Stationarity on the constraint tangent kernel
ProvedVectorSpaceOpt.constrained_extremum_tangent_stationaryLet and be real Banach spaces, let and be continuously Fréchet differentiable on an open set , and let satisfy . Write and for their derivatives at , and assume is onto. If is a local minimum or local maximum of relative to , then
for every direction . This identifies the kernel of the linearized constraint as the true tangent test space and is the immediate analytic input to the multiplier factorization.
import Mathlib open Set
namespace VectorSpaceOpt
/-- Luenberger, Chapter 9, §9.3, Lemma 1. -/
theorem constrained_extremum_tangent_stationary
{X Z : Type*}
[NormedAddCommGroup X] [NormedSpace ℝ X] [CompleteSpace X]
[NormedAddCommGroup Z] [NormedSpace ℝ Z] [CompleteSpace Z]
(U : Set X) (f : X → ℝ) (H : X → Z) (x₀ : X)
(f' : X →L[ℝ] ℝ) (H' : X →L[ℝ] Z)
(hU : IsOpen U) (hx₀ : x₀ ∈ U) (hfeas : H x₀ = 0)
(hf : ContDiffOn ℝ 1 f U) (hH : ContDiffOn ℝ 1 H U)
(hf' : HasFDerivAt f f' x₀) (hH' : HasFDerivAt H H' x₀)
(hregular : Function.Surjective H')
(hextr : IsLocalExtrOn f {x | x ∈ U ∧ H x = 0} x₀) :
∀ h : X, H' h = 0 → f' h = 0 := by
sorry
end VectorSpaceOptRead-back
What the Lean code literally says, in plain math · gpt-5
Let and be real Banach spaces, , , , , and let and be continuous real-linear maps. Assume is open, , , and are continuously Fréchet differentiable through order one on , their Fréchet derivatives at are and , is surjective, and is a local minimum or local maximum of relative to the points satisfying . Then every direction in the kernel of also lies in the kernel of : implies . The conclusion quantifies over all , including .
Confirmed by the mission captain (proposal self-audit).