Existence and uniqueness of the fit when the Gram matrix is invertible
ProvedMetodosNumericos.mmq_unique_minimizerIf the Gram matrix has nonzero determinant, then there is exactly one coefficient vector minimizing the sum of squared residuals. This supplies the hypothesis under which the source's assumption that attains a minimum is justified.
import Mathlib import Definitions.Def_MetodosNumericos_ajusteDefs
namespace MetodosNumericos
theorem mmq_unique_minimizer {m n : ℕ} (phi : Fin (n + 1) → ℝ → ℝ)
(x f : Fin (m + 1) → ℝ) (hgram : IsUnit (gramMatrix phi x).det) :
∃! c : Fin (n + 1) → ℝ, ∀ d : Fin (n + 1) → ℝ, sqError phi x f c ≤ sqError phi x f 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 natural numbers , a family of real functions and node and data families of reals, the hypothesis is that the determinant of the matrix with entries is invertible in , which for real numbers means nonzero.
The conclusion is that there exists exactly one coefficient family of reals with the property that for every coefficient family ,
Uniqueness is uniqueness of the minimizing coefficient vector itself, not of the fitted function; existence is asserted, not merely uniqueness.
Confirmed by the mission captain (proposal self-audit).
Confirmed by the moderator at approval.