openAI-math-dataset / lean /OAI /Geometry /NodalSets /Spectral /MetricProjectionStability.lean
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import OAI.Geometry.NodalSets.Spectral.MetricProjectionBounds
namespace OAI
namespace Yau.Geometry
open Yau.Jets
noncomputable section
theorem metric_projection_uniform_stability {M K : ℝ} (hM : 0 ≤ M) (hK : 0 ≤ K) :
∃ ε₀ C : ℝ, 0 < ε₀ ∧ 0 < C ∧
∀ (g h : Coord →L[ℝ] Coord →L[ℝ] ℝ) (e e₀ t : Coord) (ε : ℝ),
‖g‖ ≤ M → ‖e‖ ≤ K → ‖e₀‖ ≤ K → ‖t‖ ≤ K →
g e e = 1 → h t t = 1 → h e₀ t = 0 →
‖e-e₀‖ ≤ ε → ‖g-h‖ ≤ ε → ε ≤ ε₀ →
let q := metricPerpProjection g e t
q ≠ 0 ∧ g (metricNormalize g q) (metricNormalize g q) = 1 ∧
g e (metricNormalize g q) = 0 ∧ ‖metricNormalize g q-t‖ ≤ C*ε := by
let P : ℝ := (M*K+K^2)*K
let H : ℝ := 2*M*(K+1)*P+(K+1)^2
have hP : 0 ≤ P := by dsimp [P]; positivity
have hH : 0 ≤ H := by dsimp [H]; positivity
let ε₀ : ℝ := min (1/(P+1)) (1/(2*(H+1)))
let C : ℝ := 2*P+8*(K+1)*H+1
have hε₀ : 0 < ε₀ := lt_min (by positivity) (by positivity)
have hC : 0 < C := by dsimp [C]; positivity
refine ⟨ε₀,C,hε₀,hC,?_⟩
intro g h e e₀ t ε hg he he₀ ht heunit htunit horth hd hm hε
have hε0 : 0 ≤ ε := (norm_nonneg _).trans hd
have hε1 := hε.trans (min_le_left _ _)
have hε2 := hε.trans (min_le_right _ _)
have hsmall1 : P*ε ≤ 1 := by
have hh := (le_div_iff₀ (by positivity : 0 < P+1)).mp hε1
nlinarith
have hsmall2 : H*ε ≤ 1/2 := by
have hh := (le_div_iff₀ (by positivity : 0 < 2*(H+1))).mp hε2
nlinarith
have h1 : (M*ε*K+ε*K^2)*K ≤ 1 := by convert hsmall1 using 1; dsimp [P]; ring
have h2 : 2*M*(K+1)*((M*ε*K+ε*K^2)*K)+ε*(K+1)^2 ≤ 1/2 := by
convert hsmall2 using 1
dsimp [H,P]
ring
obtain ⟨hn,hu,ho,hb⟩ := normalized_projection_stability g h e e₀ t hM hK hg he he₀ ht
heunit htunit horth hd hm h1 h2
refine ⟨hn,hu,ho,hb.trans ?_⟩
dsimp [C,H,P]
nlinarith only [hε0]
end
end Yau.Geometry
end OAI