make_newton_display
===================

.. py:function:: t3toolbox.backend.optimizer_display.make_newton_display(problem, val_sample = None, val_data = None, print_fn = print, fmt = '%.1e', record = True)

   .. code-block:: python

      def make_newton_display(
              problem,                                  # backend.optimizers.Problem (holds the kind + full data)
              val_sample: typ.Any = None,               # optional validation sample (same kind/layout as problem.sample)
              val_data:   typ.Any = None,               # optional validation data; both given -> a train|val table
              print_fn:   typ.Optional[typ.Callable] = print,   # where to send each iteration's text (None = silent)
              fmt:        str  = '%.1e',
              record:     bool = True,                  # accumulate per-iteration records (scalars + err matrices)
      ) -> typ.Tuple[typ.Callable, list]:               # (callback for newton_cg, records list filled as it runs)


   Build a ``callback(NewtonInfo)`` (+ its ``records`` list) that displays each Newton iteration.

   Precomputes the constant per-block **data norms** ``block_sumsq(data)`` once (the relative-error
   denominators). Each call: recomputes the residual block norms, forms the train (and, if ``val_data``
   is given, validation -- one extra ``point_forward``, no transpose/sweep) relative-error matrices,
   prints via ``print_fn``, and appends a self-contained record (the scalar fields + ``train_err`` /
   ``val_err``). Requires ``problem.kind.block_sumsq`` (all built-in kinds have it).