roseking's picture
Add lean source and binary files (part 61)
ea76aac verified
Raw History Blame Contribute Delete
30.1 kB
import Mathlib
import OAI.Computability.DirectedFeedback.Encoding.PortTables
namespace OAI
noncomputable section
namespace DFVSGames.Foundations.PCP.CayleySampling
open scoped BigOperators
open Finset
open DFVSGames.Foundations.Hastad
variable {V D : Type*}
abbrev FalseFiber (χ : V → Bool) := {x : V // χ x = false}
def falseRepresentative (χ : V → Bool) (flip : V → V)
(hχ : ∀ x, χ (flip x) = !(χ x)) (x : V) : FalseFiber χ :=
if h : χ x = false then ⟨x, h⟩
else ⟨flip x, by cases hx : χ x <;> simp_all⟩
def booleanSplitEquiv (χ : V → Bool) (flip : V → V)
(hflip : Function.Involutive flip) (hχ : ∀ x, χ (flip x) = !(χ x)) :
V ≃ Bool × FalseFiber χ where
toFun x := (χ x, falseRepresentative χ flip hχ x)
invFun p := if p.1 then flip p.2.val else p.2.val
left_inv x := by
cases hx : χ x <;> simp [falseRepresentative, hx]
exact hflip x
right_inv p := by
rcases p with ⟨b, x⟩
apply Prod.ext
· cases b <;> simp [hχ, x.property]
· cases b <;> apply Subtype.ext <;>
simp [falseRepresentative, hχ, x.property]
exact hflip x.val
@[simp] theorem booleanSplitEquiv_fst (χ : V → Bool) (flip : V → V)
(hflip : Function.Involutive flip) (hχ : ∀ x, χ (flip x) = !(χ x)) (x : V) :
(booleanSplitEquiv χ flip hflip hχ x).1 = χ x := rfl
def sampleSplitEquiv (χ : V → Bool) (flip : V → V)
(hflip : Function.Involutive flip) (hχ : ∀ x, χ (flip x) = !(χ x)) :
(D → V) ≃ (D → Bool) × (D → FalseFiber χ) :=
(Equiv.piCongrRight fun _ : D => booleanSplitEquiv χ flip hflip hχ).trans
(Equiv.arrowProdEquivProdArrow D (fun _ => Bool) (fun _ => FalseFiber χ))
@[simp] theorem sampleSplitEquiv_fst (χ : V → Bool) (flip : V → V)
(hflip : Function.Involutive flip) (hχ : ∀ x, χ (flip x) = !(χ x)) (g : D → V) :
(sampleSplitEquiv χ flip hflip hχ g).1 = fun d => χ (g d) := rfl
theorem card_boolean_split [Fintype V] (χ : V → Bool) (flip : V → V)
(hflip : Function.Involutive flip) (hχ : ∀ x, χ (flip x) = !(χ x)) :
Fintype.card V = 2 * Fintype.card (FalseFiber χ) := by
simpa using Fintype.card_congr (booleanSplitEquiv χ flip hflip hχ)
theorem expect_boolean_samples [Fintype V] [Nonempty V] [Fintype D] [DecidableEq D]
(χ : V → Bool) (flip : V → V) (hflip : Function.Involutive flip)
(hχ : ∀ x, χ (flip x) = !(χ x)) (F : (D → Bool) → ℝ) :
(𝔼 g : D → V, F (fun d => χ (g d))) = 𝔼 b : D → Bool, F b := by
classical
have : Nonempty (FalseFiber χ) :=
⟨falseRepresentative χ flip hχ (Classical.choice ‹Nonempty V›)⟩
calc
(𝔼 g : D → V, F (fun d => χ (g d))) =
𝔼 p : (D → Bool) × (D → FalseFiber χ), F p.1 := by
apply Fintype.expect_equiv (sampleSplitEquiv χ flip hflip hχ)
intro g
rfl
_ = 𝔼 b : D → Bool, F b := by
rw [← Finset.univ_product_univ, Finset.expect_product]
simp only [Fintype.expect_const]
variable {I : Type*} [Fintype I] [DecidableEq I] [Fintype D] [DecidableEq D]
omit [DecidableEq I] in
theorem exists_true_of_ne_zero (s : Cube I) (hs : s ≠ fun _ => false) :
∃ i, s i = true := by
by_contra h
apply hs
funext i
cases hi : s i
· rfl
· exact False.elim (h ⟨i, hi⟩)
theorem expect_parity_samples (s : Cube I) (hs : s ≠ fun _ => false)
(F : (D → Bool) → ℝ) :
(𝔼 g : D → Cube I, F (fun d => AssignmentTester.parity s (g d))) =
𝔼 b : D → Bool, F b := by
obtain ⟨i, hi⟩ := exists_true_of_ne_zero s hs
exact expect_boolean_samples (AssignmentTester.parity s) (AssignmentTester.flipAt i)
(AssignmentTester.flipAt_twice i)
(fun x => AssignmentTester.parity_flipAt s i x hi) F
theorem expect_walsh_samples (s : Cube I) (hs : s ≠ fun _ => false)
(F : (D → ℝ) → ℝ) :
(𝔼 g : D → Cube I, F (fun d => walsh s (g d))) =
𝔼 b : D → Bool, F (fun d => bitSign (b d)) := by
simpa only [AssignmentTester.bitSign_parity] using
expect_parity_samples s hs (fun b => F (fun d => bitSign (b d)))
theorem expect_walsh_mean (s : Cube I) (hs : s ≠ fun _ => false) (F : ℝ → ℝ) :
(𝔼 g : D → Cube I, F (𝔼 d, walsh s (g d))) =
𝔼 b : D → Bool, F (𝔼 d, bitSign (b d)) :=
expect_walsh_samples s hs (fun z => F (𝔼 d, z d))
end DFVSGames.Foundations.PCP.CayleySampling
end
namespace DFVSGames.Foundations.PCP.CayleyTailCount
open scoped BigOperators
open DFVSGames.Foundations.Hastad (bitSign)
variable {α : Type*} [DecidableEq α]
def upperMasks (s : Finset α) (m : ℕ) : Finset (Finset α) :=
s.powerset.filter fun t => 3 * m ≤ t.card
def lowerMasks (s : Finset α) (m : ℕ) : Finset (Finset α) :=
s.powerset.filter fun t => t.card ≤ m
def badMasks (s : Finset α) (m : ℕ) : Finset (Finset α) :=
lowerMasks s m ∪ upperMasks s m
omit [DecidableEq α] in
theorem upperMasks_weight (s : Finset α) (m : ℕ) (hcard : s.card = 4 * m) :
(upperMasks s m).card * 3 ^ (3 * m) ≤ 4 ^ (4 * m) := by
calc
_ = ∑ _t ∈ upperMasks s m, 3 ^ (3 * m) := by simp
_ ≤ ∑ t ∈ upperMasks s m, 3 ^ t.card := by
apply Finset.sum_le_sum
intro t ht
exact Nat.pow_le_pow_right (by decide) (Finset.mem_filter.mp ht).2
_ ≤ ∑ t ∈ s.powerset, 3 ^ t.card := by
apply Finset.sum_le_sum_of_subset_of_nonneg
· exact Finset.filter_subset _ _
· intro t _ _
exact Nat.zero_le _
_ = 4 ^ s.card := by
simpa using Finset.sum_pow_mul_eq_add_pow (3 : ℕ) 1 s
_ = 4 ^ (4 * m) := by rw [hcard]
theorem lowerMasks_complement_image (s : Finset α) (m : ℕ)
(hcard : s.card = 4 * m) :
(lowerMasks s m).image (fun t => s \ t) = upperMasks s m := by
ext u
constructor
· intro hu
obtain ⟨t, ht, rfl⟩ := Finset.mem_image.mp hu
obtain ⟨hts, htlo⟩ := Finset.mem_filter.mp ht
have hsub := Finset.mem_powerset.mp hts
apply Finset.mem_filter.mpr
refine ⟨Finset.mem_powerset.mpr Finset.sdiff_subset, ?_⟩
rw [Finset.card_sdiff_of_subset hsub, hcard]
omega
· intro hu
obtain ⟨hus, huhi⟩ := Finset.mem_filter.mp hu
have hsub := Finset.mem_powerset.mp hus
apply Finset.mem_image.mpr
refine ⟨s \ u, ?_, Finset.sdiff_sdiff_eq_self hsub⟩
apply Finset.mem_filter.mpr
refine ⟨Finset.mem_powerset.mpr Finset.sdiff_subset, ?_⟩
rw [Finset.card_sdiff_of_subset hsub, hcard]
omega
theorem lowerMasks_card_eq_upperMasks (s : Finset α) (m : ℕ)
(hcard : s.card = 4 * m) :
(lowerMasks s m).card = (upperMasks s m).card := by
have hinj : Set.InjOn (fun t : Finset α => s \ t) (lowerMasks s m) := by
intro a ha b hb hab
have has := Finset.mem_powerset.mp (Finset.mem_filter.mp ha).1
have hbs := Finset.mem_powerset.mp (Finset.mem_filter.mp hb).1
have h := congrArg (fun t : Finset α => s \ t) hab
simpa only [Finset.sdiff_sdiff_eq_self has, Finset.sdiff_sdiff_eq_self hbs] using h
calc
_ = ((lowerMasks s m).image (fun t => s \ t)).card :=
(Finset.card_image_of_injOn hinj).symm
_ = _ := congrArg Finset.card (lowerMasks_complement_image s m hcard)
private theorem ratio_le_of_weight_inline_CayleyTailCount (c m : ℕ)
(h : c * 3 ^ (3 * m) ≤ 4 ^ (4 * m)) :
(c : ℝ) / 2 ^ (4 * m) ≤ (16 / 27 : ℝ) ^ m := by
have hc : (c : ℝ) * 3 ^ (3 * m) ≤ 4 ^ (4 * m) := by exact_mod_cast h
have h3 : 0 < (3 : ℝ) ^ (3 * m) := by positivity
have h2 : 0 < (2 : ℝ) ^ (4 * m) := by positivity
calc
_ ≤ ((4 : ℝ) ^ (4 * m) / 3 ^ (3 * m)) / 2 ^ (4 * m) :=
div_le_div_of_nonneg_right ((le_div_iff₀ h3).mpr hc) h2.le
_ = _ := by
rw [pow_mul, pow_mul, pow_mul, ← div_pow, ← div_pow]
norm_num
omit [DecidableEq α] in
theorem upperMasks_ratio_le (s : Finset α) (m : ℕ) (hcard : s.card = 4 * m) :
((upperMasks s m).card : ℝ) / 2 ^ s.card ≤ (16 / 27 : ℝ) ^ m := by
rw [hcard]
exact ratio_le_of_weight_inline_CayleyTailCount _ m (upperMasks_weight s m hcard)
theorem lowerMasks_ratio_le (s : Finset α) (m : ℕ) (hcard : s.card = 4 * m) :
((lowerMasks s m).card : ℝ) / 2 ^ s.card ≤ (16 / 27 : ℝ) ^ m := by
rw [lowerMasks_card_eq_upperMasks s m hcard]
exact upperMasks_ratio_le s m hcard
theorem badMasks_ratio_le_sharp (s : Finset α) (m : ℕ) (hcard : s.card = 4 * m) :
((badMasks s m).card : ℝ) / 2 ^ s.card ≤ 2 * (16 / 27 : ℝ) ^ m := by
have hc : ((badMasks s m).card : ℝ) ≤
(lowerMasks s m).card + (upperMasks s m).card := by
exact_mod_cast (Finset.card_union_le (lowerMasks s m) (upperMasks s m))
have hpos : 0 < (2 : ℝ) ^ s.card := by positivity
calc
_ ≤ ((lowerMasks s m).card + (upperMasks s m).card : ℝ) / 2 ^ s.card :=
div_le_div_of_nonneg_right hc hpos.le
_ = ((lowerMasks s m).card : ℝ) / 2 ^ s.card +
((upperMasks s m).card : ℝ) / 2 ^ s.card := add_div _ _ _
_ ≤ (16 / 27 : ℝ) ^ m + (16 / 27 : ℝ) ^ m :=
add_le_add (lowerMasks_ratio_le s m hcard) (upperMasks_ratio_le s m hcard)
_ = _ := by ring
theorem badMasks_ratio_le (s : Finset α) (m : ℕ) (hcard : s.card = 4 * m) :
((badMasks s m).card : ℝ) / 2 ^ s.card ≤ 2 * (2 / 3 : ℝ) ^ m := by
apply (badMasks_ratio_le_sharp s m hcard).trans
exact mul_le_mul_of_nonneg_left
(pow_le_pow_left₀ (by norm_num) (by norm_num) m) (by norm_num)
section Words
variable {D : Type*} [Fintype D]
def trueMask (word : D → Bool) : Finset D :=
Finset.univ.filter fun i => word i = true
def countTrue (word : D → Bool) : ℕ := (trueMask word).card
def badWord (m : ℕ) (word : D → Bool) : Prop :=
countTrue word ≤ m ∨ 3 * m ≤ countTrue word
instance (m : ℕ) (word : D → Bool) : Decidable (badWord m word) :=
inferInstanceAs (Decidable (countTrue word ≤ m ∨ 3 * m ≤ countTrue word))
theorem bitSign_mean_eq [Nonempty D] (word : D → Bool) :
(𝔼 i, bitSign (word i)) =
1 - 2 * ((countTrue word : ℝ) / Fintype.card D) := by
have hsign (b : Bool) : bitSign b = 1 - 2 * (if b = true then (1 : ℝ) else 0) := by
cases b <;> norm_num [bitSign]
calc
_ = (𝔼 i : D, (1 - 2 * (if word i = true then (1 : ℝ) else 0))) := by
apply Finset.expect_congr rfl
intro i _
exact hsign (word i)
_ = 1 - 2 * (𝔼 i, if word i = true then (1 : ℝ) else 0) := by
rw [Finset.expect_sub_distrib, ← Finset.mul_expect, Fintype.expect_const]
_ = _ := by
rw [Fintype.expect_eq_sum_div_card, Finset.sum_boole]
rfl
theorem not_badWord_bias (m : ℕ) (hcard : Fintype.card D = 4 * m) (hm : 0 < m)
(word : D → Bool) (hword : ¬ badWord m word) :
|𝔼 i, bitSign (word i)| ≤ (1 / 2 : ℝ) := by
have hD : 0 < Fintype.card D := by rw [hcard]; omega
let : Nonempty D := Fintype.card_pos_iff.mp hD
have hlo : m < countTrue word := lt_of_not_ge (not_or.mp hword).1
have hhi : countTrue word < 3 * m := lt_of_not_ge (not_or.mp hword).2
have hmr : 0 < (m : ℝ) := by exact_mod_cast hm
have hlor : (m : ℝ) ≤ countTrue word := by exact_mod_cast hlo.le
have hhir : (countTrue word : ℝ) ≤ 3 * m := by exact_mod_cast hhi.le
have hden : 0 < 4 * (m : ℝ) := by positivity
have hratioLo : (1 / 4 : ℝ) ≤ (countTrue word : ℝ) / (4 * m) :=
(le_div_iff₀ hden).mpr (by nlinarith)
have hratioHi : (countTrue word : ℝ) / (4 * m) ≤ (3 / 4 : ℝ) :=
(div_le_iff₀ hden).mpr (by nlinarith)
rw [bitSign_mean_eq word, hcard]
simp only [Nat.cast_mul, Nat.cast_ofNat]
apply abs_le.mpr
constructor <;> linarith
def wordMaskEquiv [DecidableEq D] : (D → Bool) ≃ Finset D where
toFun := trueMask
invFun s i := decide (i ∈ s)
left_inv word := by
funext i
cases h : word i <;> simp [trueMask, h]
right_inv s := by
ext i
simp [trueMask]
theorem twoTail_probability [DecidableEq D] (m : ℕ) (hcard : Fintype.card D = 4 * m) :
(𝔼 word : D → Bool, if badWord m word then (1 : ℝ) else 0) ≤
2 * (2 / 3 : ℝ) ^ m := by
classical
have he : (𝔼 word : D → Bool, if badWord m word then (1 : ℝ) else 0) =
(𝔼 s : Finset D, if s.card ≤ m ∨ 3 * m ≤ s.card then (1 : ℝ) else 0) := by
apply Finset.expect_equiv (wordMaskEquiv (D := D))
· intro word
simp
· intro word _
rfl
have hs : (Finset.univ.filter fun s : Finset D => s.card ≤ m ∨ 3 * m ≤ s.card) =
badMasks (Finset.univ : Finset D) m := by
ext s
simp [badMasks, lowerMasks, upperMasks]
rw [he, Fintype.expect_eq_sum_div_card, Finset.sum_boole, hs, Fintype.card_finset]
simpa only [Nat.cast_pow, Nat.cast_ofNat, Finset.card_univ] using
badMasks_ratio_le (Finset.univ : Finset D) m (by simpa using hcard)
end Words
theorem union_bound_le_half (n m : ℕ) (hm : 3 * (n + 2) ≤ m) :
(2 : ℝ) ^ n * (2 * (2 / 3 : ℝ) ^ m) ≤ 1 / 2 := by
have hpow : (2 / 3 : ℝ) ^ m ≤ (2 / 3 : ℝ) ^ (3 * (n + 2)) :=
pow_le_pow_of_le_one (by norm_num) (by norm_num) hm
have hbase : (2 / 3 : ℝ) ^ (3 * (n + 2)) ≤ (1 / 2 : ℝ) ^ (n + 2) := by
rw [pow_mul]
exact pow_le_pow_left₀ (by positivity) (by norm_num) (n + 2)
calc
_ ≤ (2 : ℝ) ^ n * (2 * (1 / 2 : ℝ) ^ (n + 2)) := by
exact mul_le_mul_of_nonneg_left
(mul_le_mul_of_nonneg_left (hpow.trans hbase) (by norm_num)) (by positivity)
_ = 1 / 2 := by
rw [pow_add, div_pow]
norm_num
field_simp
norm_num
theorem fixed_parameters : 3 * (448 + 2) ≤ 16384 ∧ 4 * 16384 = 2 ^ 16 := by
norm_num
theorem fixed_union_bound :
(2 : ℝ) ^ 448 * (2 * (2 / 3 : ℝ) ^ 16384) ≤ 1 / 2 :=
union_bound_le_half 448 16384 fixed_parameters.1
theorem seventh_power_size : (2 : ℕ) ^ 448 = (((2 : ℕ) ^ 16) ^ 7) ^ 4 := by
rw [← pow_mul, ← pow_mul]
theorem seventh_power_abs_le {a : ℝ} (ha : |a| ≤ 1 / 2) : |a ^ 7| ≤ 1 / 128 := by
rw [abs_pow]
calc
|a| ^ 7 ≤ (1 / 2 : ℝ) ^ 7 := pow_le_pow_left₀ (abs_nonneg a) ha 7
_ = 1 / 128 := by norm_num
end DFVSGames.Foundations.PCP.CayleyTailCount
namespace DFVSGames.Foundations.PCP.SpectralReturn
open PoweringWalks
open scoped BigOperators
noncomputable section
def mean {A : Type*} [Fintype A] (f : A → ℝ) : ℝ := Finset.univ.expect f
def energy {A : Type*} [Fintype A] (f : A → ℝ) : ℝ := mean (fun x => (f x) ^ 2)
def correlation {A : Type*} [Fintype A] (f g : A → ℝ) : ℝ :=
mean (fun x => f x * g x)
def averagingOperator {V D : Type*} [Fintype D] (G : PortGraph V D)
(f : V → ℝ) (v : V) : ℝ := Finset.univ.expect (fun d => f (G.rot (v, d)).1)
def iterateOperator {V D : Type*} [Fintype D] (G : PortGraph V D) :
Nat → (V → ℝ) → (V → ℝ)
| 0, f => f
| n + 1, f => averagingOperator G (iterateOperator G n f)
def edgeProfile {V D : Type*} [Fintype D] (bad : V × D → Bool) (v : V) : ℝ :=
Finset.univ.expect (fun d => if bad (v, d) then 1 else 0)
def edgeDensity {V D : Type*} [Fintype V] [Fintype D] (bad : V × D → Bool) : ℝ :=
mean (edgeProfile bad)
def markedStep {V D : Type*} [Fintype D] (G : PortGraph V D)
(bad : V × D → Bool) (f : V → ℝ) (v : V) : ℝ :=
Finset.univ.expect (fun d => if bad (v, d) then f (G.rot (v, d)).1 else 0)
def returnMass {V D : Type*} [Fintype V] [Fintype D]
(G : PortGraph V D) (bad : V × D → Bool) (gap : Nat) : ℝ :=
mean (markedStep G bad (iterateOperator G gap (edgeProfile bad)))
structure SpectralCertificate {V D : Type*} [Fintype V] [Fintype D]
(G : PortGraph V D) (lambda : ℝ) : Prop where
nonnegative : 0 ≤ lambda
lt_one : lambda < 1
contraction : ∀ f : V → ℝ, mean f = 0 →
energy (averagingOperator G f) ≤ lambda ^ 2 * energy f
section Averages
variable {A B : Type*} [Fintype A] [Fintype B]
theorem mean_eq_sum_div_card (f : A → ℝ) :
mean f = (∑ x, f x) / (Fintype.card A : ℝ) :=
Fintype.expect_eq_sum_div_card f
theorem mean_equiv (e : A ≃ B) (f : B → ℝ) :
mean (fun x => f (e x)) = mean f :=
Fintype.expect_equiv e _ _ (fun _ => rfl)
theorem mean_prod (f : A × B → ℝ) :
mean f = mean (fun a => mean (fun b => f (a, b))) := by
simpa only [mean, Finset.univ_product_univ] using
Finset.expect_product (Finset.univ : Finset A) (Finset.univ : Finset B) f
@[simp] theorem mean_const [Nonempty A] (c : ℝ) : mean (fun _ : A => c) = c :=
Fintype.expect_const c
theorem mean_add (f g : A → ℝ) :
mean (fun x => f x + g x) = mean f + mean g :=
Finset.expect_add_distrib _ f g
theorem mean_sub (f g : A → ℝ) :
mean (fun x => f x - g x) = mean f - mean g :=
Finset.expect_sub_distrib _ f g
theorem mean_mul_left (c : ℝ) (f : A → ℝ) :
mean (fun x => c * f x) = c * mean f :=
(Finset.mul_expect _ f c).symm
theorem mean_mul_right (f : A → ℝ) (c : ℝ) :
mean (fun x => f x * c) = mean f * c :=
(Finset.expect_mul _ f c).symm
theorem mean_nonnegative (f : A → ℝ) (hf : ∀ x, 0 ≤ f x) : 0 ≤ mean f :=
Finset.expect_nonneg (fun x _ => hf x)
theorem mean_mono {f g : A → ℝ} (h : ∀ x, f x ≤ g x) : mean f ≤ mean g :=
Finset.expect_le_expect (fun x _ => h x)
theorem energy_nonnegative (f : A → ℝ) : 0 ≤ energy f :=
mean_nonnegative _ (fun x => sq_nonneg (f x))
theorem correlation_sq_le (f g : A → ℝ) :
correlation f g ^ 2 ≤ energy f * energy g :=
Finset.expect_mul_sq_le_sq_mul_sq Finset.univ f g
theorem energy_sub_const [Nonempty A] (f : A → ℝ) (c : ℝ) :
energy (fun x => f x - c) = energy f - 2 * c * mean f + c ^ 2 := by
have h : (fun x => (f x - c) ^ 2) =
(fun x => (f x) ^ 2 - (2 * c) * f x + c ^ 2) := by
funext x
ring
unfold energy
rw [h, mean_add, mean_sub, mean_mul_left, mean_const]
theorem correlation_sub_const [Nonempty A] (f g : A → ℝ) (c d : ℝ) :
correlation (fun x => f x - c) (fun x => g x - d) =
correlation f g - d * mean f - c * mean g + c * d := by
have h : (fun x => (f x - c) * (g x - d)) =
(fun x => f x * g x - d * f x - c * g x + c * d) := by
funext x
ring
unfold correlation
rw [h, mean_add, mean_sub, mean_sub, mean_mul_left, mean_mul_left, mean_const]
end Averages
section Graph
variable {V D : Type*} [Fintype V] [Fintype D] [Nonempty V] [Nonempty D]
(G : PortGraph V D)
omit [Nonempty V] in
theorem mean_operator (f : V → ℝ) : mean (averagingOperator G f) = mean f := by
calc
mean (averagingOperator G f) = mean (fun e : V × D => f (G.rot e).1) :=
(mean_prod (fun e : V × D => f (G.rot e).1)).symm
_ = mean (fun e : V × D => f e.1) := mean_equiv G.rot (fun e => f e.1)
_ = mean (fun v : V => mean (fun _ : D => f v)) :=
mean_prod (fun e : V × D => f e.1)
_ = mean f := by simp only [mean_const]
omit [Fintype V] [Nonempty V] in
theorem operator_sub_const (f : V → ℝ) (c : ℝ) :
averagingOperator G (fun v => f v - c) = fun v => averagingOperator G f v - c := by
funext v
change mean (fun d => f (G.rot (v, d)).1 - c) = _
rw [mean_sub, mean_const]
rfl
omit [Nonempty V] in
theorem mean_iterate (n : Nat) (f : V → ℝ) : mean (iterateOperator G n f) = mean f := by
induction n with
| zero => rfl
| succ n ih => rw [iterateOperator, mean_operator, ih]
omit [Fintype V] [Nonempty V] in
theorem iterate_sub_const (n : Nat) (f : V → ℝ) (c : ℝ) :
iterateOperator G n (fun v => f v - c) = fun v => iterateOperator G n f v - c := by
induction n with
| zero => rfl
| succ n ih => rw [iterateOperator, ih, operator_sub_const]; rfl
omit [Nonempty V] [Nonempty D] in
theorem mean_markedStep (bad : V × D → Bool)
(reversal : ∀ e, bad (G.rot e) = bad e) (f : V → ℝ) :
mean (markedStep G bad f) = correlation (edgeProfile bad) f := by
calc
mean (markedStep G bad f) =
mean (fun e : V × D => if bad e then f (G.rot e).1 else 0) := by
exact (mean_prod (fun e : V × D => if bad e then f (G.rot e).1 else 0)).symm
_ = mean (fun e : V × D => if bad (G.rot e)
then f (G.rot (G.rot e)).1 else 0) :=
(mean_equiv G.rot (fun e : V × D => if bad e then f (G.rot e).1 else 0)).symm
_ = mean (fun e : V × D => if bad e then f e.1 else 0) := by
congr 1
funext e
rw [reversal e, G.rot_involutive e]
_ = mean (fun v => mean (fun d => if bad (v, d) then f v else 0)) :=
mean_prod (fun e : V × D => if bad e then f e.1 else 0)
_ = correlation (edgeProfile bad) f := by
unfold correlation
congr 1
funext v
have h : (fun d => if bad (v, d) then f v else 0) =
(fun d => (if bad (v, d) then (1 : ℝ) else 0) * f v) := by
funext d
cases bad (v, d) <;> simp
rw [h, mean_mul_right]
rfl
omit [Nonempty V] [Nonempty D] in
theorem returnMass_eq_correlation (bad : V × D → Bool)
(reversal : ∀ e, bad (G.rot e) = bad e) (gap : Nat) :
returnMass G bad gap = correlation (edgeProfile bad)
(iterateOperator G gap (edgeProfile bad)) :=
mean_markedStep G bad reversal _
omit [Nonempty V] in
theorem iterate_energy_bound (lambda : ℝ) (certificate : SpectralCertificate G lambda)
(f : V → ℝ) (hf : mean f = 0) (n : Nat) :
energy (iterateOperator G n f) ≤ (lambda ^ n) ^ 2 * energy f := by
induction n with
| zero => simp [iterateOperator]
| succ n ih =>
calc
energy (iterateOperator G (n + 1) f) ≤
lambda ^ 2 * energy (iterateOperator G n f) :=
certificate.contraction _ ((mean_iterate G n f).trans hf)
_ ≤ lambda ^ 2 * ((lambda ^ n) ^ 2 * energy f) :=
mul_le_mul_of_nonneg_left ih (sq_nonneg lambda)
_ = (lambda ^ (n + 1)) ^ 2 * energy f := by rw [pow_succ]; ring
omit [Nonempty V] in
theorem zero_mean_correlation_bound (lambda : ℝ)
(certificate : SpectralCertificate G lambda) (f : V → ℝ)
(hf : mean f = 0) (n : Nat) :
correlation f (iterateOperator G n f) ≤ lambda ^ n * energy f := by
have hsq : correlation f (iterateOperator G n f) ^ 2 ≤
(lambda ^ n * energy f) ^ 2 := by
calc
_ ≤ energy f * energy (iterateOperator G n f) := correlation_sq_le _ _
_ ≤ energy f * ((lambda ^ n) ^ 2 * energy f) :=
mul_le_mul_of_nonneg_left (iterate_energy_bound G lambda certificate f hf n)
(energy_nonnegative f)
_ = _ := by ring
exact le_of_sq_le_sq hsq
(mul_nonneg (pow_nonneg certificate.nonnegative n) (energy_nonnegative f))
theorem correlation_centered (f : V → ℝ) (n : Nat) :
correlation f (iterateOperator G n f) = mean f ^ 2 +
correlation (fun v => f v - mean f)
(iterateOperator G n (fun v => f v - mean f)) := by
rw [iterate_sub_const, correlation_sub_const, mean_iterate]
ring
end Graph
section Profiles
variable {V D : Type*} [Fintype V] [Fintype D] [Nonempty V] [Nonempty D]
omit [Nonempty V] [Nonempty D] in
theorem edgeDensity_eq_edge_mean (bad : V × D → Bool) :
edgeDensity bad = mean (fun e => if bad e then (1 : ℝ) else 0) :=
(mean_prod (fun e : V × D => if bad e then (1 : ℝ) else 0)).symm
omit [Nonempty V] [Nonempty D] in
theorem edgeDensity_eq_card (bad : V × D → Bool) :
edgeDensity bad =
((Finset.univ.filter (fun e => bad e = true)).card : ℝ) /
(Fintype.card (V × D) : ℝ) := by
rw [edgeDensity_eq_edge_mean, mean_eq_sum_div_card]
simp only [Finset.sum_boole]
omit [Fintype V] [Nonempty V] [Nonempty D] in
theorem edgeProfile_nonnegative (bad : V × D → Bool) (v : V) : 0 ≤ edgeProfile bad v :=
mean_nonnegative _ (fun d => by cases bad (v, d) <;> norm_num)
omit [Fintype V] [Nonempty V] in
theorem edgeProfile_le_one (bad : V × D → Bool) (v : V) : edgeProfile bad v ≤ 1 := by
change mean (fun d => if bad (v, d) then (1 : ℝ) else 0) ≤ 1
calc
_ ≤ mean (fun _ : D => (1 : ℝ)) := mean_mono (fun d => by
cases bad (v, d) <;> norm_num)
_ = 1 := mean_const _
omit [Nonempty V] [Nonempty D] in
theorem edgeDensity_nonnegative (bad : V × D → Bool) : 0 ≤ edgeDensity bad :=
mean_nonnegative _ (edgeProfile_nonnegative bad)
theorem edgeDensity_le_one (bad : V × D → Bool) : edgeDensity bad ≤ 1 := by
calc
_ ≤ mean (fun _ : V => (1 : ℝ)) := mean_mono (edgeProfile_le_one bad)
_ = 1 := mean_const _
omit [Nonempty V] in
theorem edgeProfile_energy_le_density (bad : V × D → Bool) :
energy (edgeProfile bad) ≤ edgeDensity bad :=
mean_mono (fun v => by
have h0 := edgeProfile_nonnegative bad v
have h1 := edgeProfile_le_one bad v
nlinarith)
theorem edgeProfile_centered_energy (bad : V × D → Bool) :
energy (fun v => edgeProfile bad v - edgeDensity bad) ≤
edgeDensity bad * (1 - edgeDensity bad) := by
rw [energy_sub_const]
have h := edgeProfile_energy_le_density bad
change energy (edgeProfile bad) - 2 * edgeDensity bad * edgeDensity bad +
edgeDensity bad ^ 2 ≤ _
nlinarith
theorem returnMass_le_sharp (G : PortGraph V D) (lambda : ℝ)
(certificate : SpectralCertificate G lambda) (bad : V × D → Bool)
(reversal : ∀ e, bad (G.rot e) = bad e) (gap : Nat) :
returnMass G bad gap ≤ edgeDensity bad ^ 2 +
lambda ^ gap * (edgeDensity bad * (1 - edgeDensity bad)) := by
rw [returnMass_eq_correlation G bad reversal, correlation_centered]
have hzero : mean (fun v => edgeProfile bad v - edgeDensity bad) = 0 := by
rw [mean_sub, mean_const]
simp only [edgeDensity, sub_self]
have hc := zero_mean_correlation_bound G lambda certificate
(fun v => edgeProfile bad v - edgeDensity bad) hzero gap
have hv := mul_le_mul_of_nonneg_left (edgeProfile_centered_energy bad)
(pow_nonneg certificate.nonnegative gap)
change edgeDensity bad ^ 2 + _ ≤ _
exact add_le_add (le_refl _) (hc.trans hv)
theorem returnMass_le (G : PortGraph V D) (lambda : ℝ)
(certificate : SpectralCertificate G lambda) (bad : V × D → Bool)
(reversal : ∀ e, bad (G.rot e) = bad e) (gap : Nat) :
returnMass G bad gap ≤ edgeDensity bad ^ 2 + edgeDensity bad * lambda ^ gap := by
have h := returnMass_le_sharp G lambda certificate bad reversal gap
have hvar : edgeDensity bad * (1 - edgeDensity bad) ≤ edgeDensity bad := by
nlinarith [sq_nonneg (edgeDensity bad)]
have hm := mul_le_mul_of_nonneg_left hvar (pow_nonneg certificate.nonnegative gap)
nlinarith
theorem adjacent_returnMass_le (G : PortGraph V D) (lambda : ℝ)
(certificate : SpectralCertificate G lambda) (bad : V × D → Bool)
(reversal : ∀ e, bad (G.rot e) = bad e) :
returnMass G bad 0 ≤ edgeDensity bad := by
have h := returnMass_le_sharp G lambda certificate bad reversal 0
norm_num at h
nlinarith
end Profiles
theorem geometric_sum_le (lambda : ℝ) (h0 : 0 ≤ lambda) (h1 : lambda < 1) (n : Nat) :
(∑ k ∈ Finset.range n, lambda ^ k) ≤ 1 / (1 - lambda) := by
have hsum : (∑ k ∈ Finset.range n, lambda ^ k) * (1 - lambda) = 1 - lambda ^ n := by
induction n with
| zero => simp
| succ n ih => rw [Finset.sum_range_succ, pow_succ]; nlinarith
have hden : 0 < 1 - lambda := by linarith
apply (le_div_iff₀ hden).2
rw [hsum]
linarith [pow_nonneg h0 n]
variable {V D : Type*} [Fintype V] [Fintype D] [Nonempty V] [Nonempty D]
theorem sum_returnMass_le (G : PortGraph V D) (lambda : ℝ)
(certificate : SpectralCertificate G lambda) (bad : V × D → Bool)
(reversal : ∀ e, bad (G.rot e) = bad e) (n : Nat) :
(∑ gap ∈ Finset.range n, returnMass G bad gap) ≤
(n : ℝ) * edgeDensity bad ^ 2 + edgeDensity bad / (1 - lambda) := by
calc
_ ≤ ∑ gap ∈ Finset.range n,
(edgeDensity bad ^ 2 + edgeDensity bad * lambda ^ gap) :=
Finset.sum_le_sum (fun gap _ => returnMass_le G lambda certificate bad reversal gap)
_ = (n : ℝ) * edgeDensity bad ^ 2 +
edgeDensity bad * (∑ gap ∈ Finset.range n, lambda ^ gap) := by
rw [Finset.sum_add_distrib, ← Finset.mul_sum]
simp only [Finset.sum_const, Finset.card_range, nsmul_eq_mul]
_ ≤ (n : ℝ) * edgeDensity bad ^ 2 + edgeDensity bad * (1 / (1 - lambda)) :=
add_le_add (le_refl _)
(mul_le_mul_of_nonneg_left
(geometric_sum_le lambda certificate.nonnegative certificate.lt_one n)
(edgeDensity_nonnegative bad))
_ = _ := by ring
theorem triangular_returnMass_sum_le (G : PortGraph V D) (lambda : ℝ)
(certificate : SpectralCertificate G lambda) (bad : V × D → Bool)
(reversal : ∀ e, bad (G.rot e) = bad e) (n : Nat) :
(∑ j ∈ Finset.range n, ∑ gap ∈ Finset.range j, returnMass G bad gap) ≤
((n : ℝ) * ((n : ℝ) - 1) / 2) * edgeDensity bad ^ 2 +
(n : ℝ) * (edgeDensity bad / (1 - lambda)) := by
induction n with
| zero => simp
| succ n ih =>
rw [Finset.sum_range_succ]
have hrow := sum_returnMass_le G lambda certificate bad reversal n
have h := add_le_add ih hrow
calc
_ ≤ ((n : ℝ) * ((n : ℝ) - 1) / 2) * edgeDensity bad ^ 2 +
(n : ℝ) * (edgeDensity bad / (1 - lambda)) +
((n : ℝ) * edgeDensity bad ^ 2 + edgeDensity bad / (1 - lambda)) := h
_ = _ := by simp only [Nat.cast_add, Nat.cast_one]; ring
theorem secondMoment_return_envelope (G : PortGraph V D) (lambda : ℝ)
(certificate : SpectralCertificate G lambda) (bad : V × D → Bool)
(reversal : ∀ e, bad (G.rot e) = bad e) (n : Nat) :
(n : ℝ) * edgeDensity bad +
2 * (∑ j ∈ Finset.range n, ∑ gap ∈ Finset.range j, returnMass G bad gap) ≤
(n : ℝ) * edgeDensity bad *
(1 + 2 / (1 - lambda) + ((n : ℝ) - 1) * edgeDensity bad) := by
calc
_ ≤ (n : ℝ) * edgeDensity bad +
2 * (((n : ℝ) * ((n : ℝ) - 1) / 2) * edgeDensity bad ^ 2 +
(n : ℝ) * (edgeDensity bad / (1 - lambda))) :=
add_le_add (le_refl _)
(mul_le_mul_of_nonneg_left
(triangular_returnMass_sum_le G lambda certificate bad reversal n) (by norm_num))
_ = _ := by ring
end
end DFVSGames.Foundations.PCP.SpectralReturn
end OAI