No strict linearized descent at a local minimizer
ProvedVectorSpaceOpt.kkt_no_strict_linearized_descentLet x₀ be feasible for the cone inequality G x ≤ₚ 0 and a local minimizer of f on that feasible set. Suppose f and G have continuous-linear Gâteaux derivatives f' and G' at x₀. Then there is no direction h that strictly lowers the linearized objective and simultaneously enters the strict interior of the linearized feasible cone:
The assertion is deliberately based on IsLocalMinOn, not a stronger global-minimum hypothesis. It is the nonintersection fact that turns local optimality into a geometric separation problem and isolates the only use of the nonlinear maps in the KKT argument.
import Definitions.Def_VectorSpaceOpt_cone_optimization open Set
namespace VectorSpaceOpt
/-- The nonintersection step in Luenberger's proof of §9.4, Theorem 1. -/
theorem kkt_no_strict_linearized_descent
{X Z : Type*}
[NormedAddCommGroup X] [NormedSpace ℝ X]
[NormedAddCommGroup Z] [NormedSpace ℝ Z]
(P : ConvexCone ℝ Z) (f : X → ℝ) (G : X → Z) (x₀ : X)
(f' : X →L[ℝ] ℝ) (G' : X →L[ℝ] Z)
(hf : HasGateauxDerivAt f f' x₀) (hG : HasGateauxDerivAt G G' x₀)
(hmin : IsLocalMinOn f {x | coneLE P (G x) 0} x₀)
(hfeas : coneLE P (G x₀) 0) :
¬ ∃ h : X, f' h < 0 ∧ coneLT P (G x₀ + G' h) 0 := by
sorry
end VectorSpaceOptRead-back
What the Lean code literally says, in plain math · gpt-5
Let be real normed spaces, a convex cone in , , , , and , continuous real-linear maps. Assume that for every direction , the line derivatives at of and are and . Assume is a local minimum of relative to the cone-feasible set of all with , and separately assume the feasibility condition . Then there is no for which both and . No nonempty-interior or cone-regularity hypothesis is made, so if is empty the negated existential conclusion is automatic.
Confirmed by the mission captain (proposal self-audit).