Generalized inverse function theorem
ProvedVectorSpaceOpt.generalized_inverse_functionLet and be real Banach spaces, open, and continuously Fréchet differentiable on . Suppose , the derivative of at is the continuous linear map , and is onto. Then there are and such that every target with has a preimage satisfying and the quantitative bound
This is Luenberger's Lyusternik–Graves local-surjectivity theorem and supplies the nonlinear feasibility perturbations needed by the tangent-stationarity milestone.
import Mathlib open Set
namespace VectorSpaceOpt
/-- Luenberger, Chapter 9, §9.2, Theorem 1. -/
theorem generalized_inverse_function
{X Y : Type*}
[NormedAddCommGroup X] [NormedSpace ℝ X] [CompleteSpace X]
[NormedAddCommGroup Y] [NormedSpace ℝ Y] [CompleteSpace Y]
(U : Set X) (T : X → Y) (x₀ : X) (T' : X →L[ℝ] Y)
(hU : IsOpen U) (hx₀ : x₀ ∈ U)
(hT : ContDiffOn ℝ 1 T U) (hT' : HasFDerivAt T T' x₀)
(hregular : Function.Surjective T') :
∃ ε K : ℝ, 0 < ε ∧ 0 ≤ K ∧
∀ y : Y, dist y (T x₀) < ε →
∃ x : X, x ∈ U ∧ T x = y ∧ ‖x - x₀‖ ≤ K * ‖y - T x₀‖ := by
sorry
end VectorSpaceOptRead-back
What the Lean code literally says, in plain math · gpt-5
Let be real Banach spaces, , , , and a continuous real-linear map. Assume is open, , is continuously Fréchet differentiable through order one on , is the Fréchet derivative of at , and is surjective. Then there exist real numbers with and such that every satisfying the strict inequality has at least one preimage with and . The same and work for all such ; neither uniqueness of nor continuity of a chosen right inverse is asserted.
Confirmed by the mission captain (proposal self-audit).