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Add lean source and binary files (part 61)
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import Mathlib
import OAI.Computability.DirectedFeedback.RankGraph.FailureEvent
namespace OAI
section
noncomputable section
open scoped BigOperators
noncomputable section
open scoped Classical BigOperators
noncomputable section
open scoped Classical
noncomputable section
open scoped Classical
noncomputable section
open scoped Classical BigOperators
noncomputable section
open scoped BigOperators
noncomputable section
open scoped BigOperators
open DirectedFeedback.SourceProbability
noncomputable section
open scoped Classical BigOperators
noncomputable section
open scoped Classical BigOperators
noncomputable section
open scoped Classical BigOperators
noncomputable section
open scoped Classical BigOperators
noncomputable section
open scoped Classical BigOperators
noncomputable section
open scoped Classical BigOperators
noncomputable section
open scoped Classical BigOperators
noncomputable section
open scoped Classical BigOperators
noncomputable section
open scoped Classical BigOperators
noncomputable section
open scoped Classical BigOperators
noncomputable section
open scoped Classical BigOperators
noncomputable section
open scoped Classical
noncomputable section
open scoped Classical BigOperators
noncomputable section
open scoped Classical BigOperators
noncomputable section
open scoped Classical BigOperators
noncomputable section
open scoped Classical BigOperators
noncomputable section
open scoped Classical BigOperators
noncomputable section
namespace DirectedFeedback.Junta
open scoped BigOperators
open Finset DirectedFeedback.JuntaSupport DFVSMaxCut.BooleanFourthMoment
open DFVSMaxCut.Analytic
def roundBoolean {n : ℕ} (p : Cube (Fin n) → ℝ) (x : Cube (Fin n)) : ℝ :=
if (1 : ℝ) / 2 ≤ p x then 1 else 0
theorem roundBoolean_isBoolean {n : ℕ} (p : Cube (Fin n) → ℝ) :
IsBoolean (roundBoolean p) := by
intro x
unfold roundBoolean
split_ifs <;> simp
theorem roundBoolean_onCoordinates {n : ℕ} (J : Finset (Fin n))
(p : Cube (Fin n) → ℝ) (hp : OnCoordinates J p) : OnCoordinates J (roundBoolean p) := by
intro x y h
unfold roundBoolean
rw [hp x y h]
theorem rounding_error_le {n : ℕ} (f p : Cube (Fin n) → ℝ) (hf : IsBoolean f) :
error f (roundBoolean p) ≤ 4 * (𝔼 x, (f x - p x) ^ 2) := by
unfold error
rw [Finset.mul_expect]
apply Finset.expect_le_expect
intro x _
by_cases heq : f x = roundBoolean p x
· rw [ite_eq_left heq]
positivity
· rw [ite_eq_right heq]
rcases hf x with hf0 | hf1
· have hp : (1 : ℝ) / 2 ≤ p x := by
by_contra h
exact heq (by rw [hf0]; unfold roundBoolean; rw [ite_eq_right h])
rw [hf0]
nlinarith [sq_nonneg (p x - 1 / 2)]
· have hp : p x < (1 : ℝ) / 2 := by
by_contra h
exact heq (by rw [hf1]; unfold roundBoolean; rw [ite_eq_left (le_of_not_gt h)])
rw [hf1]
nlinarith [sq_nonneg (p x - 1 / 2)]
theorem friedgut_uniform (b α : ℝ) (hb : 0 ≤ b) (hα : 0 < α) :
∃ Jmax : ℕ, ∀ (n : ℕ) (f : Cube (Fin n) → ℝ),
IsBoolean f → totalInfluence f ≤ b →
∃ J : Finset (Fin n), J.card ≤ Jmax ∧
∃ g : Cube (Fin n) → ℝ, IsBoolean g ∧ OnCoordinates J g ∧ error f g ≤ α := by
obtain ⟨K, hK⟩ := exists_nat_gt (2 * b / α)
have hKpos : (0 : ℝ) < K + 1 := by positivity
have hKb : 2 * b ≤ (K + 1 : ℝ) * α := by
have := (div_lt_iff₀ hα).mp hK
nlinarith
let τ : ℝ := α / (2 * 3 ^ K * (b + 1))
have hb1 : 0 < b + 1 := by linarith
have hτ : 0 < τ := by dsimp [τ]; positivity
have hτid : (3 : ℝ) ^ K * τ * (b + 1) = α / 2 := by
dsimp [τ]
field_simp
have hτb : (3 : ℝ) ^ K * τ * b ≤ α / 2 := by
rw [← hτid]
exact mul_le_mul_of_nonneg_left (by linarith : b ≤ b + 1) (by positivity)
refine ⟨Nat.ceil (b / τ ^ 2), ?_⟩
intro n f hf hIf
let J := highInfluences f τ
have hcard : (J.card : ℝ) ≤ b / τ ^ 2 := by
apply (le_div_iff₀ (sq_pos_of_pos hτ)).mpr
exact (highInfluences_card_bound f τ).trans hIf
refine ⟨J, ?_, roundBoolean (project J f), roundBoolean_isBoolean _,
roundBoolean_onCoordinates J _ (project_onCoordinates J f), ?_⟩
· exact_mod_cast hcard.trans (Nat.le_ceil _)
· have htail := (spectral_tail f K).trans hIf
have htail' : 8 *
(∑ s ∈ Finset.univ.filter (fun s => K < degree s), coefficient f s ^ 2) ≤ α := by
apply (mul_le_mul_iff_of_pos_left hKpos).mp
nlinarith
have hlow := lowDiff_sum_outside f hf K τ hτ.le
have hlow' : 8 * (∑ i ∈ Finset.univ \ J, (𝔼 x, lowDiff f K i x ^ 2)) ≤ α := by
have hh := mul_le_mul_of_nonneg_left hIf (by positivity : 0 ≤ (3 : ℝ) ^ K * τ)
dsimp [J]
nlinarith
have hproject := project_squared_error_le J f K
have hround := rounding_error_le f (project J f) hf
linarith
def cubeEquiv {I J : Type*} (e : I ≃ J) : (I → Bool) ≃ (J → Bool) where
toFun x := fun j => x (e.symm j)
invFun y := fun i => y (e i)
left_inv x := by funext i; simp
right_inv y := by funext j; simp
theorem cubeEquiv_update {I J : Type*} [DecidableEq I] [DecidableEq J]
(e : I ≃ J) (x : I → Bool) (i : I) (a : Bool) :
cubeEquiv e (Function.update x i a) = Function.update (cubeEquiv e x) (e i) a := by
funext j
by_cases hj : j = e i
· subst j; simp [cubeEquiv]
· have hji : e.symm j ≠ i := by
intro heq
apply hj
simpa using congrArg e heq
simp [cubeEquiv, hj, hji]
def DependsOn {I : Type*} (J : Finset I) (f : (I → Bool) → ℝ) : Prop :=
∀ x y, (∀ i ∈ J, x i = y i) → f x = f y
def cubeInfluence {I : Type*} [Fintype I] [DecidableEq I]
(f : (I → Bool) → ℝ) (i : I) : ℝ :=
𝔼 x, (f x - f (Function.update x i (!x i))) ^ 2
def cubeError {I : Type*} [Fintype I] [DecidableEq I] (f g : (I → Bool) → ℝ) : ℝ :=
𝔼 x, if f x = g x then 0 else 1
theorem cubeInfluence_reindex {I J : Type*} [Fintype I] [Fintype J]
[DecidableEq I] [DecidableEq J] (e : I ≃ J) (f : (J → Bool) → ℝ) (i : I) :
cubeInfluence (fun x => f (cubeEquiv e x)) i = cubeInfluence f (e i) := by
apply Fintype.expect_equiv (cubeEquiv e)
intro x
simp only [cubeEquiv_update]
simp [cubeEquiv]
theorem friedgut_finite (b α : ℝ) (hb : 0 ≤ b) (hα : 0 < α) :
∃ Jmax : ℕ, ∀ (I : Type) [Fintype I] [DecidableEq I]
(f : (I → Bool) → ℝ),
(∀ x, f x = 0 ∨ f x = 1) → (∑ i, cubeInfluence f i) ≤ b →
∃ J : Finset I, J.card ≤ Jmax ∧
∃ g : (I → Bool) → ℝ, (∀ x, g x = 0 ∨ g x = 1) ∧
DependsOn J g ∧ cubeError f g ≤ α := by
obtain ⟨Jmax, hJmax⟩ := friedgut_uniform b α hb hα
refine ⟨Jmax, ?_⟩
intro I _ _ f hf hIf
let e : Fin (Fintype.card I) ≃ I := (Fintype.equivFin I).symm
let f' : Cube (Fin (Fintype.card I)) → ℝ := fun x => f (cubeEquiv e x)
have htrans : totalInfluence f' = ∑ i, cubeInfluence f i := by
unfold totalInfluence
change (∑ i, cubeInfluence (fun x => f (cubeEquiv e x)) i) = _
simp_rw [cubeInfluence_reindex]
exact e.sum_comp (cubeInfluence f)
obtain ⟨J, hcard, g, hg, hdep, herr⟩ :=
hJmax (Fintype.card I) f' (fun x => hf _) (htrans ▸ hIf)
refine ⟨J.map e.toEmbedding, by simpa using hcard,
(fun x => g (cubeEquiv e.symm x)), (fun x => hg _), ?_, ?_⟩
· intro x y h
apply hdep
intro i hi
exact h (e i) (Finset.mem_map.mpr ⟨i, hi, rfl⟩)
· have heq : cubeError f (fun x => g (cubeEquiv e.symm x)) = error f' g := by
unfold cubeError error
apply Fintype.expect_equiv (cubeEquiv e.symm)
intro x
have hx : cubeEquiv e (cubeEquiv e.symm x) = x := by funext i; simp [cubeEquiv]
change (if f x = g (cubeEquiv e.symm x) then (0 : ℝ) else 1) =
(if f (cubeEquiv e (cubeEquiv e.symm x)) = g (cubeEquiv e.symm x) then 0 else 1)
rw [hx]
exact heq ▸ herr
end DirectedFeedback.Junta
namespace DirectedFeedback.Junta
open DirectedFeedback.SourceProbability FiniteDistribution
open DirectedFeedback.Probability
variable {I : Type} [Fintype I] [DecidableEq I]
def merge (J : Finset I) (x y : I → Bool) : I → Bool :=
fun i => if i ∈ J then x i else y i
def swapOutside (J : Finset I) : ((I → Bool) × (I → Bool)) ≃ ((I → Bool) × (I → Bool)) where
toFun p := (merge J p.1 p.2, merge J p.2 p.1)
invFun p := (merge J p.1 p.2, merge J p.2 p.1)
left_inv p := by ext i <;> by_cases hi : i ∈ J <;> simp [merge, hi]
right_inv p := by ext i <;> by_cases hi : i ∈ J <;> simp [merge, hi]
theorem expect_merge (J : Finset I) (h : (I → Bool) → ℝ) :
(𝔼 x, 𝔼 y, h (merge J x y)) = 𝔼 x, h x := by
calc
_ = 𝔼 p : (I → Bool) × (I → Bool), h (merge J p.1 p.2) := by
rw [← Finset.expect_product']; simp
_ = 𝔼 p : (I → Bool) × (I → Bool), h p.1 := by
exact (Fintype.expect_equiv (swapOutside J) (fun p => h p.1)
(fun p => h (merge J p.1 p.2)) (by
intro p
congr 1
funext i
by_cases hi : i ∈ J <;> simp [swapOutside, merge, hi])).symm
_ = _ := by rw [← Finset.univ_product_univ, Finset.expect_product]; simp
def disagree (a b : ℝ) : ℝ := if a = b then 0 else 1
theorem disagree_triangle (a b c : ℝ) : disagree a c ≤ disagree a b + disagree b c := by
by_cases hab : a = b
· subst b; simp [disagree]
by_cases hbc : b = c
· subst c; simp [disagree]
simp only [disagree, ite_eq_right hab, ite_eq_right hbc]
split_ifs <;> norm_num
theorem restriction_approximation (J : Finset I) (f h : (I → Bool) → ℝ)
(hh : DependsOn J h) :
∃ y, cubeError f (fun x => f (merge J x y)) ≤ 2 * cubeError f h := by
have hdep (x y : I → Bool) : h x = h (merge J x y) := by
apply hh
intro i hi
simp [merge, hi]
have hbound : (𝔼 y, cubeError f (fun x => f (merge J x y))) ≤ 2 * cubeError f h := by
unfold cubeError
change (𝔼 y, 𝔼 x, disagree (f x) (f (merge J x y))) ≤ _
calc
_ ≤ 𝔼 y, 𝔼 x, (disagree (f x) (h x) + disagree (f (merge J x y)) (h (merge J x y))) := by
apply Finset.expect_le_expect
intro y _
apply Finset.expect_le_expect
intro x _
have ht := disagree_triangle (f x) (h x) (f (merge J x y))
simpa [hdep x y, disagree, eq_comm] using ht
_ = (𝔼 x, disagree (f x) (h x)) + (𝔼 y, 𝔼 x,
disagree (f (merge J x y)) (h (merge J x y))) := by
simp_rw [Finset.expect_add_distrib]
simp
_ = _ := by
rw [Finset.expect_comm]
rw [expect_merge J (fun z => disagree (f z) (h z))]
change cubeError f h + cubeError f h = 2 * cubeError f h
ring
obtain ⟨y, _, hy⟩ := Finset.exists_le_of_expect_le Finset.univ_nonempty hbound
exact ⟨y, hy⟩
end DirectedFeedback.Junta
namespace DirectedFeedback.Junta
open DirectedFeedback.SourceProbability FiniteDistribution
open DirectedFeedback.Probability
variable {I : Type} [Fintype I] [DecidableEq I]
def flatAnd {k : ℕ} (x : I × Fin k → Bool) : I → Bool :=
fun i => andBit (fun a => x (i, a))
theorem flatAnd_expectation (k : ℕ) (f : (I → Bool) → ℝ) :
(𝔼 x : I × Fin k → Bool, f (flatAnd x)) = (dyadicLaw I k).expectation f := by
rw [← blockAnd_expectation]
apply Fintype.expect_equiv (Equiv.curry I (Fin k) Bool)
intro x
rfl
def biasedInfluence (k : ℕ) (f : (I → Bool) → ℝ) (i : I) : ℝ :=
(dyadicLaw I k).expectation (fun x => (f x - f (Function.update x i (!x i))) ^ 2)
omit [Fintype I] in
theorem flatAnd_update_other {k : ℕ} (x : I × Fin k → Bool) (q : I × Fin k)
(i : I) (hi : i ≠ q.1) :
flatAnd (Function.update x q (!x q)) i = flatAnd x i := by
unfold flatAnd
congr 1
funext a
have hia : (i, a) ≠ q := fun h => hi (congrArg Prod.fst h)
simp [hia]
omit [Fintype I] in
theorem flatAnd_update_cases {k : ℕ} (x : I × Fin k → Bool) (q : I × Fin k) :
flatAnd (Function.update x q (!x q)) = flatAnd x ∨
flatAnd (Function.update x q (!x q)) =
Function.update (flatAnd x) q.1 (!(flatAnd x q.1)) := by
by_cases h : flatAnd (Function.update x q (!x q)) q.1 = flatAnd x q.1
· left
funext i
by_cases hi : i = q.1
· simpa [hi] using h
· exact flatAnd_update_other x q i hi
· right
funext i
by_cases hi : i = q.1
· subst i
simp only [Function.update_self]
cases ha : flatAnd (Function.update x q (!x q)) q.1 <;>
cases hb : flatAnd x q.1 <;> simp_all
· rw [Function.update_of_ne hi]
exact flatAnd_update_other x q i hi
theorem flatAnd_influence_le (k : ℕ) (f : (I → Bool) → ℝ) (q : I × Fin k) :
cubeInfluence (fun x => f (flatAnd x)) q ≤ biasedInfluence k f q.1 := by
unfold cubeInfluence biasedInfluence
rw [← flatAnd_expectation k]
apply Finset.expect_le_expect
intro x _
dsimp only
obtain h | h := flatAnd_update_cases x q
· simp only [h, sub_self, zero_pow (by decide : 2 ≠ 0)]
exact sq_nonneg _
· rw [h]
theorem flatAnd_totalInfluence_le (k : ℕ) (f : (I → Bool) → ℝ) :
(∑ q : I × Fin k, cubeInfluence (fun x => f (flatAnd x)) q) ≤
k * ∑ i, biasedInfluence k f i := by
calc
_ ≤ ∑ q : I × Fin k, biasedInfluence k f q.1 :=
Finset.sum_le_sum (fun q _ => flatAnd_influence_le k f q)
_ = _ := by
rw [Fintype.sum_prod_type]
simp [← Finset.mul_sum]
theorem friedgut_dyadic (k : ℕ) (b α : ℝ) (hb : 0 ≤ b) (hα : 0 < α) :
∃ Jmax : ℕ, ∀ (I : Type) [Fintype I] [DecidableEq I] (f : (I → Bool) → ℝ),
(∀ x, f x = 0 ∨ f x = 1) → (∑ i, biasedInfluence k f i) ≤ b →
∃ J : Finset I, J.card ≤ Jmax ∧ ∃ g : (I → Bool) → ℝ,
(∀ x, g x = 0 ∨ g x = 1) ∧ DependsOn J g ∧
(dyadicLaw I k).expectation (fun x => disagree (f x) (g x)) ≤ α := by
obtain ⟨Jmax, hJmax⟩ := friedgut_finite (k * b) (α / 2) (mul_nonneg (Nat.cast_nonneg _) hb)
(by positivity)
refine ⟨Jmax, ?_⟩
intro I _ _ f hf hIf
let F : (I × Fin k → Bool) → ℝ := fun x => f (flatAnd x)
have hIF : (∑ q, cubeInfluence F q) ≤ k * b :=
(flatAnd_totalInfluence_le k f).trans (mul_le_mul_of_nonneg_left hIf (Nat.cast_nonneg _))
obtain ⟨L, hL, H, _, hH, herror⟩ := hJmax (I × Fin k) F (fun x => hf _) hIF
let J : Finset I := L.image Prod.fst
let K : Finset (I × Fin k) := Finset.univ.filter (fun q => q.1 ∈ J)
have hHK : DependsOn K H := by
intro x y hxy
apply hH
intro q hq
apply hxy
simp only [K, Finset.mem_filter, Finset.mem_univ, true_and]
exact Finset.mem_image.mpr ⟨q, hq, rfl⟩
obtain ⟨y, hy⟩ := restriction_approximation K F H hHK
let g : (I → Bool) → ℝ := fun z => f (merge J z (flatAnd y))
have hmerge (x : I × Fin k → Bool) :
flatAnd (merge K x y) = merge J (flatAnd x) (flatAnd y) := by
funext i
by_cases hi : i ∈ J <;> simp [flatAnd, merge, K, hi]
refine ⟨J, (Finset.card_image_le).trans hL, g, (fun x => hf _), ?_, ?_⟩
· intro x z hxz
apply congrArg f
funext i
by_cases hi : i ∈ J
· simp [merge, hi, hxz i hi]
· simp [merge, hi]
· have heq : cubeError F (fun x => F (merge K x y)) =
(dyadicLaw I k).expectation (fun z => disagree (f z) (g z)) := by
unfold cubeError
change (𝔼 x, disagree (F x) (F (merge K x y))) = _
simp only [F, hmerge]
exact flatAnd_expectation k (fun z => disagree (f z) (g z))
rw [heq] at hy
linarith
end DirectedFeedback.Junta
noncomputable section
open scoped Classical BigOperators
namespace DirectedFeedback.Pivotal
open DirectedFeedback.SourceProbability FiniteDistribution
open DirectedFeedback.Probability
variable {I : Type} [Fintype I] [DecidableEq I]
theorem bitValue_boolean (b : Bool) : bitValue b = 0 ∨ bitValue b = 1 := by
cases b <;> simp [bitValue]
theorem dyadic_coin (k : ℕ) :
andCoin k = coin ((2 : ℝ)⁻¹ ^ k) (by positivity)
(pow_le_one₀ (by norm_num) (by norm_num)) := by
apply eq_of_weight_eq
intro b
cases b
· exact andCoin_false k
· exact andCoin_true k
theorem dyadic_law (k : ℕ) :
dyadicLaw I k = law ((2 : ℝ)⁻¹ ^ k) (by positivity)
(pow_le_one₀ (by norm_num) (by norm_num)) := by
simp only [dyadicLaw, law, dyadic_coin]
omit [Fintype I] in
theorem update_insertBit (j : I) (b : Bool) (x : {i : I // i ≠ j} → Bool) :
Function.update (insertBit j b x) j (!b) = insertBit j (!b) x := by
funext i
by_cases hi : i = j
· subst i; simp
· simp [insertBit, hi]
theorem prod_insertBit (p : ℝ) (j : I) (b : Bool) (x : {i : I // i ≠ j} → Bool) :
(∏ i, atom p (insertBit j b x i)) = atom p b * ∏ i : {i : I // i ≠ j}, atom p (x i) := by
rw [← Finset.mul_prod_erase _ _ (Finset.mem_univ j)]
rw [insertBit_self, prod_except]
simp only [insertBit_other]
omit [Fintype I] in
theorem flip_square (f : (I → Bool) → Bool) (hf : Monotone f)
(j : I) (b : Bool) (x : {i : I // i ≠ j} → Bool) :
(bitValue (f (insertBit j b x)) -
bitValue (f (Function.update (insertBit j b x) j (!(insertBit j b x j)))))^2 =
discreteDerivative f j x := by
rw [insertBit_self, update_insertBit, discreteDerivative_eq_indicator f hf]
have hm : f (insertBit j false x) ≤ f (insertBit j true x) := by
apply hf
intro i
by_cases hi : i = j
· subst i; simp
· simp [insertBit, hi]
cases b <;> cases h0 : f (insertBit j false x) <;> cases h1 : f (insertBit j true x) <;>
simp_all [bitValue, pivotalEvent, Bool.le_iff_imp]
theorem biasedInfluence_eq (k : ℕ) (f : (I → Bool) → Bool) (hf : Monotone f) (j : I) :
Junta.biasedInfluence k (fun x => bitValue (f x)) j =
∑ x : {i : I // i ≠ j} → Bool,
(∏ i, atom ((2 : ℝ)⁻¹^k) (x i)) * discreteDerivative f j x := by
unfold Junta.biasedInfluence
rw [dyadic_law]
change (∑ x, (∏ i, atom ((2 : ℝ)⁻¹^k) (x i)) *
(bitValue (f x) - bitValue (f (Function.update x j (!(x j)))))^2) = _
rw [sum_split j]
apply Finset.sum_congr rfl
intro x _
rw [prod_insertBit, prod_insertBit, flip_square f hf, flip_square f hf]
simp only [atom, Bool.false_eq_true, ↓reduceIte]
ring
theorem dyadic_influences_le_budget (k : ℕ) (f : (I → Bool) → Bool) (hf : Monotone f) :
(∑ j, Junta.biasedInfluence k (fun x => bitValue (f x)) j) ≤ budget f := by
simp_rw [biasedInfluence_eq k f hf]
exact influence_le_budget f hf _ (by positivity) (pow_le_one₀ (by norm_num) (by norm_num))
theorem boolean_dyadic_junta (k : ℕ) (B γ : ℝ) (hB : 0 ≤ B) (hγ : 0 < γ) :
∃ Jmax : ℕ, ∀ (I : Type) [Fintype I] [DecidableEq I] (f : (I → Bool) → Bool),
Monotone f → budget f ≤ B →
∃ J : Finset I, J.card ≤ Jmax ∧ ∃ g : (I → Bool) → Bool,
(∀ x y, (∀ i ∈ J, x i = y i) → g x = g y) ∧
(law ((2 : ℝ)⁻¹^k) (by positivity) (pow_le_one₀ (by norm_num) (by norm_num))).probability
(fun x => decide (f x ≠ g x)) ≤ γ := by
obtain ⟨Jmax, hJmax⟩ := Junta.friedgut_dyadic k B γ hB hγ
refine ⟨Jmax, ?_⟩
intro I _ _ f hf hbf
obtain ⟨J,hJ,g,hg,hdep,herr⟩ := hJmax I (fun x => bitValue (f x))
(fun x => bitValue_boolean (f x)) ((dyadic_influences_le_budget k f hf).trans hbf)
let g' : (I → Bool) → Bool := fun x => decide (g x = 1)
refine ⟨J,hJ,g',?_,?_⟩
· intro x y hxy
simp only [g', hdep x y hxy]
· rw [probability_eq_expectation, ← dyadic_law]
convert herr using 1
apply expectation_congr
intro x
obtain hx | hx := hg x <;> cases hfx : f x <;>
simp [g', Junta.disagree, bitValue, hx]
end DirectedFeedback.Pivotal
noncomputable section
open scoped Classical BigOperators
namespace DirectedFeedback.Pivotal
open DirectedFeedback.SourceProbability FiniteDistribution
def DyadicJuntaBound (M : ℕ) (B γ : ℝ) (Jmax : ℕ) : Prop :=
∀ k : Fin (M+2), ∀ (I : Type) [Fintype I] [DecidableEq I] (f : (I → Bool) → Bool),
Monotone f → budget f ≤ B →
∃ J : Finset I, J.card ≤ Jmax ∧ ∃ g : (I → Bool) → Bool,
(∀ x y, (∀ i ∈ J, x i = y i) → g x = g y) ∧
(law ((2:ℝ)⁻¹^k.val) (by positivity)
(pow_le_one₀ (by norm_num) (by norm_num))).probability
(fun x => decide (f x ≠ g x)) ≤ γ
theorem uniform_dyadic_junta (M : ℕ) (B γ : ℝ) (hB : 0 ≤ B) (hγ : 0 < γ) :
∃ Jmax : ℕ, 1 ≤ Jmax ∧ DyadicJuntaBound M B γ Jmax := by
have h := fun k : Fin (M+2) => boolean_dyadic_junta k.val B γ hB hγ
choose J hJ using h
refine ⟨1 + ∑ k, J k, by omega, ?_⟩
intro k I _ _ f hf hb
obtain ⟨S,hS,g,hg,herr⟩ := hJ k I f hf hb
refine ⟨S, ?_,g,hg,herr⟩
have hh : J k ≤ ∑ j : Fin (M+2), J j :=
Finset.single_le_sum (fun _ _ => Nat.zero_le _) (Finset.mem_univ k)
omega
theorem family_dyadic_junta {M Jmax : ℕ} {B γ : ℝ}
(hJ : DyadicJuntaBound M B γ Jmax) (k : Fin (M+2))
{Ω U I : Type} [Fintype I] [DecidableEq I]
(f : Ω → U → (I → Bool) → Bool) (hf : ∀ ω u, Monotone (f ω u)) :
∃ (S : Ω → U → Finset I) (g : Ω → U → (I → Bool) → Bool),
(∀ ω u, (S ω u).card ≤ Jmax) ∧
(∀ ω u x y, (∀ i ∈ S ω u, x i = y i) → g ω u x = g ω u y) ∧
(∀ ω u, budget (f ω u) ≤ B →
(law ((2:ℝ)⁻¹^k.val) (by positivity)
(pow_le_one₀ (by norm_num) (by norm_num))).probability
(fun x => decide (f ω u x ≠ g ω u x)) ≤ γ) := by
have hex (ω : Ω) (u : U) : ∃ S : Finset I, S.card ≤ Jmax ∧
∃ g : (I → Bool) → Bool,
(∀ x y, (∀ i ∈ S, x i = y i) → g x = g y) ∧
(budget (f ω u) ≤ B →
(law ((2:ℝ)⁻¹^k.val) (by positivity)
(pow_le_one₀ (by norm_num) (by norm_num))).probability
(fun x => decide (f ω u x ≠ g x)) ≤ γ) := by
by_cases hb : budget (f ω u) ≤ B
· obtain ⟨S,hS,g,hg,herr⟩ := hJ k I (f ω u) (hf ω u) hb
exact ⟨S,hS,g,hg,fun _ => herr⟩
· exact ⟨∅,by simp,fun _ => false,by simp,fun h => (hb h).elim⟩
choose S hS g hg herr using hex
exact ⟨S,g,hS,hg,herr⟩
end DirectedFeedback.Pivotal
noncomputable section
open scoped Classical BigOperators
namespace DirectedFeedback.Coupling
open DirectedFeedback.SourceProbability FiniteDistribution
variable {X Y : Type*} [Fintype X] [Fintype Y]
theorem product_pattern_lipschitz {a b c d ε δ : ℝ}
(hb : 0 ≤ b) (hc : 0 ≤ c) (hc1 : c ≤ 1)
(hb1 : b ≤ 1) (hε : |a-c| ≤ ε) (hδ : |b-d| ≤ δ) :
|a*(1-b)-c*(1-d)| ≤ ε + δ := by
have hε0 : 0 ≤ ε := (abs_nonneg _).trans hε
have hδ0 : 0 ≤ δ := (abs_nonneg _).trans hδ
calc
_ = |(a-c)*(1-b) + c*(d-b)| := by congr 1; ring
_ ≤ |a-c| *|1-b| + |c| *|d-b| := by simpa only [abs_mul] using abs_add_le ((a-c)*(1-b)) (c*(d-b))
_ = |a-c| *(1-b) + c*|b-d| := by rw [abs_of_nonneg (by linarith : 0 ≤ 1-b), abs_of_nonneg hc, abs_sub_comm d b]
_ ≤ ε*(1-b) + c*δ := add_le_add (mul_le_mul_of_nonneg_right hε (by linarith))
(mul_le_mul_of_nonneg_left hδ hc)
_ ≤ ε + δ := by nlinarith [mul_nonneg hε0 hb, mul_nonneg (sub_nonneg.mpr hc1) hδ0]
theorem comparison_juntas (e : X ≃ Y × Bool) (p : ℝ) (hp0 : 0 ≤ p) (hp : p ≤ 1/2)
(J : Finset X) (K : Finset Y) (hJK : Disjoint (J.image (fun x => (e x).1)) K)
(f f' : (X → Bool) → Bool) (g g' : (Y → Bool) → Bool)
(hf' : Uses J f') (hg' : Uses K g') (γ : ℝ)
(hf : (Pivotal.law p hp0 (by linarith)).probability (fun x => decide (f x ≠ f' x)) ≤ γ) (hg : (Pivotal.law (2*p) (by positivity) (by linarith)).probability (fun y => decide (g y ≠ g' y)) ≤ γ) :
|(Pivotal.law p hp0 (by linarith)).probability f *
(1 - (Pivotal.law (2*p) (by positivity) (by linarith)).probability g) -
(law p hp0 hp).probability (fun z => f (bigBits e z) && !(g (smallBits z)))| ≤ 4*γ := by
let μ := Pivotal.law (I := X) p hp0 (by linarith)
let ν := Pivotal.law (I := Y) (2*p) (by positivity) (by linarith)
let ρ := law (Y := Y) p hp0 hp
have hprod : |μ.probability f * (1-ν.probability g) -
μ.probability f' * (1-ν.probability g')| ≤ 2*γ := by
have h := product_pattern_lipschitz (probability_nonnegative ν g)
(probability_nonnegative μ f') (probability_le_one μ f') (probability_le_one ν g)
((probability_abs_sub_le_disagree μ f f').trans hf) ((probability_abs_sub_le_disagree ν g g').trans hg)
linarith
have hc := pair_event_disagree ρ (fun z => f (bigBits e z)) (fun z => f' (bigBits e z)) (fun z => !(g (smallBits z))) (fun z => !(g' (smallBits z)))
have hnot : (fun z : Y → Block => decide ((!g (smallBits z)) ≠ (!g' (smallBits z)))) = fun z => decide (g (smallBits z) ≠ g' (smallBits z)) := by
funext z; simp
rw [hnot] at hc
change |_ - _| ≤ (law p hp0 hp).probability (fun z => (fun x => decide (f x ≠ f' x)) (bigBits e z)) + (law p hp0 hp).probability (fun z => (fun y => decide (g y ≠ g' y)) (smallBits z)) at hc
rw [big_probability e p hp0 hp (fun x => decide (f x ≠ f' x)), small_probability p hp0 hp (fun y => decide (g y ≠ g' y))] at hc
have hcc : |ρ.probability (fun z => f (bigBits e z) && !(g (smallBits z))) -
ρ.probability (fun z => f' (bigBits e z) && !(g' (smallBits z)))| ≤ 2*γ := by
linarith
have heq := disjoint_pattern e p hp0 hp J K hJK f' g' hf' hg'
change ρ.probability (fun z => f' (bigBits e z) && !(g' (smallBits z))) =
μ.probability f' * (1 - ν.probability g') at heq
change |μ.probability f * (1-ν.probability g) -
ρ.probability (fun z => f (bigBits e z) && !(g (smallBits z)))| ≤ _
calc
_ ≤ |μ.probability f * (1-ν.probability g) - μ.probability f' * (1-ν.probability g')| +
|μ.probability f' * (1-ν.probability g') -
ρ.probability (fun z => f (bigBits e z) && !(g (smallBits z)))| := abs_sub_le _ _ _
_ ≤ 4*γ := by
have hc' : |μ.probability f' * (1-ν.probability g') - ρ.probability (fun z => f (bigBits e z) && !(g (smallBits z)))| ≤ 2*γ := by rw [← heq, abs_sub_comm]
exact hcc
linarith
end DirectedFeedback.Coupling
noncomputable section
open scoped Classical BigOperators
namespace DirectedFeedback.Soundness
open DirectedFeedback.SourceProbability FiniteDistribution
open Games
variable {Ω U V E X Y : Type} [Fintype Ω] [Fintype U] [Fintype V] [Fintype E]
[Fintype X] [Fintype Y] [Nonempty X] [Nonempty Y]
theorem averaged_comparison (μ : FiniteDistribution Ω) (G : Game U V E X Y)
(θ : ℝ) (hsound : G.Sound θ) (fiber : E → X ≃ Y × Bool)
(hproj : ∀ e x, (fiber e x).1 = G.project e x)
(p γ C : ℝ) (hp0 : 0 ≤ p) (hp : p ≤ 1/2) (hγ : 0 < γ) (hC : 0 < C)
(f : Ω → U → (X → Bool) → Bool) (g : Ω → V → (Y → Bool) → Bool)
(Bu : Ω → U → ℝ) (Bv : Ω → V → ℝ)
(hBu0 : ∀ ω u, 0 ≤ Bu ω u) (hBv0 : ∀ ω v, 0 ≤ Bv ω v)
(hBu : (μ.product G.edgeLaw).expectation (fun a => Bu a.1 (G.left a.2)) ≤ C)
(hBv : (μ.product G.edgeLaw).expectation (fun a => Bv a.1 (G.right a.2)) ≤ C)
(J : ℕ) (hJ : 1 ≤ J) (Ju : Ω → U → Finset X) (Jv : Ω → V → Finset Y)
(f' : Ω → U → (X → Bool) → Bool) (g' : Ω → V → (Y → Bool) → Bool)
(hJu : ∀ ω u, (Ju ω u).card ≤ J) (hJv : ∀ ω v, (Jv ω v).card ≤ J)
(hf' : ∀ ω u, Uses (Ju ω u) (f' ω u)) (hg' : ∀ ω v, Uses (Jv ω v) (g' ω v))
(hf : ∀ ω u, Bu ω u ≤ C/γ →
(Pivotal.law p hp0 (by linarith)).probability (fun x => decide (f ω u x ≠ f' ω u x)) ≤ γ)
(hg : ∀ ω v, Bv ω v ≤ C/γ →
(Pivotal.law (2*p) (by positivity) (by linarith)).probability
(fun y => decide (g ω v y ≠ g' ω v y)) ≤ γ) :
(μ.product G.edgeLaw).expectation (fun a =>
(Pivotal.law p hp0 (by linarith)).probability (f a.1 (G.left a.2)) *
(1 - (Pivotal.law (2*p) (by positivity) (by linarith)).probability (g a.1 (G.right a.2)))) ≤
(μ.product G.edgeLaw).expectation (fun a =>
(Coupling.law p hp0 hp).probability (fun z =>
f a.1 (G.left a.2) (Coupling.bigBits (fiber a.2) z) &&
!(g a.1 (G.right a.2) (Coupling.smallBits z)))) + 2*γ + θ*(J:ℝ)^2 + 4*γ := by
let μc := μ.product G.edgeLaw
let bU : Ω × E → Bool := fun a => decide (C/γ < Bu a.1 (G.left a.2))
let bV : Ω × E → Bool := fun a => decide (C/γ < Bv a.1 (G.right a.2))
let meet : Ω × E → Bool := fun a =>
decide (∃ x ∈ Ju a.1 (G.left a.2), G.project a.2 x ∈ Jv a.1 (G.right a.2))
have hU : μc.probability bU ≤ γ := by
have hh := markov_bound μc (fun a => Bu a.1 (G.left a.2)) (fun a => hBu0 _ _)
(C/γ) (div_pos hC hγ)
have he : C / (C/γ) = γ := by field_simp
exact hh.trans ((div_le_div_of_nonneg_right hBu (by positivity)).trans_eq he)
have hV : μc.probability bV ≤ γ := by
have hh := markov_bound μc (fun a => Bv a.1 (G.right a.2)) (fun a => hBv0 _ _)
(C/γ) (div_pos hC hγ)
have he : C / (C/γ) = γ := by field_simp
exact hh.trans ((div_le_div_of_nonneg_right hBv (by positivity)).trans_eq he)
have hmeet : μc.probability meet ≤ θ*(J:ℝ)^2 := by
rw [probability_eq_expect, expectation_product]
simp_rw [← probability_eq_expect]
change μ.expectation (fun ω => G.edgeLaw.probability (fun e =>
decide (∃ x ∈ Ju ω (G.left e), G.project e x ∈ Jv ω (G.right e)))) ≤ _
calc
_ ≤ μ.expectation (fun _ => θ*(J:ℝ)^2) := expectation_mono _
(fun ω => list_decoding G θ hsound (Ju ω) (Jv ω) J hJ (hJu ω) (hJv ω))
_ = _ := expectation_const _ _
let A : Ω × E → ℝ := fun a =>
(Pivotal.law p hp0 (by linarith)).probability (f a.1 (G.left a.2)) *
(1 - (Pivotal.law (2*p) (by positivity) (by linarith)).probability (g a.1 (G.right a.2)))
let B : Ω × E → ℝ := fun a =>
(Coupling.law p hp0 hp).probability (fun z => f a.1 (G.left a.2) (Coupling.bigBits (fiber a.2) z) &&
!(g a.1 (G.right a.2) (Coupling.smallBits z)))
have hA1 (a : Ω × E) : A a ≤ 1 := by
have h0 := probability_nonnegative (Pivotal.law (I := Y) (2*p) (by positivity) (by linarith))
(g a.1 (G.right a.2))
have h1 := probability_le_one (Pivotal.law (I := Y) (2*p) (by positivity) (by linarith))
(g a.1 (G.right a.2))
have hh := probability_le_one (Pivotal.law (I := X) p hp0 (by linarith)) (f a.1 (G.left a.2))
exact (mul_le_mul_of_nonneg_right hh (by linarith)).trans (by simpa using h0)
have hB0 (a : Ω × E) : 0 ≤ B a := probability_nonnegative _ _
have hpoint (a : Ω × E) : A a ≤ B a + 4*γ +
(if bU a then 1 else 0) + (if bV a then 1 else 0) + (if meet a then 1 else 0) := by
by_cases hu : Bu a.1 (G.left a.2) ≤ C/γ
· by_cases hv : Bv a.1 (G.right a.2) ≤ C/γ
· by_cases hm : ∃ x ∈ Ju a.1 (G.left a.2), G.project a.2 x ∈ Jv a.1 (G.right a.2)
· simp only [bU, bV, meet, not_lt.mpr hu, not_lt.mpr hv, hm, decide_false, decide_true,
Bool.false_eq_true, ↓reduceIte, add_zero]
linarith [hA1 a, hB0 a]
· have hd : Disjoint ((Ju a.1 (G.left a.2)).image (fun x => (fiber a.2 x).1))
(Jv a.1 (G.right a.2)) := by
rw [Finset.disjoint_left]
intro y hy hy'
obtain ⟨x,hx,rfl⟩ := Finset.mem_image.mp hy
exact hm ⟨x,hx, by simpa [hproj] using hy'⟩
have hh := Coupling.comparison_juntas (fiber a.2) p hp0 hp _ _ hd _ _ _ _
(hf' _ _) (hg' _ _) γ (hf _ _ hu) (hg _ _ hv)
have hh' : A a - B a ≤ 4*γ := (le_abs_self _).trans hh
simpa only [bU, bV, meet, not_lt.mpr hu, not_lt.mpr hv, hm, decide_false,
Bool.false_eq_true, ↓reduceIte, add_zero] using (show A a ≤ B a + 4*γ by linarith)
· have hv' := lt_of_not_ge hv
simp only [bU, bV, not_lt.mpr hu, hv', decide_false, decide_true,
Bool.false_eq_true, ↓reduceIte, add_zero]
split_ifs <;> linarith [hA1 a, hB0 a]
· have hu' := lt_of_not_ge hu
simp only [bU, hu', decide_true, ↓reduceIte]
split_ifs <;> linarith [hA1 a, hB0 a]
have hh := expectation_mono μc hpoint
simp only [expectation_add, expectation_const, ← probability_eq_expect] at hh
change μc.expectation A ≤ μc.expectation B + 2*γ + θ*(J:ℝ)^2 + 4*γ
linarith
end DirectedFeedback.Soundness
noncomputable section
open scoped Classical BigOperators
namespace DirectedFeedback.Construction
open DirectedFeedback.SourceProbability FiniteDistribution
open RankGraph Prefix PrefixExperiment Games Pivotal
variable {U V E X Y : Type} [Fintype U] [Fintype V] [Fintype E]
[Fintype X] [Fintype Y] [Nonempty X] [Nonempty Y]
variable {M T N : ℕ} [NeZero M] [NeZero T]
omit [NeZero M] in
theorem double_bias (i : Fin M) : 2*bias i = (2:ℝ)⁻¹^(i.val+1) := by
unfold bias
rw [show i.val+2 = (i.val+1)+1 by omega, pow_succ]
ring
variable (G : Game U V E X Y) (ψLaw : FiniteDistribution (Emb (rankLength X T) N))
variable (hYX : Fintype.card Y ≤ Fintype.card X) (fiber : E → X ≃ Y × Bool)
variable (deleted : OutputVertex (M := M) G ψLaw → Bool)
variable (ord : OutputVertex (M := M) G ψLaw → ℕ)
variable (hord : ∀ {a b}, deleted a = false → deleted b = false →
OutputArc G ψLaw hYX fiber a b → ord a < ord b)
variable (hrank : ∀ r, deleted (.inl r) = false)
def bigAcceptance (fallback : X) (i : Fin M) (a : Common U X M T N × E) : ℝ :=
(subsetLaw i).probability (bigFunction G ψLaw hYX fiber deleted ord hord hrank fallback a.1 (G.left a.2))
def smallAcceptance (fallback : Y) (i : Fin M) (a : Common U X M T N × E) : ℝ :=
(Pivotal.law (2*bias i) (mul_nonneg (by norm_num) (bias_pos i).le) (by linarith [bias_le_quarter i])).probability
(smallFunction G ψLaw hYX fiber deleted ord hord hrank fallback a.1 (G.right a.2))
def bigBudget (fallback : X) (a : Common U X M T N × E) : ℝ :=
budget (bigFunction G ψLaw hYX fiber deleted ord hord hrank fallback a.1 (G.left a.2))
def smallBudget (fallback : Y) (a : Common U X M T N × E) : ℝ :=
budget (smallFunction G ψLaw hYX fiber deleted ord hord hrank fallback a.1 (G.right a.2))
theorem independent_comparison (fallbackX : X) (fallbackY : Y) (i : Fin M)
(D K ρ γ θ : ℝ) (hD : 0 ≤ D) (hK : 0 < K) (hγ : 0 < γ)
(hsound : G.Sound θ) (hproj : ∀ e x, (fiber e x).1 = G.project e x)
(J : ℕ) (hJ : 1 ≤ J) (hJunta : DyadicJuntaBound M ((D+1)/γ) γ J)
(hBu : ((common (M := M) G ψLaw).product G.edgeLaw).expectation
(bigBudget G ψLaw hYX fiber deleted ord hord hrank fallbackX) ≤ D+1)
(hBv : ((common (M := M) G ψLaw).product G.edgeLaw).expectation
(smallBudget G ψLaw hYX fiber deleted ord hord hrank fallbackY) ≤ D+1)
(hcost : K * (comparisonLaw G ψLaw).probability (comparisonDeleted G ψLaw deleted) ≤ D)
(hbU : ((common (M := M) G ψLaw).product G.edgeLaw).probability (fun a =>
decide (¬bigGood G ψLaw ord a.1 (G.left a.2))) ≤ ρ)
(hbV : ((common (M := M) G ψLaw).product G.edgeLaw).probability (fun a =>
decide (¬smallGood G ψLaw ord a.1 (G.right a.2))) ≤ ρ) :
((common (M := M) G ψLaw).product G.edgeLaw).expectation (fun a =>
bigAcceptance G ψLaw hYX fiber deleted ord hord hrank fallbackX i a *
(1-smallAcceptance G ψLaw hYX fiber deleted ord hord hrank fallbackY i a)) ≤
2*ρ + M*(2:ℝ)^T*D/K + 6*γ + θ*(J:ℝ)^2 := by
let f := bigFunction G ψLaw hYX fiber deleted ord hord hrank fallbackX
let g := smallFunction G ψLaw hYX fiber deleted ord hord hrank fallbackY
have hfm : ∀ d u, Monotone (f d u) := fun d u =>
(bigFunction_properties G ψLaw hYX fiber deleted ord hord hrank fallbackX d u).1
have hgm : ∀ d v, Monotone (g d v) := fun d v =>
(smallFunction_properties G ψLaw hYX fiber deleted ord hord hrank fallbackY d v).1
obtain ⟨Ju,fu,hJu,hfu,hEu⟩ := family_dyadic_junta hJunta
⟨i.val+2,by have := i.isLt; omega⟩ f hfm
obtain ⟨Jv,gv,hJv,hgv,hEv⟩ := family_dyadic_junta hJunta
⟨i.val+1,by have := i.isLt; omega⟩ g hgm
have hh := Soundness.averaged_comparison (common G ψLaw) G θ hsound fiber hproj
(bias i) γ (D+1) (bias_pos i).le (by linarith [bias_le_quarter i]) hγ (by linarith)
f g (fun d u => budget (f d u)) (fun d v => budget (g d v))
(fun d u => budget_nonneg _) (fun d v => budget_nonneg _) hBu hBv
J hJ Ju Jv fu gv hJu hJv hfu hgv hEu (by
intro d v hv
simpa only [double_bias] using hEv d v hv)
have hc := comparison_charge G ψLaw hYX fiber deleted ord hord hrank
fallbackX fallbackY i D K ρ hK hcost hbU hbV
change _ ≤ _ at hh
change _ ≤ _ at hc
have he : ((common (M := M) G ψLaw).product G.edgeLaw).expectation (fun a =>
bigAcceptance G ψLaw hYX fiber deleted ord hord hrank fallbackX i a *
(1-smallAcceptance G ψLaw hYX fiber deleted ord hord hrank fallbackY i a)) ≤
((common (M := M) G ψLaw).product G.edgeLaw).expectation (fun a =>
(Coupling.law (Y := Y) (bias i) (bias_pos i).le (by linarith [bias_le_quarter i])).probability
(failureEvent G ψLaw hYX fiber deleted ord hord hrank fallbackX fallbackY a)) +
2*γ + θ*(J:ℝ)^2 + 4*γ := hh
linarith
end DirectedFeedback.Construction
namespace DirectedFeedback
theorem transGen_loop_walk {V : Type*} {R : V → V → Prop} {a : V}
(h : Relation.TransGen R a a) :
∃ (n : ℕ) (f : Fin (n + 1) → V), CyclicWalk R f := by
obtain ⟨b, hab, hba⟩ := Relation.TransGen.head'_iff.mp h
obtain ⟨l, hl, hend⟩ := List.exists_isChain_cons_of_relationReflTransGen hba
have hc : List.IsChain R (a :: b :: l) := .cons_cons hab hl
let f : Fin (l.length + 1) → V := fun i => (a :: b :: l)[i.val]'(by simp)
refine ⟨l.length, f, ?_⟩
intro i
have hedge := List.isChain_iff_getElem.mp hc i.val (by simp; have := i.isLt; omega)
by_cases hi : i.val + 1 < l.length + 1
· simpa only [f, Fin.val_add_one_of_lt' hi] using hedge
· have hilast : i.val = l.length := by have := i.isLt; omega
have hwrap : i + 1 = 0 := by apply Fin.ext; simp [Fin.val_add, hilast]
have he : (a :: b :: l)[i.val + 1]'(by simp; have := i.isLt; omega) = a := by
have hlast : (a :: b :: l).getLast (by simp) = a := by
simpa using hend
rw [List.getLast_eq_getElem] at hlast
simpa [hilast] using hlast
simpa only [f, hwrap, Fin.val_zero, List.getElem_cons_zero, he] using hedge
theorem exists_topological_order {V : Type*} [Fintype V] [DecidableEq V]
(R : V → V → Prop) (F : Finset V) (hF : FeedbackR R F) :
∃ order : V → ℕ, (∀ a b, a ∉ F → b ∉ F → R a b → order a < order b) ∧
(∀ a b, a ∉ F → b ∉ F → order a = order b → a = b) := by
classical
let S := {v : V // v ∉ F}
let r : S → S → Prop := fun a b => R a.val b.val
have hr : ∀ a : S, ¬Relation.TransGen r a a := by
intro a ha
obtain ⟨n, f, hf⟩ := transGen_loop_walk ha
have hw : CyclicWalk R (fun i => (f i).val) := hf
obtain ⟨i, hi⟩ := feedback_hits_walk R F hF n (fun i => (f i).val) hw
exact (f i).property hi
let po : PartialOrder S :=
{ le := Relation.ReflTransGen r
le_refl a := .refl
le_trans _ _ _ := Relation.ReflTransGen.trans
le_antisymm a b hab hba := by
rcases Relation.reflTransGen_iff_eq_or_transGen.mp hab with heq | hne
· exact heq.symm
· exact False.elim (hr a (hne.trans_left hba)) }
let := po
let : Fintype (LinearExtension S) := Fintype.ofEquiv S (Equiv.refl _)
let e := (Fintype.orderIsoFinOfCardEq (LinearExtension S) rfl).symm
let rank (a : S) : ℕ := (e (toLinearExtension a)).val
have hlt {a b : S} (h : r a b) : rank a < rank b := by
apply e.strictMono
apply (toLinearExtension.monotone (show a ≤ b from Relation.ReflTransGen.single h)).lt_of_ne
intro heq
have heq' : a = b := heq
subst b
exact hr a (.single h)
have hinj {a b : S} (h : rank a = rank b) : a = b := by
have hh := e.injective (Fin.ext h)
exact hh
refine ⟨fun a => if ha : a ∉ F then rank ⟨a, ha⟩ else 0, ?_, ?_⟩
· intro a b ha hb hab
simpa [ha, hb] using hlt (a := ⟨a, ha⟩) (b := ⟨b, hb⟩) hab
· intro a b ha hb heq
have hh : rank ⟨a, ha⟩ = rank ⟨b, hb⟩ := by simpa [ha, hb] using heq
exact congrArg Subtype.val (hinj hh)
end DirectedFeedback
noncomputable section
open scoped Classical BigOperators
namespace DirectedFeedback.Prefix
theorem prefixCell_congr {X : Type} (t : ℕ) (P Q : ℕ → Set X)
(h : ∀ j < t, P j = Q j) : prefixCell t P = prefixCell t Q := by
induction t with
| zero => rfl
| succ t ih =>
funext a
dsimp only [prefixCell]
rw [ih (fun j hj => h j (by omega)), h t (by omega)]
end DirectedFeedback.Prefix
namespace DirectedFeedback.RankGraph
open DirectedFeedback.SourceProbability.FiniteDistribution Prefix
variable {U V X Y : Type} {T N L : ℕ}
theorem good_take_prefix (ord : Rank U V X Y T N → ℕ) (ψ : Fin L → Fin N)
(hL : 30 ≤ L) (n : ℕ) (hn : n ≤ T) (s : Fin n → U)
(hg : Good ord ψ hL (.inl ⟨⟨n,by omega⟩,s⟩)) (t : ℕ) (ht : t ≤ n) :
Good ord ψ hL (.inl ⟨⟨t,by omega⟩,takePrefix ht s⟩) := by
induction n with
| zero => have he : t = 0 := by omega
subst t
exact hg
| succ n ih =>
by_cases ht' : t ≤ n
· have hp := good_prefix_big (ord := ord) (ψ := ψ) hL n (by omega) s hg
exact ih (by omega) (Fin.init s) hp ht'
· have he : t = n+1 := by omega
subst t
exact hg
end DirectedFeedback.RankGraph
namespace DirectedFeedback.Construction
open DirectedFeedback.SourceProbability FiniteDistribution
open RankGraph Prefix PrefixExperiment Games
variable {U V E X Y : Type} [Fintype U] [Fintype V] [Fintype E]
[Fintype X] [Fintype Y] [Nonempty X] [Nonempty Y]
variable {M T N : ℕ} [NeZero M] [NeZero T]
omit [Fintype U] [Fintype X] [NeZero M] [NeZero T] [Nonempty X] in
theorem cell_takePrefix {t : ℕ} (ht : t ≤ T) (a : PrefixData U X M T) :
cell (takePrefix ht a) = prefixCell t (sets a) := by
apply prefixCell_congr
intro j hj
simp only [sets, hj, dite_eq_left, takePrefix, lt_of_lt_of_le hj ht]
end DirectedFeedback.Construction
end
end
end
end
end
end
end
end
end
end
end
end
end
end
end
end
end
end
end
end
end
end
end
end
end
end
end
end
end
end
end
end
end OAI