GaussNewtonModel.evaluate
=========================

.. py:method:: t3toolbox.fitting.GaussNewtonModel.evaluate(p)

   .. code-block:: python

      def evaluate(self, p: t3m.T3Tangent) -> NDArray:  # m(p), shape C


   The quadratic-model value ``m(p) = c + gᵀp + ½ pᵀ H p`` with ``H = JᵀJ``, reusing the cached
   ``c`` and ``g`` and one **forward** apply: the quadratic term needs only ``½ pᵀ H p = ½‖𝒥 Π p‖²``,
   not a Hessian apply. ``p`` is projected here, shared by the linear term ``⟨g, Πp⟩`` and the
   quadratic. Equals ``½‖r + 𝒥 Π p‖²`` exactly (the objective is quadratic in the ambient tensor).
   With a ``regularizer``, ``c`` and ``g`` already carry ``ρ`` / ``g_R``, so only the quadratic
   ``½⟨p, H_R p⟩`` is added here.