def tf_min_eig_vec(self):
    """Function for min eigen vector using tf's full eigen decomposition."""
    # Full eigen decomposition requires the explicit psd matrix M
    _, matrix_m = self.dual_object.get_full_psd_matrix()
    [eig_vals, eig_vectors] = tf.self_adjoint_eig(matrix_m)
    index = tf.argmin(eig_vals)
    return tf.reshape(
        eig_vectors[:, index], shape=[eig_vectors.shape[0].value, 1])
def tf_min_eig_vec ( self ) : _ , matrix_m = self . dual_object . get_full_psd_matrix ( ) [ eig_vals , eig_vectors ] = tf . self_adjoint_eig ( matrix_m ) index = tf . argmin ( eig_vals ) return tf . reshape ( eig_vectors [ : , index ] , shape = [ eig_vectors . shape [ 0 ] . value , 1 ] )
Function for min eigen vector using tf's full eigen decomposition.