The limit of a convergent affine iteration is a fixed point
ProvedMetodosNumericos.affine_iteration_limit_is_solutionIf the iterates converge to , then . This is Proposição 5.5.1: the limit of the successive approximations solves the fixed-point form of the system, hence the system itself.
import Mathlib open Filter Topology
namespace MetodosNumericos
theorem affine_iteration_limit_is_solution {n : ℕ} (B : Matrix (Fin n) (Fin n) ℝ)
(d : Fin n → ℝ) (x : ℕ → (Fin n → ℝ)) (alpha : Fin n → ℝ)
(hrec : ∀ k, x (k + 1) = B.mulVec (x k) + d)
(hconv : Tendsto x atTop (𝓝 alpha)) :
alpha = B.mulVec alpha + d := by sorry
end MetodosNumericosRead-back
What the Lean code literally says, in plain math · self-authored-by-drafting-agent (non-blind)
Disclosure: this read-back is not blind. It was written by the same agent that drafted the Lean statement, at the explicit instruction of the mission's human owner, and not by an independent auditor with fresh context.
For a natural number , a real matrix , a vector , a sequence of vectors and a vector , the hypotheses are:
- for every natural number , (matrix-vector product plus vector, componentwise);
- the sequence converges to in (equivalently, coordinatewise).
The conclusion is the vector equality . Nothing is assumed about or , and the initial vector is unconstrained. For the statement is trivially true.
Confirmed by the mission captain (proposal self-audit).
Confirmed by the moderator at approval.