodes())) / len(x.nodes()) for x in X0]
 Ys = [
     np.array([v for (k, v) in nx.get_node_attributes(x, "attr_name").items()]).reshape(
@@ -199,7 +199,7 @@ def graph_colors(nx_graph, vmin=0, vmax=7):
 
 # %%
 pos = nx.kamada_kawai_layout(bary)
-nx.draw(
+nx.draw_networkx(
     bary, pos=pos, node_color=graph_colors(bary, vmin=-1, vmax=1), with_labels=False
 )
 plt.suptitle("Barycenter", fontsize=20)