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Add lean source and binary files (part 61)
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import Mathlib
import OAI.Computability.MaxCut.Estimates.Density
namespace OAI
noncomputable section
namespace OptimalMaxCut.GaussianBellman
open scoped BigOperators
open Set Finset
abbrev BitCube (n : ℕ) := Fin n → Bool
noncomputable def corrAverage (t : ℝ) : (n : ℕ) → (BitCube n → BitCube n → ℝ) → ℝ
| 0, F => F default default
| n + 1, F => corrAverage t n (fun x y =>
∑ a : Bool, ∑ b : Bool, bitPairWeight t a b * F (Fin.cons a x) (Fin.cons b y))
noncomputable def midpointSection {n : ℕ} (f : BitCube (n + 1) → ℝ) (x : BitCube n) : ℝ :=
(f (Fin.cons false x) + f (Fin.cons true x)) / 2
noncomputable def slopeSection {n : ℕ} (f : BitCube (n + 1) → ℝ) (x : BitCube n) : ℝ :=
(f (Fin.cons false x) - f (Fin.cons true x)) / 2
theorem section_decomposition {n : ℕ} (f : BitCube (n + 1) → ℝ) (x : BitCube n) (a : Bool) :
f (Fin.cons a x) = midpointSection f x + sign a * slopeSection f x := by
cases a <;> simp only [midpointSection, slopeSection, sign, Bool.false_eq_true, ↓reduceIte] <;> ring
theorem midpointSection_mem {n : ℕ} {f : BitCube (n + 1) → ℝ} {l u : ℝ}
(hf : ∀ x, f x ∈ Icc l u) (x : BitCube n) : midpointSection f x ∈ Icc l u := by
have h0 := hf (Fin.cons false x)
have h1 := hf (Fin.cons true x)
dsimp [midpointSection]
constructor <;> linarith [h0.1, h0.2, h1.1, h1.2]
theorem expect_section {n : ℕ} (f : BitCube (n + 1) → ℝ) :
(𝔼 x, f x) = 𝔼 x, midpointSection f x := by
have he := (Fintype.expect_equiv (Fin.consEquiv (fun _ : Fin (n + 1) => Bool))
(fun p => f (Fin.cons p.1 p.2)) f (fun _ => rfl)).symm
rw [← Finset.univ_product_univ, Finset.expect_product, Finset.expect_comm] at he
rw [he]
apply Finset.expect_congr rfl
intro x _
rw [Fintype.expect_eq_sum_div_card]
simp [midpointSection, add_comm]
theorem corrAverage_mono {t : ℝ} (ht : t ∈ Icc (-1 : ℝ) 1) (n : ℕ)
{F G : BitCube n → BitCube n → ℝ} (h : ∀ x y, F x y ≤ G x y) :
corrAverage t n F ≤ corrAverage t n G := by
induction n with
| zero => exact h _ _
| succ n ih =>
apply ih
intro x y
apply Finset.sum_le_sum
intro a _
apply Finset.sum_le_sum
intro b _
exact mul_le_mul_of_nonneg_left (h _ _) (bitPairWeight_nonneg ht a b)
theorem corrAverage_add (t : ℝ) (n : ℕ) (F G : BitCube n → BitCube n → ℝ) :
corrAverage t n (fun x y => F x y + G x y) = corrAverage t n F + corrAverage t n G := by
induction n with
| zero => rfl
| succ n ih =>
simp only [corrAverage, mul_add, Finset.sum_add_distrib, ih]
theorem corrAverage_mul_const (t c : ℝ) (n : ℕ) (F : BitCube n → BitCube n → ℝ) :
corrAverage t n (fun x y => F x y * c) = corrAverage t n F * c := by
induction n with
| zero => rfl
| succ n ih =>
simp only [corrAverage, ← mul_assoc, ← Finset.sum_mul, ih]
theorem corrAverage_const_mul (t c : ℝ) (n : ℕ) (F : BitCube n → BitCube n → ℝ) :
corrAverage t n (fun x y => c * F x y) = c * corrAverage t n F := by
simpa only [mul_comm] using corrAverage_mul_const t c n F
theorem expect_zero_cube (f : BitCube 0 → ℝ) : (𝔼 x, f x) = f default := by
have hf : f = fun _ => f default := funext (fun x => congrArg f (Subsingleton.elim x default))
rw [hf, Fintype.expect_const]
theorem corrAverage_fst (t : ℝ) (n : ℕ) (f : BitCube n → ℝ) :
corrAverage t n (fun x _ => f x) = 𝔼 x, f x := by
induction n with
| zero => exact (expect_zero_cube f).symm
| succ n ih =>
have hp (x : BitCube n) :
(∑ a : Bool, ∑ b : Bool, bitPairWeight t a b * f (Fin.cons a x)) = midpointSection f x := by
simp only [Fintype.sum_bool, bitPairWeight, sign, Bool.false_eq_true, ↓reduceIte, midpointSection]
ring
simp only [corrAverage, hp]
rw [ih, expect_section]
theorem corrAverage_snd (t : ℝ) (n : ℕ) (f : BitCube n → ℝ) :
corrAverage t n (fun _ y => f y) = 𝔼 y, f y := by
induction n with
| zero => exact (expect_zero_cube f).symm
| succ n ih =>
have hp (y : BitCube n) :
(∑ a : Bool, ∑ b : Bool, bitPairWeight t a b * f (Fin.cons b y)) = midpointSection f y := by
simp only [Fintype.sum_bool, bitPairWeight, sign, Bool.false_eq_true, ↓reduceIte, midpointSection]
ring
simp only [corrAverage, hp]
rw [ih, expect_section]
noncomputable def cubicCost : (n : ℕ) → (BitCube n → ℝ) → ℝ
| 0, _ => 0
| n + 1, f => (𝔼 x, |slopeSection f x| ^ 3) + cubicCost n (midpointSection f)
theorem finite_bellman_tensorization {t C l u : ℝ} (ht : t ∈ Icc (-1 : ℝ) 1)
(J : ℝ → ℝ → ℝ)
(hJ : ∀ x y a b : ℝ,
(∀ e : Bool, x + sign e * a ∈ Icc l u) →
(∀ e : Bool, y + sign e * b ∈ Icc l u) →
(∑ e : Bool, ∑ f : Bool, bitPairWeight t e f * J (x + sign e * a) (y + sign f * b)) ≤
J x y + C * (|a| ^ 3 + |b| ^ 3))
(n : ℕ) (f g : BitCube n → ℝ) (hf : ∀ x, f x ∈ Icc l u) (hg : ∀ x, g x ∈ Icc l u) :
corrAverage t n (fun x y => J (f x) (g y)) ≤
J (𝔼 x, f x) (𝔼 y, g y) + C * (cubicCost n f + cubicCost n g) := by
induction n with
| zero => simp only [corrAverage, cubicCost, expect_zero_cube, add_zero, mul_zero, le_refl]
| succ n ih =>
have hlocal (x y : BitCube n) :
(∑ a : Bool, ∑ b : Bool, bitPairWeight t a b * J (f (Fin.cons a x)) (g (Fin.cons b y))) ≤
J (midpointSection f x) (midpointSection g y) +
C * (|slopeSection f x| ^ 3 + |slopeSection g y| ^ 3) := by
simpa only [← section_decomposition] using hJ (midpointSection f x) (midpointSection g y)
(slopeSection f x) (slopeSection g y)
(fun e => by rw [← section_decomposition]; exact hf _)
(fun e => by rw [← section_decomposition]; exact hg _)
have hb := corrAverage_mono ht n hlocal
rw [corrAverage_add, corrAverage_const_mul, corrAverage_add, corrAverage_fst, corrAverage_snd] at hb
have hi := ih (midpointSection f) (midpointSection g) (midpointSection_mem hf) (midpointSection_mem hg)
rw [expect_section f, expect_section g]
simp only [corrAverage, cubicCost]
linarith
noncomputable def gradient {n : ℕ} (f : BitCube n → ℝ) (i : Fin n) (x : BitCube n) : ℝ :=
(f x - f (Function.update x i (!x i))) / 2
theorem gradient_cons_zero {n : ℕ} (f : BitCube (n + 1) → ℝ) (a : Bool) (x : BitCube n) :
gradient f 0 (Fin.cons a x) = sign a * slopeSection f x := by
cases a <;> simp [gradient, sign, slopeSection] ; ring
theorem gradient_midpoint {n : ℕ} (f : BitCube (n + 1) → ℝ) (i : Fin n) (x : BitCube n) :
gradient (midpointSection f) i x = midpointSection (gradient f i.succ) x := by
simp only [gradient, midpointSection, Fin.cons_succ, ← Fin.cons_update]
ring
theorem abs_midpoint_cube_le (a b : ℝ) :
|(a + b) / 2| ^ 3 ≤ (|a| ^ 3 + |b| ^ 3) / 2 := by
have h := abs_add_le a b
have hpow : |a + b| ^ 3 ≤ (|a| + |b|) ^ 3 := pow_le_pow_left₀ (abs_nonneg _) h 3
rw [abs_div, abs_of_pos (by norm_num : (0 : ℝ) < 2), div_pow]
have hm := mul_nonneg (add_nonneg (abs_nonneg a) (abs_nonneg b)) (sq_nonneg (|a| - |b|))
nlinarith
theorem midpoint_cubic_contract {n : ℕ} (f : BitCube (n + 1) → ℝ) :
(𝔼 x, |midpointSection f x| ^ 3) ≤ 𝔼 x, |f x| ^ 3 := by
rw [expect_section (fun x => |f x| ^ 3)]
apply Finset.expect_le_expect
intro x _
exact abs_midpoint_cube_le _ _
theorem gradient_zero_cubic {n : ℕ} (f : BitCube (n + 1) → ℝ) :
(𝔼 x, |gradient f 0 x| ^ 3) = 𝔼 x, |slopeSection f x| ^ 3 := by
rw [expect_section]
apply Finset.expect_congr rfl
intro x _
simp [midpointSection, gradient_cons_zero, sign]
theorem cubicCost_le_gradients (n : ℕ) (f : BitCube n → ℝ) :
cubicCost n f ≤ ∑ i, 𝔼 x, |gradient f i x| ^ 3 := by
induction n with
| zero => simp [cubicCost]
| succ n ih =>
rw [cubicCost, Fin.sum_univ_succ, gradient_zero_cubic]
apply add_le_add le_rfl
calc
_ ≤ ∑ i, 𝔼 x, |gradient (midpointSection f) i x| ^ 3 := ih _
_ ≤ _ := by
apply Finset.sum_le_sum
intro i _
simp only [gradient_midpoint]
exact midpoint_cubic_contract _
theorem gradient_lipschitz {n : ℕ} (F : ℝ → ℝ)
(hF : ∀ a b, |F a - F b| ≤ |a - b|) (f : BitCube n → ℝ) (i : Fin n) (x : BitCube n) :
|gradient (fun x => F (f x)) i x| ≤ |gradient f i x| := by
simp only [gradient, abs_div]
exact div_le_div_of_nonneg_right (hF _ _) (abs_nonneg _)
end OptimalMaxCut.GaussianBellman
end
end OAI