svd from adlib

fixedpoint.py

 import torch
 from scipy import optimize
 
+from tensornets.adlib import SVD 
+svd = SVD.apply
+
 def step(T, C, E, chi):
     dimT, dimE = T.shape[0], E.shape[0]
     D_new = min(dimE*dimT, chi)
@@ -15,7 +18,7 @@ def step(T, C, E, chi):
     Rho = Rho/Rho.norm()
    
     # step 2: Get Isometry P
-    U, S, V = torch.svd(Rho)
+    U, S, V = svd(Rho)
     truncation_error = S[D_new:].sum()/S.sum()
     P = U[:, :D_new] # projection operator