[MRG] fix doc+example lowrank sinkhorn (#601)

* fix doc+example lowrank sinkhorn

* fix autosummary for lowrank doc

* update release

---------

Co-authored-by: Rémi Flamary <remi.flamary@gmail.com>

Author: cedricvincentcuaz

RELEASES.md

@@ -5,6 +5,7 @@
 #### Closed issues
 - Fixed an issue with cost correction for mismatched labels in `ot.da.BaseTransport` fit methods. This fix addresses the original issue introduced PR #587 (PR #593)
 - Fix gpu compatibility of sr(F)GW solvers when `G0 is not None`(PR #596)
+- Fix doc and example for lowrank sinkhorn (PR #601)
 
 ## 0.9.2
 *December 2023*

docs/source/all.rst

@@ -24,6 +24,7 @@ API and modules
    gaussian
    gnn
    gromov
+   lowrank
    lp
    mapping
    optim

examples/others/plot_lowrank_sinkhorn.py

@@ -88,40 +88,35 @@
 #%%
 
 # Plot sinkhorn vs low rank sinkhorn
-pl.figure(1, figsize=(10, 4))
+pl.figure(1, figsize=(10, 8))
 
-pl.subplot(1, 3, 1)
+pl.subplot(2, 3, 1)
 pl.imshow(list_P_Sin[0], interpolation='nearest')
 pl.axis('off')
 pl.title('Sinkhorn (reg=0.05)')
 
-pl.subplot(1, 3, 2)
+pl.subplot(2, 3, 2)
 pl.imshow(list_P_Sin[1], interpolation='nearest')
 pl.axis('off')
 pl.title('Sinkhorn (reg=0.005)')
 
-pl.subplot(1, 3, 3)
+pl.subplot(2, 3, 3)
 pl.imshow(list_P_Sin[2], interpolation='nearest')
 pl.axis('off')
 pl.title('Sinkhorn (reg=0.001)')
 pl.show()
 
-
-#%%
-
-pl.figure(2, figsize=(10, 4))
-
-pl.subplot(1, 3, 1)
+pl.subplot(2, 3, 4)
 pl.imshow(list_P_LR[0], interpolation='nearest')
 pl.axis('off')
 pl.title('Low rank (rank=3)')
 
-pl.subplot(1, 3, 2)
+pl.subplot(2, 3, 5)
 pl.imshow(list_P_LR[1], interpolation='nearest')
 pl.axis('off')
 pl.title('Low rank (rank=10)')
 
-pl.subplot(1, 3, 3)
+pl.subplot(2, 3, 6)
 pl.imshow(list_P_LR[2], interpolation='nearest')
 pl.axis('off')
 pl.title('Low rank (rank=50)')

ot/__init__.py

@@ -5,7 +5,7 @@
     :py:mod:`ot.utils`, :py:mod:`ot.datasets`,
     :py:mod:`ot.gromov`, :py:mod:`ot.smooth`
     :py:mod:`ot.stochastic`, :py:mod:`ot.partial`, :py:mod:`ot.regpath`
-    , :py:mod:`ot.unbalanced`, :py:mod`ot.mapping`.
+    , :py:mod:`ot.unbalanced`, :py:mod:`ot.mapping` .
     The following sub-modules are not imported due to additional dependencies:
     - :any:`ot.dr` : depends on :code:`pymanopt` and :code:`autograd`.
     - :any:`ot.plot` : depends on :code:`matplotlib`
@@ -71,4 +71,5 @@
            'factored_optimal_transport', 'solve', 'solve_gromov','solve_sample', 
            'smooth', 'stochastic', 'unbalanced', 'partial', 'regpath', 'solvers',
            'binary_search_circle', 'wasserstein_circle',
-           'semidiscrete_wasserstein2_unif_circle', 'sliced_wasserstein_sphere_unif', 'lowrank_sinkhorn']
+           'semidiscrete_wasserstein2_unif_circle', 'sliced_wasserstein_sphere_unif',
+           'lowrank_sinkhorn']

ot/lowrank.py

@@ -319,17 +319,18 @@ def lowrank_sinkhorn(X_s, X_t, a=None, b=None, reg=0, rank=None, alpha=1e-10, re
     The function solves the following optimization problem:
 
     .. math::
-        \mathop{\inf_{(Q,R,g) \in \mathcal{C(a,b,r)}}} \langle C, Q\mathrm{diag}(1/g)R^T \rangle -
-            \mathrm{reg} \cdot H((Q,R,g))
+        \mathop{\inf_{(\mathbf{Q},\mathbf{R},\mathbf{g}) \in \mathcal{C}(\mathbf{a},\mathbf{b},r)}} \langle \mathbf{C}, \mathbf{Q}\mathrm{diag}(1/\mathbf{g})\mathbf{R}^\top \rangle -
+            \mathrm{reg} \cdot H((\mathbf{Q}, \mathbf{R}, \mathbf{g}))
 
     where :
-    - :math:`C` is the (`dim_a`, `dim_b`) metric cost matrix
-    - :math:`H((Q,R,g))` is the values of the three respective entropies evaluated for each term.
-    - :math: `Q` and `R` are the low-rank matrix decomposition of the OT plan
-    - :math: `g` is the weight vector for the low-rank decomposition of the OT plan
+
+    - :math:`\mathbf{C}` is the (`dim_a`, `dim_b`) metric cost matrix
+    - :math:`H((\mathbf{Q}, \mathbf{R}, \mathbf{g}))` is the values of the three respective entropies evaluated for each term.
+    - :math:`\mathbf{Q}` and :math:`\mathbf{R}` are the low-rank matrix decomposition of the OT plan
+    - :math:`\mathbf{g}` is the weight vector for the low-rank decomposition of the OT plan
     - :math:`\mathbf{a}` and :math:`\mathbf{b}` are source and target weights (histograms, both sum to 1)
-    - :math: `r` is the rank of the OT plan
-    - :math: `\mathcal{C(a,b,r)}` are the low-rank couplings of the OT problem
+    - :math:`r` is the rank of the OT plan
+    - :math:`\mathcal{C}(\mathbf{a}, \mathbf{b}, r)` are the low-rank couplings of the OT problem
 
 
     Parameters