Contraction implies convergence of the affine iteration
ProvedMetodosNumericos.affine_iteration_converges_of_contractionIf for all with , and satisfies , then every sequence with converges to , whatever the starting vector. This is Proposição 5.5.3, with the consistency of the vector and matrix norms expressed directly by the hypothesis on .
import Mathlib open Filter Topology
namespace MetodosNumericos
theorem affine_iteration_converges_of_contraction {n : ℕ} (B : Matrix (Fin n) (Fin n) ℝ)
(d : Fin n → ℝ) (c : ℝ) (hc : c < 1)
(hB : ∀ v : Fin n → ℝ, ‖B.mulVec v‖ ≤ c * ‖v‖)
(y : Fin n → ℝ) (hy : y = B.mulVec y + d)
(x : ℕ → (Fin n → ℝ)) (hrec : ∀ k, x (k + 1) = B.mulVec (x k) + d) :
Tendsto x atTop (𝓝 y) := 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 , vectors , a real number and a sequence of vectors , the hypotheses are:
- ;
- for every vector , , where is the supremum norm on the finite product space (the maximum of the absolute values of the coordinates);
- ;
- for every , .
The conclusion is that as .
The starting vector is arbitrary. Taking shows the hypothesis is consistent for any ; for it forces unless . The vector is given as a hypothesis, not asserted to exist or to be unique.
Confirmed by the mission captain (proposal self-audit).
Confirmed by the moderator at approval.