openAI-math-dataset / lean /OAI /Analysis /Crouzeix /FiniteCompression.lean
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import Mathlib
import OAI.Analysis.Crouzeix.NumericalRange
namespace OAI
/-! Exact polynomial compression on spans of finitely many operator iterates. -/
noncomputable section
open scoped TensorProduct Matrix.Norms.L2Operator InnerProductSpace
open Set
namespace CrouzeixHilbert
universe u
variable {H : Type u} [NormedAddCommGroup H] [InnerProductSpace ℂ H]
def compression (A : Operator H) (E : Submodule ℂ H) [E.HasOrthogonalProjection] :
Operator E := E.orthogonalProjectionOnto.comp (A.comp E.subtypeL)
theorem compression_inner (A : Operator H) (E : Submodule ℂ H)
[E.HasOrthogonalProjection] (x : E) :
⟪x, compression A E x⟫_ℂ = ⟪(x : H), A (x : H)⟫_ℂ :=
Submodule.inner_orthogonalProjectionOnto_eq_of_mem_left x (A x)
theorem numericalRange_compression_subset (A : Operator H) (E : Submodule ℂ H)
[E.HasOrthogonalProjection] : numericalRange (compression A E) ⊆ numericalRange A := by
rintro z ⟨x, hx, hz⟩
exact ⟨(x : H), hx, (compression_inner A E x).symm.trans hz⟩
theorem coe_compression_apply (A : Operator H) (E : Submodule ℂ H)
[E.HasOrthogonalProjection] (x : E) (hx : A (x : H) ∈ E) :
(compression A E x : H) = A (x : H) := by
have hh := Submodule.orthogonalProjectionOnto_mem_subspace_eq_self (⟨A x, hx⟩ : E)
exact congrArg (fun v : E => (v : H)) hh
theorem coe_compression_pow (A : Operator H) (E : Submodule ℂ H)
[E.HasOrthogonalProjection] (x : E) (k : ℕ)
(hx : ∀ j : ℕ, j ≤ k → (A ^ j) (x : H) ∈ E) :
(((compression A E) ^ k) x : H) = (A ^ k) (x : H) := by
induction k with
| zero => simp
| succ k ih =>
have hk := ih (fun j hj => hx j (hj.trans (Nat.le_succ k)))
have hmem : A (((compression A E) ^ k) x : H) ∈ E := by
rw [hk]
simpa only [pow_succ', mul_apply_eq_comp] using hx (k + 1) le_rfl
simpa only [pow_succ', mul_apply_eq_comp] using
(coe_compression_apply A E (((compression A E) ^ k) x) hmem).trans (congrArg A hk)
def iterateSpan (A : Operator H) (d : ℕ) {l : ℕ} (ξ : Fin l → H) : Submodule ℂ H :=
Submodule.span ℂ (Set.range (fun p : Fin (d + 1) × Fin l => (A ^ (p.1 : ℕ)) (ξ p.2)))
instance iterateSpanFiniteDimensional (A : Operator H) (d : ℕ) {l : ℕ} (ξ : Fin l → H) :
FiniteDimensional ℂ (iterateSpan A d ξ) :=
FiniteDimensional.span_of_finite ℂ (Set.finite_range _)
theorem iterate_mem_iterateSpan (A : Operator H) (d : ℕ) {l : ℕ} (ξ : Fin l → H)
(j : Fin l) (k : ℕ) (hk : k ≤ d) : (A ^ k) (ξ j) ∈ iterateSpan A d ξ := by
apply Submodule.subset_span
exact ⟨(⟨k, Nat.lt_succ_of_le hk⟩, j), rfl⟩
theorem mem_iterateSpan (A : Operator H) (d : ℕ) {l : ℕ} (ξ : Fin l → H)
(j : Fin l) : ξ j ∈ iterateSpan A d ξ := by
simpa using iterate_mem_iterateSpan A d ξ j 0 (Nat.zero_le _)
def amplificationInclusion (E : Submodule ℂ H) (m : ℕ) :
Amplification E m →L[ℂ] Amplification H m :=
(TensorProduct.mapL E.subtypeL (ContinuousLinearMap.id ℂ (EuclideanSpace ℂ (Fin m)))).completion
theorem norm_amplificationInclusion (E : Submodule ℂ H) (m : ℕ)
(x : Amplification E m) : ‖amplificationInclusion E m x‖ = ‖x‖ := by
refine UniformSpace.Completion.induction_on x
(isClosed_eq (amplificationInclusion E m).continuous.norm continuous_norm) ?_
intro a
simp only [amplificationInclusion, ContinuousLinearMap.completion_apply_coe,
UniformSpace.Completion.norm_coe]
rw [TensorProduct.mapL_apply]
change ‖TensorProduct.map E.subtypeₗᵢ.toLinearMap
(LinearIsometry.id : EuclideanSpace ℂ (Fin m) →ₗᵢ[ℂ] EuclideanSpace ℂ (Fin m)).toLinearMap a‖ = ‖a‖
exact (TensorProduct.mapIsometry E.subtypeₗᵢ (LinearIsometry.id :
EuclideanSpace ℂ (Fin m) →ₗᵢ[ℂ] EuclideanSpace ℂ (Fin m))).norm_map a
@[simp]
theorem amplificationInclusion_tmul (E : Submodule ℂ H) {m : ℕ}
(x : E) (y : EuclideanSpace ℂ (Fin m)) :
amplificationInclusion E m (↑(x ⊗ₜ[ℂ] y)) =
(↑((x : H) ⊗ₜ[ℂ] y) : Amplification H m) := by
simp [amplificationInclusion, ContinuousLinearMap.completion_apply_coe]
theorem polynomialEval_tmul (A : Operator H) {m d : ℕ} (B : Fin (d + 1) → Coeff m)
(x : H) (y : EuclideanSpace ℂ (Fin m)) :
polynomialEval A B (↑(x ⊗ₜ[ℂ] y)) =
∑ k : Fin (d + 1), (↑(((A ^ (k : ℕ)) x) ⊗ₜ[ℂ]
(Matrix.toEuclideanCLM (𝕜 := ℂ) (n := Fin m) (B k) y)) : Amplification H m) := by
simp only [polynomialEval, sum_apply, tensorOperator,
ContinuousLinearMap.completion_apply_coe, TensorProduct.mapL_tmul]
theorem polynomial_compression_tmul (A : Operator H) (E : Submodule ℂ H)
[E.HasOrthogonalProjection] {m d : ℕ} (B : Fin (d + 1) → Coeff m)
(x : E) (y : EuclideanSpace ℂ (Fin m))
(hx : ∀ j : ℕ, j ≤ d → (A ^ j) (x : H) ∈ E) :
polynomialEval A B (amplificationInclusion E m (↑(x ⊗ₜ[ℂ] y))) =
amplificationInclusion E m (polynomialEval (compression A E) B (↑(x ⊗ₜ[ℂ] y))) := by
simp only [amplificationInclusion_tmul, polynomialEval_tmul, map_sum]
apply Finset.sum_congr rfl
intro k _
rw [coe_compression_pow A E x k (fun j hj => hx j (hj.trans (Nat.le_of_lt_succ k.isLt)))]
theorem completion_coe_finsetSum {V : Type*} [NormedAddCommGroup V]
{ι : Type*} (s : Finset ι) (f : ι → V) :
(↑(∑ i ∈ s, f i) : UniformSpace.Completion V) =
∑ i ∈ s, (↑(f i) : UniformSpace.Completion V) :=
map_sum (UniformSpace.Completion.toCompl : V →+ UniformSpace.Completion V) f s
theorem polynomial_bound_of_finite_compressions (A : Operator H)
{m d : ℕ} (B : Fin (d + 1) → Coeff m) {C : ℝ} (hC : 0 ≤ C)
(hfin : ∀ (E : Submodule ℂ H) [FiniteDimensional ℂ E],
‖polynomialEval (compression A E) B‖ ≤ C) :
‖polynomialEval A B‖ ≤ C := by
apply (polynomialEval A B).opNorm_le_bound hC
intro x
refine UniformSpace.Completion.induction_on x
(isClosed_le (polynomialEval A B).continuous.norm (continuous_const.mul continuous_norm)) ?_
intro a
obtain ⟨l, ξ, η, ha⟩ := TensorProduct.exists_sum_tmul_eq a
let E := iterateSpan A d ξ
let xE (j : Fin l) : E := ⟨ξ j, mem_iterateSpan A d ξ j⟩
let v : Amplification E m := ∑ j : Fin l, (↑(xE j ⊗ₜ[ℂ] η j) : Amplification E m)
have hav : (↑a : Amplification H m) = amplificationInclusion E m v := by
rw [ha, completion_coe_finsetSum]
simp only [v, map_sum, amplificationInclusion_tmul]
rfl
have hinter : polynomialEval A B (amplificationInclusion E m v) =
amplificationInclusion E m (polynomialEval (compression A E) B v) := by
simp only [v, map_sum]
apply Finset.sum_congr rfl
intro j _
apply polynomial_compression_tmul
intro k hk
exact iterate_mem_iterateSpan A d ξ j k hk
calc
‖polynomialEval A B (↑a)‖ =
‖amplificationInclusion E m (polynomialEval (compression A E) B v)‖ := by
rw [hav, hinter]
_ = ‖polynomialEval (compression A E) B v‖ := norm_amplificationInclusion E m _
_ ≤ ‖polynomialEval (compression A E) B‖ * ‖v‖ :=
(polynomialEval (compression A E) B).le_opNorm v
_ ≤ C * ‖v‖ := mul_le_mul_of_nonneg_right (hfin E) (norm_nonneg _)
_ = C * ‖(↑a : Amplification H m)‖ := by rw [hav, norm_amplificationInclusion]
theorem polynomial_inequality_of_finite_compressions (A : Operator H)
{m d : ℕ} (B : Fin (d + 1) → Coeff m)
(hfin : ∀ (E : Submodule ℂ H) [FiniteDimensional ℂ E],
‖polynomialEval (compression A E) B‖ ≤
2 * supNorm (numericalRange (compression A E)) (matrixPolynomial B)) :
‖polynomialEval A B‖ ≤ 2 * supNorm (numericalRange A) (matrixPolynomial B) := by
have hbK := supNorm_bddAbove (isCompact_numericalClosure A)
(continuous_matrixPolynomial B).continuousOn
have hbW : BddAbove (insert 0 ((fun z => ‖matrixPolynomial B z‖) '' numericalRange A)) :=
hbK.mono (Set.insert_subset_insert (Set.image_mono subset_closure))
apply polynomial_bound_of_finite_compressions A B
(mul_nonneg (by norm_num : (0 : ℝ) ≤ 2) (supNorm_nonneg hbW))
intro E _
exact (hfin E).trans (mul_le_mul_of_nonneg_left
(supNorm_mono (numericalRange_compression_subset A E) hbW) (by norm_num))
end CrouzeixHilbert
end
end OAI