Minor changes about hermitian prop. checking (eigtj) and doc.

wrapper/matlab/+matfaust/+fact/eigtj.m

@@ -15,7 +15,7 @@
 %> &nbsp;&nbsp;&nbsp; @b eigtj(M, J, 'nGivens_per_fac', t, 'tol', 0.01, 'relerr', true) Uses a stopping criterion based on relative squared error norm(V*D*V'-M, 'fro')^2/norm(M, 'fro')^2. This criterion is concurrent to maxiter (here J).<br/>
 %> &nbsp;&nbsp;&nbsp; @b eigtj(M, J, 'nGivens_per_fac', t, 'tol', 0.01, 'relerr', true) Uses a stopping criterion based on absolute squared error norm(V*D*V'-M, 'fro')^2. This criterion is concurrent to maxiter (here J).<br/>
 %>
-%> @param M the matrix to diagonalize. Must be real and symmetric.
+%> @param M the matrix to diagonalize. Must be real and symmetric or hermitian.
 %> @param maxiter defines the number of Givens rotations that are computed in eigenvector transform V. The number of rotations per factor of V is defined by nGivens_per_fac.
 %> @param 'nGivens_per_fac', integer the number of Givens rotations per factor of V, must be an integer between 1 to floor(size(M, 1)/2) which is the default value.
 %> @param 'tol', number (optional) the tolerance error under what the algorithm stops. By default, it's zero for not stopping on error criterion.

wrapper/matlab/+matfaust/+fact/fgft_givens.m

@@ -5,7 +5,7 @@
 %> @b Usage
 %>      @b See fact.eigtj
 %>
-%> @param Lap the Laplacian matrix (which is symmetric).
+%> @param Lap the Laplacian matrix (which is symmetric or hermitian).
 %> @param maxiter see fact.eigtj
 %> @param 'nGivens_per_fac', integer see fact.eigtj
 %> @param 'tol', number see fact.eigtj

wrapper/python/pyfaust/fact.py

@@ -183,7 +183,7 @@ def fgft_givens(Lap, maxiter, tol=0.0, relerr=True, nGivens_per_fac=None,
         eigtj, fgft_palm
     """
     if(nGivens_per_fac == None): nGivens_per_fac = int(Lap.shape[0]/2)
-    if((isinstance(Lap, np.ndarray) and (Lap.T != Lap).any()) or Lap.shape[0] != Lap.shape[1]):
+    if((isinstance(Lap, np.ndarray) and (np.matrix(Lap).H != Lap).any()) or Lap.shape[0] != Lap.shape[1]):
         raise ValueError(" the matrix/array must be square and should be symmetric.")
     if(not isinstance(maxiter, int)): raise TypeError("maxiter must be a int")
     if(maxiter < 1): raise ValueError("maxiter must be >= 1")