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Add lean source and binary files (part 19)
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import OAI.Analysis.HyperbolicCones.KernelSmooth
import OAI.Analysis.HyperbolicCones.TangentBasic
namespace OAI
noncomputable section
open scoped ContDiff Topology RealInnerProductSpace Matrix.Norms.L2Operator
open Set Filter
namespace Paper256
theorem kernel_projection_tangent_derivative (c : ℕ) (hC : 0 < c)
(E : Sym 4 →ₗ[ℝ] Sym c) :
∃ (v : Vec 4) (T : Vec 4 →ₗ[ℝ] Mat c ℝ),
‖v‖ = 1 ∧ v 0 ≠ 0 ∧ ∀ h : Vec 4, inner ℝ v h = 0 →
HasDerivAt (fun s : ℝ => kernelProjection
(E (1 - outerSym (tangentCurve v h s)) : Mat c ℝ)) (T h) 0 := by
obtain ⟨v, U, F, hv, hv0, hU, hvU, hF, heq⟩ :=
kernel_projection_smooth_local_extension c hC E
have hd : DifferentiableAt ℝ F v :=
(hF.contDiffAt (hU.mem_nhds hvU)).differentiableAt (by norm_num)
refine ⟨v, (fderiv ℝ F v).toLinearMap, hv, hv0, ?_⟩
intro h hh
have hc := tangentCurve_derivative v h
have hcomp : HasDerivAt (fun s => F (tangentCurve v h s)) ((fderiv ℝ F v) h) 0 := by
simpa only [Function.comp_apply] using!
hd.hasFDerivAt.comp_hasDerivAt_of_eq 0 hc (by simp [tangentCurve])
apply hcomp.congr_of_eventuallyEq
have hmem : ∀ᶠ s in 𝓝 (0 : ℝ), tangentCurve v h s ∈ U := by
apply hc.continuousAt.preimage_mem_nhds
simpa [tangentCurve] using hU.mem_nhds hvU
filter_upwards [hmem] with s hs
exact (heq _ hs (tangentCurve_unit v h hv hh s)).symm
end Paper256
end
end OAI