openAI-math-dataset / lean /OAI /Analysis /CircleDomains /Modulus /CompactMajorantSeparation.lean
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Add lean source and binary files (part 12)
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import OAI.Analysis.CircleDomains.Topology.CompactPositiveFunctional
import Mathlib.Analysis.Convex.Cone.Extension
import Mathlib.Topology.ContinuousMap.Algebra
namespace OAI
/-! Hahn–Banach separation for a compact family of measures and a
sublinear majorant cost. -/
open Set MeasureTheory
open scoped CompactlySupported
noncomputable section
namespace CircleDomainRigidity
lemma exists_compact_positive_functional_of_majorant_cost
{X : Type*} [TopologicalSpace X] [CompactSpace X] [Nonempty X]
(p : C_c(X, ℝ) → ℝ)
(phom : ∀ c : ℝ, 0 < c → ∀ f, p (c • f) = c * p f)
(padd : ∀ f g, p (f+g) ≤ p f + p g)
(pneg : ∀ f, (∀ x, 0 ≤ f x) → p (-f) = 0)
(c : ℝ)
(pconst : ∀ r : ℝ, r*c ≤ p
(r • (⟨1,HasCompactSupport.of_compactSpace (1 : C(X,ℝ))⟩ : C_c(X,ℝ)))) :
∃ Λ : C_c(X,ℝ) →ₚ[ℝ] ℝ,
Λ ⟨1,HasCompactSupport.of_compactSpace (1 : C(X,ℝ))⟩ = c ∧
∀ f, Λ f ≤ p f := by
classical
let oneX : C_c(X,ℝ) := ⟨1,HasCompactSupport.of_compactSpace (1 : C(X,ℝ))⟩
let V : Submodule ℝ C_c(X,ℝ) := Submodule.span ℝ {oneX}
let x₀ : X := Classical.choice inferInstance
let ev : C_c(X,ℝ) →ₗ[ℝ] ℝ :=
{ toFun := fun f => f x₀
map_add' := fun _ _ => rfl
map_smul' := fun _ _ => rfl }
let l : V →ₗ[ℝ] ℝ := c • (ev.comp V.subtype)
have hl (f : V) : l f ≤ p f := by
obtain ⟨r,hr⟩ := Submodule.mem_span_singleton.mp f.property
change c * ev f.val ≤ p f.val
rw [← hr]
simpa [ev,oneX,mul_comm] using pconst r
obtain ⟨L,hL,hLp⟩ := exists_extension_of_le_sublinear
(⟨V,l⟩ : C_c(X,ℝ) →ₗ.[ℝ] ℝ) p phom padd hl
have hpos (f : C_c(X,ℝ)) (hf : 0 ≤ f) : 0 ≤ L f := by
have hn := hLp (-f)
rw [pneg f (fun x => hf x),map_neg] at hn
linarith
refine ⟨PositiveLinearMap.mk₀ L hpos,?_,hLp⟩
have he := hL (⟨oneX,Submodule.mem_span_singleton_self oneX⟩ : V)
change L oneX = c
simpa [l,ev,oneX] using he
end CircleDomainRigidity
end
end OAI