TensorNetwork: numpy.linalg.LinAlgError: SVD did not converge

Thx for the current repo.

I want to use TensorNetwork package to simulate time evolution of transverse ising model with itebd algorithm. but it seems svd throws out error like this, can anyone give answers or any hints?

• does the split_node_full_svd used in apply_two_site_gate() has parameter like max_iter or something else?
• are there demo code snippets for using tn.InfiniteMPS or tn.FiniteMPS with tebd algorithm?

thanks~

$ python evolution.py 
1727.9739590611944
6.306919862739928e-15
0.014427307657060863
7.142939906200455e-15
0.0015813901699260678
7.156663235322543e-15
../anaconda3/envs/tf2/lib/python3.7/site-packages/tensornetwork/backends/numpy/numpy_backend.py:90: RuntimeWarning: invalid value encountered in sqrt
  return np.sqrt(tensor)

Intel MKL ERROR: Parameter 4 was incorrect on entry to DLASCL.

Intel MKL ERROR: Parameter 4 was incorrect on entry to DLASCL.
Traceback (most recent call last):
  File "evolution.py", line 83, in <module>
    itebd_ising(N=10)
  File "evolution.py", line 76, in itebd_ising
    imps_state.canonicalize()
  File "../anaconda3/envs/tf2/lib/python3.7/site-packages/tensornetwork/matrixproductstates/infinite_mps.py", line 276, in canonicalize
    relative=True)
  File "..anaconda3/envs/tf2/lib/python3.7/site-packages/tensornetwork/backends/numpy/numpy_backend.py", line 627, in svd
    relative=relative)
  File "../anaconda3/envs/tf2/lib/python3.7/site-packages/tensornetwork/backends/numpy/decompositions.py", line 36, in svd
    u, s, vh = np.linalg.svd(tensor, full_matrices=False)
  File "<__array_function__ internals>", line 6, in svd
  File "../anaconda3/envs/tf2/lib/python3.7/site-packages/numpy/linalg/linalg.py", line 1636, in svd
    u, s, vh = gufunc(a, signature=signature, extobj=extobj)
  File "../anaconda3/envs/tf2/lib/python3.7/site-packages/numpy/linalg/linalg.py", line 106, in _raise_linalgerror_svd_nonconvergence
    raise LinAlgError("SVD did not converge")
numpy.linalg.LinAlgError: SVD did not converge

code script shown below,

import tensornetwork as tn 
import numpy as np 
import tensorflow as tf
# tn.set_default_backend('numpy')

def itebd_ising(N=1000):
    J=1.0; 
    g=0.5; 
    chi=50
    d=2;  
    delta=0.05;
    # N=3000;

    # two-site Hamiltonian
    H = np.array([[J, -g/2, -g/2,0],
                [-g/2, -J,0,-g/2],
                [-g/2.,0,-J,-g/2.],
                [0,-g/2.,-g/2.,J]])
    w,v=np.linalg.eig(H)
    U=np.reshape(np.dot(np.dot(v,np.diag(np.exp(-delta*w))),np.transpose(v)),(2,2,2,2))

    imps_state=tn.InfiniteMPS.random(d=[2,2], D=[chi,chi,chi], dtype=np.complex128)
    print(imps_state.check_canonical())
    imps_state.canonicalize()
    print(imps_state.check_canonical())
    truncation=[]
    for step in range(N):
        if step%2==0:
            truncation_err = imps_state.apply_two_site_gate(U, site1=0, site2=1,max_singular_values=chi)
        else:
            truncation_err = imps_state.apply_two_site_gate(np.transpose(U,(1,0,3,2)), site1=0, site2=1,max_singular_values=chi)
        truncation.append(np.linalg.norm(truncation_err))
        imps_state.canonicalize()
        print(np.linalg.norm(truncation_err))
        print(imps_state.check_canonical())
        # pdb.set_trace()
    print("-"*20)
    print(sum(truncation))
if __name__ == "__main__":
    itebd_ising(N=10)