openAI-math-dataset / lean /OAI /Probability /BinarySweep /Processes /CoordinateLayerEdgeSwap.lean
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Add lean source and binary files (part 119)
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import Mathlib
import OAI.Probability.BinarySweep.Processes.SupportTimeBoundBounds
namespace OAI
noncomputable section
open scoped BigOperators
namespace BinaryCoordinateSweeps
lemma coordinateLayer_edgeSwap (d : ℕ) (j : Fin d) (x : Slot d) :
coordinateLayer d j (fun y => decide (y = fun i : {i : Fin d // i ≠ j} => x i)) =
Equiv.swap x (Function.update x j (!x j)) := by
classical
let e := Equiv.piSplitAt j (fun _ : Fin d => Bool)
let z : {i : Fin d // i ≠ j} → Bool := fun i => x i
change (e.trans (Equiv.prodCongrLeft (fun y => boolSwitch (decide (y = z))))).trans e.symm = _
rw [fiber_boolSwitch_eq_swap z (x j), Equiv.trans_swap_trans_symm]
have hx : e.symm (x j, z) = x := by
exact e.symm_apply_apply x
have hy : e.symm (!x j, z) = Function.update x j (!x j) := by
ext i
by_cases h : i = j
· subst i; simp [e, Equiv.piSplitAt_symm_apply]
· simp [e, z, Equiv.piSplitAt_symm_apply, h]
rw [hx, hy]
lemma swap_update_mem_reachable (d : ℕ) (j : Fin d) (x : Slot d) (b : Bool) :
Equiv.swap x (Function.update x j b) ∈ sweepReachable d := by
classical
by_cases h : b = x j
· subst b
simp only [Function.update_eq_self, Equiv.swap_self]
exact (sweepReachable d).one_mem
· have hb : b = !x j := by cases b <;> cases hx : x j <;> simp_all
rw [hb, ← coordinateLayer_edgeSwap]
exact coordinateLayer_mem_reachable d j _
lemma swap_mem_sweepReachable (d : ℕ) (x y : Slot d) :
Equiv.swap x y ∈ sweepReachable d := by
classical
have h (s : Finset (Fin d)) :
Equiv.swap x (fun i => if i ∈ s then y i else x i) ∈ sweepReachable d := by
induction s using Finset.induction_on with
| empty =>
simp only [Finset.notMem_empty, ite_false, Equiv.swap_self]
exact (sweepReachable d).one_mem
| @insert j s hj ih =>
have he : (fun i => if i ∈ insert j s then y i else x i) =
Function.update (fun i => if i ∈ s then y i else x i) j (y j) := by
ext i
by_cases hi : i = j
· subst i; simp
· simp [hi]
rw [he]
exact SubmonoidClass.swap_mem_trans (sweepReachable d) ih
(swap_update_mem_reachable d j _ _)
simpa using h Finset.univ
theorem every_permutation_sweepReachable (d : ℕ) (g : Equiv.Perm (Slot d)) :
g ∈ sweepReachable d := by
classical
induction g using Equiv.Perm.swap_induction_on with
| one => exact (sweepReachable d).one_mem
| swap_mul g x y h ih =>
exact (sweepReachable d).mul_mem (swap_mem_sweepReachable d x y) ih
section FiniteRandomWalk
variable {G : Type*} [Fintype G] [Group G]
def IsProbability (p : G → ℝ) : Prop := (∀ g, 0 ≤ p g) ∧ ∑ g, p g = 1
lemma sum_mulLeft (a : G) (f : G → ℝ) : ∑ g, f (a * g) = ∑ g, f g :=
Equiv.sum_comp (Equiv.mulLeft a) f
lemma sum_inv_mulRight (a : G) (f : G → ℝ) : ∑ g, f (g⁻¹ * a) = ∑ g, f g :=
((Equiv.inv G).trans (Equiv.mulRight a)).sum_comp f
lemma convolution_sum (p q : G → ℝ) :
∑ g, convolution p q g = (∑ g, p g) * (∑ g, q g) := by
simp only [convolution]
rw [Finset.sum_comm]
simp_rw [← Finset.mul_sum, sum_mulLeft]
rw [Finset.sum_mul]
lemma convolution_nonneg {p q : G → ℝ} (hp : ∀ g, 0 ≤ p g) (hq : ∀ g, 0 ≤ q g) :
∀ g, 0 ≤ convolution p q g := fun _ =>
Finset.sum_nonneg (fun x _ => mul_nonneg (hp x) (hq _))
lemma convolution_probability {p q : G → ℝ} (hp : IsProbability p) (hq : IsProbability q) :
IsProbability (convolution p q) := by
exact ⟨convolution_nonneg hp.1 hq.1, by rw [convolution_sum, hp.2, hq.2, one_mul]⟩
lemma convolution_uniform_right {p : G → ℝ} (hp : ∑ g, p g = 1) :
convolution p (uniformLaw G) = uniformLaw G := by
funext g
simp [convolution, uniformLaw, ← Finset.sum_mul, hp]
lemma convolution_uniform_left {p : G → ℝ} (hp : ∑ g, p g = 1) :
convolution (uniformLaw G) p = uniformLaw G := by
funext g
simp [convolution, uniformLaw, ← Finset.mul_sum, sum_inv_mulRight, hp]
lemma convolution_assoc (p q r : G → ℝ) :
convolution (convolution p q) r = convolution p (convolution q r) := by
funext g
simp only [convolution, Finset.sum_mul, Finset.mul_sum]
rw [Finset.sum_comm]
apply Finset.sum_congr rfl
intro x _
rw [← sum_mulLeft x (fun y => p x * q (x⁻¹ * y) * r (y⁻¹ * g))]
simp [mul_assoc]
omit [Group G] in
lemma totalVariation_nonneg (p q : G → ℝ) : 0 ≤ totalVariation p q :=
mul_nonneg (by norm_num) (Finset.sum_nonneg (fun _ _ => abs_nonneg _))
omit [Group G] in
lemma totalVariation_le_one {p q : G → ℝ} (hp : IsProbability p) (hq : IsProbability q) :
totalVariation p q ≤ 1 := by
have h : ∑ g, |p g - q g| ≤ 2 := by
calc
_ ≤ ∑ g, (p g + q g) := Finset.sum_le_sum (fun g _ =>
(abs_sub (p g) (q g)).trans_eq (by rw [abs_of_nonneg (hp.1 g), abs_of_nonneg (hq.1 g)]))
_ = 2 := by rw [Finset.sum_add_distrib, hp.2, hq.2]; norm_num
unfold totalVariation
linarith
lemma convolution_tv_contract {p : G → ℝ} (hp : ∀ g, 0 ≤ p g) (q r : G → ℝ) :
totalVariation (convolution p q) (convolution p r) ≤
(∑ g, p g) * totalVariation q r := by
have h : ∑ g, |convolution p q g - convolution p r g| ≤
(∑ g, p g) * ∑ g, |q g - r g| := by
calc
_ = ∑ g, |∑ x, p x * (q (x⁻¹ * g) - r (x⁻¹ * g))| := by
simp_rw [convolution, ← Finset.sum_sub_distrib, ← mul_sub]
_ ≤ ∑ g, ∑ x, p x * |q (x⁻¹ * g) - r (x⁻¹ * g)| := by
apply Finset.sum_le_sum
intro g _
simpa only [abs_mul, abs_of_nonneg (hp _)] using
(Finset.abs_sum_le_sum_abs (s := Finset.univ)
(f := fun x => p x * (q (x⁻¹ * g) - r (x⁻¹ * g))))
_ = _ := by
rw [Finset.sum_comm]
have hs (x : G) : ∑ g, |q (x⁻¹ * g) - r (x⁻¹ * g)| = ∑ g, |q g - r g| :=
sum_mulLeft x⁻¹ (fun g => |q g - r g|)
simp_rw [← Finset.mul_sum, hs]
rw [Finset.sum_mul]
unfold totalVariation
nlinarith
end FiniteRandomWalk
abbrev GridSlot {b : ℕ} (bits : Fin b → ℕ) := (j : Fin b) → Slot (bits j)
abbrev GridOutside {b : ℕ} (bits : Fin b → ℕ) (j : Fin b) :=
(i : {i : Fin b // i ≠ j}) → Slot (bits i)
abbrev GridChoices {b : ℕ} (bits : Fin b → ℕ) :=
(j : Fin b) → GridOutside bits j → Equiv.Perm (Slot (bits j))
def gridSize {b : ℕ} (bits : Fin b → ℕ) := ∏ j, 2 ^ bits j
lemma card_gridSlot {b : ℕ} (bits : Fin b → ℕ) :
Fintype.card (GridSlot bits) = gridSize bits := by
simp [GridSlot, Slot, gridSize, Fintype.card_pi]
def gridLayer {b : ℕ} (bits : Fin b → ℕ) (g : GridChoices bits) (j : Fin b) :
Equiv.Perm (GridSlot bits) :=
let e := Equiv.piSplitAt j (fun i => Slot (bits i))
e.trans ((Equiv.prodCongrLeft (g j)).trans e.symm)
def gridSweep {b : ℕ} (bits : Fin b → ℕ) (g : GridChoices bits) :
Equiv.Perm (GridSlot bits) := (List.ofFn (gridLayer bits g)).reverse.prod
def lineLaw (d : ℕ) (z : ℝ) (g : Equiv.Perm (Slot d)) : ℝ :=
(1 - z) * uniformLaw (Equiv.Perm (Slot d)) g + z * binaryLaw d g
def gridWeight {b : ℕ} (bits : Fin b → ℕ) (z : ℝ) (g : GridChoices bits) : ℝ :=
∏ j, ∏ y, lineLaw (bits j) z (g j y)
structure PathFamily {b : ℕ} (bits : Fin b → ℕ) (h : ℕ) where
position : Fin (b + 1) → Fin h → GridSlot bits
disjoint : ∀ t, Function.Injective (position t)
changes_only_stage : ∀ (j : Fin b) (k : Fin h) (i : Fin b), i ≠ j →
position j.succ k i = position j.castSucc k i
def pathEvent {b h : ℕ} {bits : Fin b → ℕ} (H : PathFamily bits h)
(g : GridChoices bits) : Prop :=
∀ (j : Fin b) (k : Fin h),
gridLayer bits g j (H.position j.castSucc k) = H.position j.succ k
lemma chronological_trajectory {α : Type*} (b : ℕ) (f : Fin b → Equiv.Perm α)
(x : Fin (b + 1) → α) (h : ∀ j, f j (x j.castSucc) = x j.succ) :
(List.ofFn f).reverse.prod (x 0) = x (Fin.last b) := by
induction b with
| zero => simp
| succ b ih =>
simp only [List.ofFn_succ, List.reverse_cons, List.prod_append, List.prod_singleton,
Equiv.Perm.mul_apply]
have h0 : f 0 (x 0) = x (Fin.succ 0) := by simpa using h 0
rw [h0]
have ht (j : Fin b) : f j.succ (x j.castSucc.succ) = x j.succ.succ := by
simpa only [Fin.succ_castSucc] using h j.succ
exact ih (fun j => f j.succ) (fun i => x i.succ) ht
end BinaryCoordinateSweeps
end
end OAI