gram in operator nom

src/mrpro/operators/LinearOperator.py

@@ -144,7 +144,8 @@ def operator_norm(
         if max_iterations < 1:
             raise ValueError('The number of iterations should be larger than zero.')
 
-        # check that the norm of the starting value is not zero
+        dim = tuple(dim) if dim is not None else dim  # must be tuple or None for torch.sum
+
         norm_initial_value = torch.linalg.vector_norm(initial_value, dim=dim, keepdim=True)
         if not (norm_initial_value > 0).all():
             if dim is None:
@@ -156,19 +157,18 @@ def operator_norm(
                     should be different from the zero-vector.'
                 )
 
-        # set initial value
-        vector = initial_value
-
         # creaty dummy operator norm value that cannot be correct because by definition, the
         # operator norm is a strictly positive number. This ensures that the first time the
         # change between the old and the new estimate of the operator norm is non-zero and
         # thus prevents the loop from exiting despite a non-correct estimate.
-        op_norm_old = torch.zeros(*tuple([1 for _ in range(vector.ndim)]), device=vector.device)
+        op_norm_old = torch.zeros(*tuple([1 for _ in range(initial_value.ndim)]), device=initial_value.device)
 
-        dim = tuple(dim) if dim is not None else dim
+        gram = self.gram  # self.H@self
+
+        vector = initial_value
         for _ in range(max_iterations):
             # apply the operator to the vector
-            (vector_new,) = self.adjoint(*self(vector))
+            (vector_new,) = gram(vector)
 
             # compute estimate of the operator norm
             product = vector.real * vector_new.real