Solving the linearization at x_n gives a quadratically convergent iteration near a simple root. A small computation will anchor the general statement before the abstraction takes over.
The mathematical object
Numerical analysis replaces an exact object \(x^\ast\) by approximations \(x_n\) with controlled error. Stability asks how rounding or data perturbations affect the answer.
A reliable calculation names domain and codomain. The notation \(\mathsf{data}\mapsto\mathsf{claim}\) is harmless only after both \(\operatorname{dom}\) and \(\operatorname{cod}\) have been fixed.
One explicit computation
Now evaluate one representative case. The result should agree with the structural law above, but it is obtained without assuming the conclusion.
Why the identity matters
A good test for understanding is to change the presentation while keeping the invariant fixed. The aligned form makes that comparison unusually easy.
Where it can fail
Fast local convergence is not a global guarantee. Newton's method can diverge, interpolation can oscillate, and a small residual can coexist with a large forward error.
This is enough machinery for one note: an exact object, a worked case, a structural law, and a clearly marked boundary. Each layer can now be tested independently.