[WIP] Sliced OT Plans

README.md

@@ -72,6 +72,7 @@ POT provides the following generic OT solvers:
 * Fused unbalanced Gromov-Wasserstein [70].
 * [Optimal Transport Barycenters for Generic Costs](https://pythonot.github.io/auto_examples/barycenters/plot_free_support_barycenter_generic_cost.html) [77]
 * [Barycenters between Gaussian Mixture Models](https://pythonot.github.io/auto_examples/barycenters/plot_gmm_barycenter.html) [69, 77]
+* [Sliced Optimal Transport Plans](https://pythonot.github.io/auto_examples/sliced-wasserstein/plot_sliced_plans.html) [82, 83, 84]
 
 POT provides the following Machine Learning related solvers:
 
@@ -449,5 +450,8 @@ Artificial Intelligence.
 
 [81] Xu, H., Luo, D., & Carin, L. (2019). [Scalable Gromov-Wasserstein learning for graph partitioning and matching](https://proceedings.neurips.cc/paper/2019/hash/6e62a992c676f611616097dbea8ea030-Abstract.html). Neural Information Processing Systems (NeurIPS).
 
+[82] Mahey, G., Chapel, L., Gasso, G., Bonet, C., & Courty, N. (2023). [Fast Optimal Transport through Sliced Generalized Wasserstein Geodesics](https://proceedings.neurips.cc/paper_files/paper/2023/hash/6f1346bac8b02f76a631400e2799b24b-Abstract-Conference.html). Advances in Neural Information Processing Systems, 36, 35350–35385.
 
-```
+[83] Tanguy, E., Chapel, L., Delon, J. (2025). [Sliced Optimal Transport Plans](https://arxiv.org/abs/2508.01243) arXiv preprint 2506.03661.
+
+[84] Liu, X., Diaz Martin, R., Bai Y., Shahbazi A., Thorpe M., Aldroubi A., Kolouri, S. (2024). [Expected Sliced Transport Plans](https://openreview.net/forum?id=P7O1Vt1BdU). International Conference on Learning Representations.

RELEASES.md

@@ -1,5 +1,11 @@
 # Releases
 
+## 0.9.7dev
+
+#### New features
+
+- Added Sliced OT plans (PR #757)
+
 ## 0.9.6.post1
 
 *September 2025*

examples/sliced-wasserstein/plot_sliced_plans.py

@@ -0,0 +1,168 @@
+# -*- coding: utf-8 -*-
+"""
+===============
+Sliced OT Plans
+===============
+
+Compares different Sliced OT plans between two 2D point clouds. The min-Pivot
+Sliced plan was introduced in [82], and the Expected Sliced plan in [84], both
+were further studied theoretically in [83].
+
+.. [82] Mahey, G., Chapel, L., Gasso, G., Bonet, C., & Courty, N. (2023). Fast Optimal Transport through Sliced Generalized Wasserstein Geodesics. Advances in Neural Information Processing Systems, 36, 35350–35385.
+
+.. [83] Tanguy, E., Chapel, L., Delon, J. (2025). Sliced Optimal Transport Plans. arXiv preprint 2506.03661.
+
+.. [84] Liu, X., Diaz Martin, R., Bai Y., Shahbazi A., Thorpe M., Aldroubi A., Kolouri, S. (2024). Expected Sliced Transport Plans. International Conference on Learning Representations.
+"""
+
+# Author: Eloi Tanguy <eloi.tanguy@math.cnrs.fr>
+# License: MIT License
+
+# sphinx_gallery_thumbnail_number = 1
+
+##############################################################################
+# Setup data and imports
+# ----------------------
+import numpy as np
+import ot
+import matplotlib.pyplot as plt
+from ot.sliced import get_random_projections
+
+seed = 0
+np.random.seed(seed)
+n = 10
+d = 2
+X = np.random.randn(n, 2)
+Y = np.random.randn(n, 2) + np.array([5.0, 0.0])[None, :]
+n_proj = 20
+thetas = get_random_projections(d, n_proj).T
+alpha = 0.3
+
+##############################################################################
+# Compute min-Pivot Sliced permutation
+# ------------------------------------
+min_perm, min_cost, log_min = ot.min_pivot_sliced(X, Y, thetas, log=True)
+min_plan = np.zeros((n, n))
+min_plan[np.arange(n), min_perm] = 1 / n
+
+##############################################################################
+# Compute Expected Sliced Plan
+# ------------------------------------
+expected_plan, expected_cost, log_expected = ot.expected_sliced(X, Y, thetas, log=True)
+
+##############################################################################
+# Compute 2-Wasserstein Plan
+# ------------------------------------
+a = np.ones(n, device=X.device) / n
+dists = ot.dist(X, Y)
+W2 = ot.emd2(a, a, dists)
+W2_plan = ot.emd(a, a, dists)
+
+##############################################################################
+# Plot resulting assignments
+# ------------------------------------
+fig, axs = plt.subplots(2, 3, figsize=(12, 4))
+fig.suptitle("Sliced plans comparison", y=0.95, fontsize=16)
+
+# draw min sliced permutation
+axs[0, 0].set_title(f"Min Pivot Sliced: cost={min_cost:.2f}")
+for i in range(n):
+    axs[0, 0].plot(
+        [X[i, 0], Y[min_perm[i], 0]],
+        [X[i, 1], Y[min_perm[i], 1]],
+        color="black",
+        alpha=alpha,
+        label="min-Sliced perm" if i == 0 else None,
+    )
+axs[1, 0].imshow(min_plan, interpolation="nearest", cmap="Blues")
+
+# draw expected sliced plan
+axs[0, 1].set_title(f"Expected Sliced: cost={expected_cost:.2f}")
+for i in range(n):
+    for j in range(n):
+        w = alpha * expected_plan[i, j].item() * n
+        axs[0, 1].plot(
+            [X[i, 0], Y[j, 0]],
+            [X[i, 1], Y[j, 1]],
+            color="black",
+            alpha=w,
+            label="Expected Sliced plan" if i == 0 and j == 0 else None,
+        )
+axs[1, 1].imshow(expected_plan, interpolation="nearest", cmap="Blues")
+
+# draw W2 plan
+axs[0, 2].set_title(f"W2: cost={W2:.2f}")
+for i in range(n):
+    for j in range(n):
+        w = alpha * W2_plan[i, j].item() * n
+        axs[0, 2].plot(
+            [X[i, 0], Y[j, 0]],
+            [X[i, 1], Y[j, 1]],
+            color="black",
+            alpha=w,
+            label="W2 plan" if i == 0 and j == 0 else None,
+        )
+axs[1, 2].imshow(W2_plan, interpolation="nearest", cmap="Blues")
+
+for ax in axs[0, :]:
+    ax.scatter(X[:, 0], X[:, 1], label="X")
+    ax.scatter(Y[:, 0], Y[:, 1], label="Y")
+
+for ax in axs.flatten():
+    ax.set_aspect("equal")
+    ax.set_xticks([])
+    ax.set_yticks([])
+
+fig.tight_layout()
+
+##############################################################################
+# Compare Expected Sliced plans with different inverse-temperatures beta
+# ------------------------------------
+## As the temperature decreases, ES becomes sparser and approaches minPS
+betas = [0.0, 5.0, 50.0]
+n_plots = len(betas) + 1
+size = 4
+fig, axs = plt.subplots(2, n_plots, figsize=(size * n_plots, size))
+fig.suptitle(
+    "Expected Sliced plan varying beta (inverse temperature)", y=0.95, fontsize=16
+)
+for beta_idx, beta in enumerate(betas):
+    expected_plan, expected_cost = ot.expected_sliced(X, Y, thetas, beta=beta)
+    print(f"beta={beta}: cost={expected_cost:.2f}")
+
+    axs[0, beta_idx].set_title(f"beta={beta}: cost={expected_cost:.2f}")
+    for i in range(n):
+        for j in range(n):
+            w = alpha * expected_plan[i, j].item() * n
+            axs[0, beta_idx].plot(
+                [X[i, 0], Y[j, 0]],
+                [X[i, 1], Y[j, 1]],
+                color="black",
+                alpha=w,
+                label="Expected Sliced plan" if i == 0 and j == 0 else None,
+            )
+
+    axs[0, beta_idx].scatter(X[:, 0], X[:, 1], label="X")
+    axs[0, beta_idx].scatter(Y[:, 0], Y[:, 1], label="Y")
+    axs[1, beta_idx].imshow(expected_plan, interpolation="nearest", cmap="Blues")
+
+# draw min sliced permutation (limit when beta -> +inf)
+axs[0, -1].set_title(f"Min Pivot Sliced: cost={min_cost:.2f}")
+for i in range(n):
+    axs[0, -1].plot(
+        [X[i, 0], Y[min_perm[i], 0]],
+        [X[i, 1], Y[min_perm[i], 1]],
+        color="black",
+        alpha=alpha,
+        label="min-Sliced perm" if i == 0 else None,
+    )
+axs[0, -1].scatter(X[:, 0], X[:, 1], label="X")
+axs[0, -1].scatter(Y[:, 0], Y[:, 1], label="Y")
+axs[1, -1].imshow(min_plan, interpolation="nearest", cmap="Blues")
+
+for ax in axs.flatten():
+    ax.set_aspect("equal")
+    ax.set_xticks([])
+    ax.set_yticks([])
+
+fig.tight_layout()

ot/__init__.py

@@ -58,6 +58,8 @@
     sliced_wasserstein_sphere,
     sliced_wasserstein_sphere_unif,
     linear_sliced_wasserstein_sphere,
+    min_pivot_sliced,
+    expected_sliced,
 )
 from .gromov import (
     gromov_wasserstein,
@@ -109,6 +111,8 @@
     "sliced_wasserstein_distance",
     "sliced_wasserstein_sphere",
     "linear_sliced_wasserstein_sphere",
+    "min_pivot_sliced",
+    "expected_sliced",
     "gromov_wasserstein",
     "gromov_wasserstein2",
     "gromov_barycenters",