import Mathlib import OAI.Analysis.BiholderTransport.Coordinates.PoleCoordinates import OAI.Analysis.BiholderTransport.Regularity.HopfApproximation namespace OAI noncomputable section open Set Filter Manifold Bundle open scoped Topology ContDiff namespace WeakMTWTransport variable {n : ℕ} {M : Type*} [MetricSpace M] [CompactSpace M] [Nonempty M] [ChartedSpace (Model n) M] [IsManifold 𝓘(ℝ,Model n) ∞ M] [RiemannianBundle (fun x : M => TangentSpace 𝓘(ℝ,Model n) x)] [IsContMDiffRiemannianBundle 𝓘(ℝ,Model n) ∞ (Model n) (fun x : M => TangentSpace 𝓘(ℝ,Model n) x)] [IsRiemannianManifold 𝓘(ℝ,Model n) M] lemma hopfLax_derivative_of_scaled_minimizer {u : M → ℝ} (hu : Continuous u) {x : M} {p : TangentSpace 𝓘(ℝ,Model n) x} {t : ℝ} (ht : 0 u x+cost x a/(t-ε)+cost a y/ε have hG : HasMFDerivAt 𝓘(ℝ,Model n) 𝓘(ℝ,ℝ) G z.1 (innerSL ℝ z.2) := by have H := (hasMFDerivAt_const (I := 𝓘(ℝ,Model n)) (I' := 𝓘(ℝ,ℝ)) (u x+cost x a/(t-ε)) z.1).add (hshort.const_smul ε⁻¹) have heD : 0+ε⁻¹ • innerSL ℝ (ε • (sprayFlow t (⟨x,p⟩ : TangentBundle 𝓘(ℝ,Model n) M)).2) = innerSL ℝ z.2 := by ext v simp only [zero_add,_root_.smul_apply,smul_eq_mul,innerSL_apply_apply, real_inner_smul_left] dsimp [z,w] field_simp replace H := H.congr_mfderiv heD have heG : G = ((fun _ => u x+cost x a/(t-ε)) + ε⁻¹ • fun y => dist (sprayFlow (t-ε) (⟨x,p⟩ : TangentBundle 𝓘(ℝ,Model n) M)).1 y^2/2) := by funext y dsimp [G,cost,a,w] ring rw [heG] exact H have hmin : dist x z.1=t*‖p‖ := by dsimp [z,w] rw [←riemannianExp_smul,hp,norm_smul, Real.norm_eq_abs,abs_of_pos ht] have hleft : dist x a=(t-ε)*‖p‖ := sprayFlow_minimizing_prefix w (sub_pos.mpr hεt).le (by linarith) hmin have hright : dist a z.1=ε*‖p‖ := by have H := sprayFlow_minimizing_subinterval w (sub_pos.mpr hεt).le (by linarith) hmin simpa only [sub_sub_cancel] using H have hcontact : hopfLax t u z.1=G z.1 := by rw [heq] dsimp [G,cost] rw [hmin,hleft,hright] field_simp ring have hle : ∀ y, hopfLax t u y≤G y := by intro y have H := cost_divided_triangle_two_times x a y (sub_pos.mpr hεt) hε rw [sub_add_cancel] at H exact (hopfLax_le hu t y x).trans (by dsimp [G]; linarith) exact hd.hasMFDerivAt.congr_mfderiv ((upper_contact_mfderiv hd hG.mdifferentiableAt (Filter.Eventually.of_forall hle) hcontact).trans hG.mfderiv) lemma hopfLax_backward_minimizer {u : M → ℝ} (hu : Continuous u) {t : ℝ} (ht : 0 TangentSpace 𝓘(ℝ,Model n) x)] [IsRiemannianManifold 𝓘(ℝ,Model n) M] in lemma gradients_cancel_at_local_max {f g : M → ℝ} {z : M} (hf : MDifferentiableAt 𝓘(ℝ,Model n) 𝓘(ℝ,ℝ) f z) (hg : MDifferentiableAt 𝓘(ℝ,Model n) 𝓘(ℝ,ℝ) g z) (hmax : ∀ᶠ a in 𝓝 z,f a+g a≤f z+g z) : riemannianGradient g z= -riemannianGradient (n := n) f z := by let : FiniteDimensional ℝ (TangentSpace 𝓘(ℝ,Model n) z) := inferInstanceAs (FiniteDimensional ℝ (Model n)) have H := upper_contact_mfderiv (hf.add hg) (mdifferentiableAt_const (I := 𝓘(ℝ,Model n)) (I' := 𝓘(ℝ,ℝ))) hmax rfl rw [(hasMFDerivAt_riemannianGradient hf).add (hasMFDerivAt_riemannianGradient hg) |>.mfderiv, (hasMFDerivAt_const (I := 𝓘(ℝ,Model n)) (I' := 𝓘(ℝ,ℝ)) (f z+g z) z).mfderiv] at H have HE : riemannianGradient (n := n) f z+riemannianGradient (n := n) g z=0 := by apply ext_inner_right ℝ intro u have H0 := congrArg (fun L => L u) H change inner ℝ (riemannianGradient f z) u+inner ℝ (riemannianGradient g z) u=0 at H0 simpa only [inner_add_left,inner_zero_left] using H0 exact eq_neg_of_add_eq_zero_left (by simpa only [add_comm] using HE) lemma WeakMTW.hopf_maximum_aligned (hmtw : WeakMTW (n := n) (M := M)) {w v : M → ℝ} (hw : Continuous w) (hv : Continuous v) {t : ℝ} (ht : 0