openAI-math-dataset / lean /OAI /Geometry /SmoothYau /Smoothness /FiniteTaylorFunctionThree.lean
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import Mathlib
import OAI.Geometry.SmoothYau.Smoothness.BoundedDegreeMonomials
namespace OAI
noncomputable section
open Set Filter
open scoped Topology ContDiff
open Set Filter
open scoped Topology ContDiff
open MvPolynomial
open Set Filter
open scoped ContDiff
open Set Filter
open scoped Topology ContDiff
open Set Filter MvPolynomial
open scoped Topology ContDiff
open Set Filter Function MvPolynomial
open scoped Topology ContDiff
open Set Filter Function MvPolynomial
open scoped Topology ContDiff
open Set Filter
open scoped Topology ContDiff
namespace YauCounterexamples
variable {E X : Type*} [NormedAddCommGroup E] [NormedSpace ℝ E]
[FiniteDimensional ℝ E] [TopologicalSpace X]
omit [FiniteDimensional ℝ E] in
lemma finiteTaylorFunction_three (f : E → ℝ) (x : E) :
finiteTaylorFunction f 3 x = f 0 + fderiv ℝ f 0 x +
(2 : ℝ)⁻¹ * iteratedFDeriv ℝ 2 f 0 (fun _ => x) := by
simp [finiteTaylorFunction, Finset.sum_range_succ, iteratedFDeriv_one_apply,
Nat.factorial, smul_eq_mul]
theorem compact_uniform_negative_hessian_localization
(f : X → E → ℝ) (hf : ∀ p, ContDiff ℝ ∞ (f p))
(hthird : Continuous (fun q : X × E => iteratedFDeriv ℝ 3
(fun y => f q.1 y - finiteTaylorFunction (f q.1) 3 y) q.2))
{K : Set X} (hK : IsCompact K) (δ : X → ℝ) (κ : ℝ) (hκ : 0 < κ)
(hlinear : ∀ p ∈ K, ∀ x, fderiv ℝ (f p) 0 x ≤ δ p * ‖x‖)
(hquadratic : ∀ p ∈ K, ∀ x,
iteratedFDeriv ℝ 2 (f p) 0 (fun _ => x) ≤ -4 * κ * ‖x‖ ^ 2) :
∃ r : ℝ, 0 < r ∧ ∀ p ∈ K, ∀ x ∈ Metric.ball (0 : E) r,
f p x ≤ f p 0 + δ p * ‖x‖ - κ * ‖x‖ ^ 2 := by
obtain ⟨C₀,hC₀⟩ := (hK.prod (isCompact_closedBall (0 : E) 1)).exists_bound_of_continuousOn
hthird.continuousOn
let C := max C₀ 1
have hC : 0 < C := lt_of_lt_of_le zero_lt_one (le_max_right _ _)
refine ⟨min 1 (κ / C), lt_min zero_lt_one (div_pos hκ hC), ?_⟩
intro p hp x hx
have hbound := vanishing_jet_norm_bound
(fun y => f p y - finiteTaylorFunction (f p) 3 y)
((hf p).sub (contDiff_finiteTaylorFunction (f p) 3)) 3 1 C hC.le
(fun k hk => finiteTaylorFunction_remainder_jet (hf p) 3 k hk)
(fun y hy => (hC₀ (p,y) ⟨hp,Metric.ball_subset_closedBall hy⟩).trans (le_max_left _ _))
have hxnorm : ‖x‖ < min 1 (κ / C) := by simpa using hx
have hxball : x ∈ Metric.ball (0 : E) 1 := by
simpa using hxnorm.trans_le (min_le_left _ _)
have hxsmall : C * ‖x‖ ≤ κ := by
have h := (lt_div_iff₀ hC).mp (hxnorm.trans_le (min_le_right _ _))
nlinarith
have herr := hbound 3 (le_refl _) x hxball
change ‖iteratedFDeriv ℝ 0 (fun y => f p y - finiteTaylorFunction (f p) 3 y) x‖ ≤
C * ‖x‖ ^ 3 at herr
rw [norm_iteratedFDeriv_zero, Real.norm_eq_abs] at herr
have hu : f p x - finiteTaylorFunction (f p) 3 x ≤ C * ‖x‖ ^ 3 :=
(le_abs_self _).trans herr
rw [finiteTaylorFunction_three] at hu
have hq := hquadratic p hp x
have hl := hlinear p hp x
have hrem : C * ‖x‖ ^ 3 ≤ κ * ‖x‖ ^ 2 := by
have h := mul_le_mul_of_nonneg_right hxsmall (sq_nonneg ‖x‖)
nlinarith
norm_num at hu
linarith
end YauCounterexamples
end
end OAI