openAI-math-dataset / lean /OAI /Analysis /Cuntz /Distribution.lean
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import OAI.Analysis.Cuntz.RegularizedState
import Mathlib.MeasureTheory.Integral.IntervalIntegral.Basic
namespace OAI
noncomputable section
/-! Spectral distributions, integrals, and scalar covariance. -/
open scoped Matrix CStarAlgebra
open MeasureTheory
namespace Kaplansky
attribute [local instance] CStarAlgebra.spectralOrder CStarAlgebra.spectralOrderedRing
variable {A : Type*} [CStarAlgebra A]
/-- A positive cutoff vanishes above the actual spectral norm bound. -/
theorem cuntzCutoff_eq_zero_of_norm_le {a : A} (ha : 0 ≤ a) {ε : ℝ} (hε : ‖a‖ ≤ ε) :
cuntzCutoff a ε = 0 := by
nontriviality A
change cfc (fun t : ℝ => max (t-ε) 0) a = 0
rw [← cfc_zero ℝ a]
apply cfc_congr
intro t ht
have h : t ≤ ε := by
simpa only [Real.norm_of_nonneg (spectrum_nonneg_of_nonneg ha ht)] using
(spectrum.norm_le_norm_of_mem ht).trans hε
exact max_eq_right (sub_nonpos.mpr h)
theorem cuntzCutoff_one_of_lt {t : ℝ} (ht : t < 1) :
cuntzCutoff (1 : A) t = (1 - t) • (1 : A) := by
have h := cfc_algebraMap (A := A) (1 : ℝ) (fun x : ℝ => max (x - t) 0)
simpa only [cuntzCutoff, map_one, max_eq_left (sub_nonneg.mpr ht.le),
Algebra.algebraMap_eq_smul_one] using h
namespace CuntzDimensionState
theorem regularized_of_matrix_eq_zero (d : CuntzDimensionState A) (a : CuntzPositiveMatrix A)
(ha : a.matrix = 0) : d.regularized a = 0 := by
rw [← d.regularized_zero]
apply d.regularized_eq
change CuntzEquivalent a.matrix (0 : Matrix (Fin 0) (Fin 0) A)
rw [ha]
exact ⟨CuntzLE.zero _, CuntzLE.zero _⟩
/-- The regularized spectral distribution attached to an actual positive matrix. -/
def distribution (d : CuntzDimensionState A) (a : CuntzPositiveMatrix A) (t : ℝ) : ℝ :=
d.regularized (a.cutoff t)
theorem distribution_antitone (d : CuntzDimensionState A) (a : CuntzPositiveMatrix A) :
Antitone (d.distribution a) := by
intro s t h
exact d.regularized_mono (a := a.cutoff t) (b := a.cutoff s)
(cuntzLE_of_nonneg_le (cuntzCutoff_nonneg _ _) (cuntzCutoff_antitone _ h))
theorem distribution_nonneg (d : CuntzDimensionState A) (a : CuntzPositiveMatrix A) (t : ℝ) :
0 ≤ d.distribution a t := d.regularized_nonneg _
theorem distribution_le_size (d : CuntzDimensionState A) (a : CuntzPositiveMatrix A) (t : ℝ) :
d.distribution a t ≤ a.size := d.regularized_le_size _
theorem distribution_eq_zero (d : CuntzDimensionState A) (a : CuntzPositiveMatrix A)
{t : ℝ} (ht : ‖CStarMatrix.ofMatrix a.matrix‖ ≤ t) : d.distribution a t = 0 := by
apply d.regularized_of_matrix_eq_zero
change CStarMatrix.ofMatrix.symm (cuntzCutoff (CStarMatrix.ofMatrix a.matrix) t) = 0
rw [cuntzCutoff_eq_zero_of_norm_le a.nonneg ht]
rfl
theorem distribution_intervalIntegrable (d : CuntzDimensionState A) (a : CuntzPositiveMatrix A)
(s t : ℝ) : IntervalIntegrable (d.distribution a) volume s t :=
(d.distribution_antitone a).intervalIntegrable
theorem distribution_add (d : CuntzDimensionState A) (a b : CuntzPositiveMatrix A) (t : ℝ) :
d.distribution (a.add b) t = d.distribution a t + d.distribution b t := by
simp only [distribution, CuntzPositiveMatrix.cutoff_add, d.regularized_add]
/-- Finite layer-cake integral. This definition does not assert the quasitrace axioms. -/
def spectralValue (d : CuntzDimensionState A) (a : CuntzPositiveMatrix A) : ℝ :=
∫ t in (0 : ℝ)..‖CStarMatrix.ofMatrix a.matrix‖, d.distribution a t
theorem spectralValue_nonneg (d : CuntzDimensionState A) (a : CuntzPositiveMatrix A) :
0 ≤ d.spectralValue a :=
intervalIntegral.integral_nonneg_of_forall (norm_nonneg _) (d.distribution_nonneg a)
theorem spectralValue_le_norm_mul_size (d : CuntzDimensionState A) (a : CuntzPositiveMatrix A) :
d.spectralValue a ≤ ‖CStarMatrix.ofMatrix a.matrix‖ * a.size := by
have h := intervalIntegral.integral_mono_on (norm_nonneg (CStarMatrix.ofMatrix a.matrix))
(d.distribution_intervalIntegrable a 0 _) intervalIntegrable_const
(fun t _ => d.distribution_le_size a t)
simpa only [spectralValue, intervalIntegral.integral_const, sub_zero, smul_eq_mul] using h
/-- The layer-cake integral is independent of an upper endpoint past the norm. -/
theorem spectralValue_eq_integral (d : CuntzDimensionState A) (a : CuntzPositiveMatrix A)
{R : ℝ} (hR : ‖CStarMatrix.ofMatrix a.matrix‖ ≤ R) :
d.spectralValue a = ∫ t in (0 : ℝ)..R, d.distribution a t := by
have he : (∫ t in ‖CStarMatrix.ofMatrix a.matrix‖..R, d.distribution a t) = 0 := by
have h := intervalIntegral.integral_congr_Ioo_of_le (μ := volume) hR
(g := fun _ : ℝ => (0 : ℝ)) (fun t ht => d.distribution_eq_zero a ht.1.le)
simpa using h
have h := intervalIntegral.integral_add_adjacent_intervals
(d.distribution_intervalIntegrable a 0 ‖CStarMatrix.ofMatrix a.matrix‖)
(d.distribution_intervalIntegrable a ‖CStarMatrix.ofMatrix a.matrix‖ R)
rw [he, add_zero] at h
exact h
/-- Block addition is preserved by the finite spectral integral. -/
theorem spectralValue_add (d : CuntzDimensionState A) (a b : CuntzPositiveMatrix A) :
d.spectralValue (a.add b) = d.spectralValue a + d.spectralValue b := by
let R := ‖CStarMatrix.ofMatrix (a.add b).matrix‖ +
‖CStarMatrix.ofMatrix a.matrix‖ + ‖CStarMatrix.ofMatrix b.matrix‖
have ha : ‖CStarMatrix.ofMatrix a.matrix‖ ≤ R := by
dsimp [R]
linarith [norm_nonneg (CStarMatrix.ofMatrix (a.add b).matrix),
norm_nonneg (CStarMatrix.ofMatrix a.matrix), norm_nonneg (CStarMatrix.ofMatrix b.matrix)]
have hb : ‖CStarMatrix.ofMatrix b.matrix‖ ≤ R := by
dsimp [R]
linarith [norm_nonneg (CStarMatrix.ofMatrix (a.add b).matrix),
norm_nonneg (CStarMatrix.ofMatrix a.matrix), norm_nonneg (CStarMatrix.ofMatrix b.matrix)]
have hab : ‖CStarMatrix.ofMatrix (a.add b).matrix‖ ≤ R := by
dsimp [R]
linarith [norm_nonneg (CStarMatrix.ofMatrix (a.add b).matrix),
norm_nonneg (CStarMatrix.ofMatrix a.matrix), norm_nonneg (CStarMatrix.ofMatrix b.matrix)]
rw [d.spectralValue_eq_integral _ hab, d.spectralValue_eq_integral _ ha,
d.spectralValue_eq_integral _ hb]
simp_rw [d.distribution_add]
exact intervalIntegral.integral_add (d.distribution_intervalIntegrable a 0 R)
(d.distribution_intervalIntegrable b 0 R)
/-- A normalized dimension state forces the underlying algebra to be nonzero. -/
theorem nontrivial (d : CuntzDimensionState A) : Nontrivial A := by
rcases subsingleton_or_nontrivial A with h | h
· let := h
have he := d.regularized_of_matrix_eq_zero (.unit 1) (Subsingleton.elim _ _)
rw [d.regularized_unit] at he
norm_num at he
· exact h
theorem distribution_unit_of_lt (d : CuntzDimensionState A) (n : ℕ) {t : ℝ} (ht : t < 1) :
d.distribution (.unit n) t = (n : ℝ) := by
have hp : 0 < 1-t := sub_pos.mpr ht
have he := cuntzCutoff_one_of_lt (A := CStarMatrix (Fin n) (Fin n) A) ht
rw [← d.regularized_unit n]
apply d.regularized_eq
change CuntzEquivalent (CStarMatrix.ofMatrix.symm
(cuntzCutoff (1 : CStarMatrix (Fin n) (Fin n) A) t)) (1 : Matrix (Fin n) (Fin n) A)
rw [he]
exact cuntzEquivalent_pos_smul 1 hp
/-- The finite spectral integral has exactly the required matrix-unit values. -/
theorem spectralValue_unit (d : CuntzDimensionState A) (n : ℕ) :
d.spectralValue (.unit n) = (n : ℝ) := by
by_cases hn : n = 0
· subst n
have h := d.spectralValue_le_norm_mul_size (.unit 0)
have hp := d.spectralValue_nonneg (.unit 0)
change d.spectralValue (.unit 0) ≤ ‖CStarMatrix.ofMatrix (CuntzPositiveMatrix.unit (A := A) 0).matrix‖ * ((0 : ℕ) : ℝ) at h
rw [Nat.cast_zero, mul_zero] at h
simpa only [Nat.cast_zero] using le_antisymm h hp
· let := d.nontrivial
let : Nonempty (Fin n) := Fin.pos_iff_nonempty.mp (Nat.pos_of_ne_zero hn)
change (∫ t in (0 : ℝ)..‖(1 : CStarMatrix (Fin n) (Fin n) A)‖,
d.distribution (.unit n) t) = _
rw [norm_one]
have he := intervalIntegral.integral_congr_Ioo_of_le (μ := volume) (by norm_num : (0 : ℝ) ≤ 1)
(f := d.distribution (.unit n)) (g := fun _ : ℝ => (n : ℝ))
(fun t ht => d.distribution_unit_of_lt n ht.2)
simpa using he
end CuntzDimensionState
/-- Scaling the element rescales the spectral threshold. -/
theorem cuntzCutoff_pos_smul {a : A} (ha : 0 ≤ a) {r : ℝ} (hr : 0 < r) (t : ℝ) :
cuntzCutoff (r • a) t = r • cuntzCutoff a (t/r) := by
unfold cuntzCutoff
rw [← cfc_comp_const_mul r (fun s : ℝ => max (s-t) 0) a (ha := ha.isSelfAdjoint),
← cfc_const_mul r (fun s : ℝ => max (s-t/r) 0) a]
apply cfc_congr
intro s hs
dsimp only
rw [mul_max_of_nonneg _ _ hr.le, mul_zero, mul_sub, mul_div_cancel₀ _ hr.ne']
/-- Nonnegative scalar multiplication of a positive finite matrix. -/
def CuntzPositiveMatrix.scale (a : CuntzPositiveMatrix A) (r : ℝ) (hr : 0 ≤ r) :
CuntzPositiveMatrix A :=
⟨a.size, CStarMatrix.ofMatrix.symm (r • CStarMatrix.ofMatrix a.matrix),
smul_nonneg hr a.nonneg⟩
namespace CuntzDimensionState
theorem distribution_pos_scale (d : CuntzDimensionState A) (a : CuntzPositiveMatrix A)
{r : ℝ} (hr : 0 < r) (t : ℝ) :
d.distribution (a.scale r hr.le) t = d.distribution a (t/r) := by
apply d.regularized_eq
have he := cuntzCutoff_pos_smul a.nonneg hr t
have hc := cuntzEquivalent_pos_smul
(cuntzCutoff (CStarMatrix.ofMatrix a.matrix) (t / r)) hr
exact Eq.mpr (congrArg (fun c => CuntzEquivalent (CStarMatrix.ofMatrix.symm c)
(CStarMatrix.ofMatrix.symm (cuntzCutoff (CStarMatrix.ofMatrix a.matrix) (t / r)))) he) hc
/-- The integral scales with the positive scalar, including zero. -/
theorem spectralValue_scale (d : CuntzDimensionState A) (a : CuntzPositiveMatrix A)
(r : ℝ) (hr : 0 ≤ r) :
d.spectralValue (a.scale r hr) = r * d.spectralValue a := by
rcases eq_or_lt_of_le hr with h | h
· subst r
have hn : ‖CStarMatrix.ofMatrix (a.scale 0 hr).matrix‖ = 0 := by
change ‖(0 : ℝ) • CStarMatrix.ofMatrix a.matrix‖ = 0
rw [zero_smul, norm_zero]
simp only [spectralValue, hn, intervalIntegral.integral_same, zero_mul]
· let : NormedSpace ℝ (CStarMatrix (Fin a.size) (Fin a.size) A) :=
NormedSpace.restrictScalars ℝ ℂ _
let : NormSMulClass ℝ (CStarMatrix (Fin a.size) (Fin a.size) A) :=
NormedSpace.toNormSMulClass
change (∫ t in (0 : ℝ)..‖r • CStarMatrix.ofMatrix a.matrix‖,
d.distribution (a.scale r hr) t) = _
simp_rw [d.distribution_pos_scale a h]
rw [norm_smul, Real.norm_of_nonneg hr, intervalIntegral.integral_comp_div _ h.ne']
simp only [zero_div, mul_div_cancel_left₀ _ h.ne', smul_eq_mul, spectralValue]
end CuntzDimensionState
end Kaplansky
end
end OAI