AutoSamplingTheory.TechnicalLemmas.Measure.DisplacementMonotoneDerivative
2 named declarations scanned from AutoSamplingTheory/TechnicalLemmas/Measure/DisplacementMonotoneDerivative.lean.
Declarations
theorem AutoSamplingTheory.TechnicalLemmas.Measure.DisplacementMonotoneDerivative.inner_fderiv_nonneg_of_monotone Partial Not mapped
- The Frechet derivative of a monotone Hilbert-space map has nonnegative quadratic form in every direction. No symmetry of the derivative is used.
theorem inner_fderiv_nonneg_of_monotone
{T : E → E} {A : E →L[ℝ] E} {x : E}
(hmono : IsMonotoneMap T) (hderiv : HasFDerivAt T A x) (v : E) :
0 ≤ ⟪A v, v⟫ := by
let ell : ℝ → E := fun r => AffineMap.lineMap x (x + v) r
have hell : ∀ r : ℝ, ell r = x + r • v := by
intro r
simp [ell, AffineMap.lineMap_apply_module', add_comm]
let g : ℝ → ℝ := (innerSL ℝ v) ∘ (T ∘ ell)
have hgmono : Monotone g := by
intro s t hst
by_cases heq : s = t
· simp [heq]
have hlt : s < t := lt_of_le_of_ne hst heq
have hm := hmono (ell t) (ell s)
rw [hell t, hell s] at hm
have harg :
(x + t • v) - (x + s • v) = (t - s) • v := by
rw [sub_smul]
abel
rw [harg, real_inner_smul_right] at hm
have hdiff :
g t - g s =
⟪T (x + t • v) - T (x + s • v), v⟫ := by
calc
g t - g s =
⟪v, T (x + t • v) - T (x + s • v)⟫ := by
simp [g, hell, inner_sub_right]
_ = ⟪T (x + t • v) - T (x + s • v), v⟫ :=
real_inner_comm _ _
rw [← hdiff] at hm
nlinarith [sub_pos.mpr hlt]
have hellDeriv : HasDerivAt ell v 0 := by
dsimp [ell]
simpa using
(AffineMap.hasDerivAt_lineMap
(a := x) (b := x + v) (x := (0 : ℝ)))
have hTline : HasDerivAt (T ∘ ell) (A v) 0 := by
exact hderiv.comp_hasDerivAt_of_eq (0 : ℝ) hellDeriv (by simp [ell])
have hgderiv : HasDerivAt g ((innerSL ℝ v) (A v)) 0 := by
exact (innerSL ℝ v).hasFDerivAt.comp_hasDerivAt (0 : ℝ) hTline
have hnonneg : 0 ≤ (innerSL ℝ v) (A v) :=
hgderiv.nonneg_of_monotone hgmono
simpa [real_inner_comm] using hnonneg
-- Source excerpt truncated; follow the exact source link.
AutoSamplingTheory/TechnicalLemmas/Measure/DisplacementMonotoneDerivative.lean:41published source at 0e31a3cda412
Excerpt truncated; the exact source link is authoritative.
theorem AutoSamplingTheory.TechnicalLemmas.Measure.DisplacementMonotoneDerivative.isPositive_fderiv_of_monotone_of_isSymmetric Partial Not mapped
- If the derivative of a monotone map is additionally symmetric, then it is a positive operator. This packages the exact remaining split needed for a future gradient/Hessian regularity edge: monotonicity supplies the quadratic inequality, while symmetry must be proved separately.
theorem isPositive_fderiv_of_monotone_of_isSymmetric
{T : E → E} {A : E →L[ℝ] E} {x : E}
(hmono : IsMonotoneMap T) (hderiv : HasFDerivAt T A x)
(hsymm : A.IsSymmetric) :
A.IsPositive := by
rw [ContinuousLinearMap.isPositive_iff]
exact ⟨hsymm, inner_fderiv_nonneg_of_monotone hmono hderiv⟩
end DisplacementMonotoneDerivative
end Measure
end TechnicalLemmas
end AutoSamplingTheory
AutoSamplingTheory/TechnicalLemmas/Measure/DisplacementMonotoneDerivative.lean:90published source at 0e31a3cda412