Equality-constrained Lagrange multiplier
ProvedVectorSpaceOpt.equality_lagrange_multiplierLet and be real Banach spaces, open, and , continuously Fréchet differentiable on . Suppose , , the derivative at is surjective, and is a local extremum of on the feasible level set. If is the derivative of at , then a continuous multiplier exists with
This is the mission capstone: it converts tangent stationarity into a coordinate-free multiplier equation and supplies the regular Banach-space equality rule used throughout later vector-space optimization.
import Mathlib open Set
namespace VectorSpaceOpt
/-- Luenberger, Chapter 9, §9.3, Theorem 1. -/
theorem equality_lagrange_multiplier
{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) :
∃ z₀ : Z →L[ℝ] ℝ, f' + z₀.comp 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 there exists a continuous real-linear functional such that is the zero continuous linear functional on . No nonzero condition or uniqueness condition is imposed on .
Confirmed by the mission captain (proposal self-audit).