Improve Bures-Wasserstein distance (#468)

* Improve Bures-Wasserstein distance

* Revert changes and modify sqrtm

* Fix typo

* Add changes to RELEASES.md

---------

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

RELEASES.md

@@ -11,6 +11,7 @@
 - Fix issues with cuda for ot.binary_search_circle and with gradients for ot.sliced_wasserstein_sphere (PR #457)
 - Major documentation cleanup (PR #462, #467)
 - Fix gradients for "Wasserstein2 Minibatch GAN" example (PR #466)
+- Faster Bures-Wasserstein distance with NumPy backend (PR #468)
 
 ## 0.9.0
 

ot/backend.py

@@ -1235,7 +1235,8 @@ def inv(self, a):
         return scipy.linalg.inv(a)
 
     def sqrtm(self, a):
-        return scipy.linalg.sqrtm(a)
+        L, V = np.linalg.eigh(a)
+        return (V * np.sqrt(L)[None, :]) @ V.T
 
     def kl_div(self, p, q, eps=1e-16):
         return np.sum(p * np.log(p / q + eps))
@@ -2433,7 +2434,7 @@ def inv(self, a):
 
     def sqrtm(self, a):
         L, V = cp.linalg.eigh(a)
-        return (V * self.sqrt(L)[None, :]) @ V.T
+        return (V * cp.sqrt(L)[None, :]) @ V.T
 
     def kl_div(self, p, q, eps=1e-16):
         return cp.sum(p * cp.log(p / q + eps))
@@ -2824,7 +2825,8 @@ def inv(self, a):
         return tf.linalg.inv(a)
 
     def sqrtm(self, a):
-        return tf.linalg.sqrtm(a)
+        L, V = tf.linalg.eigh(a)
+        return (V * tf.sqrt(L)[None, :]) @ V.T
 
     def kl_div(self, p, q, eps=1e-16):
         return tnp.sum(p * tnp.log(p / q + eps))

ot/gaussian.py

@@ -202,7 +202,7 @@ def bures_wasserstein_distance(ms, mt, Cs, Ct, log=False):
     where :
 
     .. math::
-        \mathbf{B}(\Sigma_s, \Sigma_t)^{2} = \text{Tr}\left(\Sigma_s^{1/2} + \Sigma_t^{1/2} - 2 \sqrt{\Sigma_s^{1/2}\Sigma_t\Sigma_s^{1/2}} \right)
+        \mathbf{B}(\Sigma_s, \Sigma_t)^{2} = \text{Tr}\left(\Sigma_s + \Sigma_t - 2 \sqrt{\Sigma_s^{1/2}\Sigma_t\Sigma_s^{1/2}} \right)
 
     Parameters
     ----------
@@ -264,7 +264,7 @@ def empirical_bures_wasserstein_distance(xs, xt, reg=1e-6, ws=None,
     where :
 
     .. math::
-        \mathbf{B}(\Sigma_s, \Sigma_t)^{2} = \text{Tr}\left(\Sigma_s^{1/2} + \Sigma_t^{1/2} - 2 \sqrt{\Sigma_s^{1/2}\Sigma_t\Sigma_s^{1/2}} \right)
+        \mathbf{B}(\Sigma_s, \Sigma_t)^{2} = \text{Tr}\left(\Sigma_s + \Sigma_t - 2 \sqrt{\Sigma_s^{1/2}\Sigma_t\Sigma_s^{1/2}} \right)
 
     Parameters
     ----------