openAI-math-dataset / lean /OAI /Computability /PerfectCompleteness /Algebra /RightDecoderAffineSuccess.lean
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import OAI.Computability.PerfectCompleteness.Decoding.RightDecoderLemmas
import OAI.Computability.PerfectCompleteness.Decoding.RightDecoderWitness
import OAI.Computability.PerfectCompleteness.Foundations.OutputAnnihilator
namespace OAI
section
namespace PerfectCompleteness.RightDecoderAffineSuccess
open scoped TensorProduct Classical
open UniqueGamesTheorem.Foundations.Games
open TreeSourceSpaces HierarchicalArrays OwnInputReference
abbrev F2 := ZMod 2
noncomputable section
variable {branch : Nat → Nat} {n t : Nat}
(slots : RecursiveSpaces.Slots branch n → Fin t → MixedSupport.Slot)
(rows : Nat → Nat) (upper lower : Nodes branch n)
(W : Submodule F2 (UpperVector rows upper)) (a : LowerVector rows lower)
(repeats : Nat → Nat) (cut : Cut upper lower)
(labeling : KeyStrategy.Strategy (TreeCanonical.locationCount branch n t))
(input : Input slots rows upper lower W a)
attribute [local instance] RightDecoder.characterFintype RightDecoder.scalarFintype
theorem character_count :
Fintype.card (Module.Dual F2 (UpperVector rows upper)) =
2 ^ rows (Nodes.height upper) := by
simpa only [OwnInputReference.UpperVector, HierarchicalArrays.Block,
Module.finrank_fin_fun] using
(OutputAnnihilator.card_dual (K := UpperVector rows upper))
theorem coset_probability_lower
(ρ h₀ : ℝ) (hρ : 0 < ρ) (hh₀ : 0 < h₀)
(hrep : RecursiveSamplerBias.RepetitionsBalanced repeats)
(hslice : (7 / 8 : ℝ) ^ repeats (Nodes.height upper) ≤ h₀ / 2)
(hlist : (7 / 8 : ℝ) ^ repeats (Nodes.height upper) ≤ (ρ * h₀ / 4) ^ 2 / 2)
(hexcluded : 1 / (2 : ℝ) ^ Module.finrank F2 W < ρ / 8)
(Q : Submodule F2 (Module.Dual F2 (UpperSpace slots upper)))
(hsize : h₀ ≤ 1 / (2 : ℝ) ^ Module.finrank F2 (Q →ₗ[F2] W))
(target : Q →ₗ[F2] W) (z : Module.Dual F2 (UpperSpace slots upper))
(offset : UpperVector rows upper)
(hmatch : ρ ≤ RightDecoderWitness.conditionalMatch slots rows upper lower W a
repeats cut labeling input Q target z offset) :
(ρ * h₀ / 4) ^ 2 / (2 * (2 : ℝ) ^ rows (Nodes.height upper)) ≤
(RightDecoder.law slots rows upper lower W a repeats cut labeling input (ρ * h₀ / 4)).probability
(fun decoded => decide (decoded - z ∈ Q)) := by
have hthreshold : 0 < ρ * h₀ / 4 := div_pos (mul_pos hρ hh₀) (by norm_num)
obtain ⟨σ, hσ, Φ, hΦ, herror⟩ :=
RightDecoderWitness.exists_heavy_error_coset slots rows upper lower W a
repeats cut labeling input ρ h₀ hρ hh₀ hrep hslice hexcluded Q hsize target z offset hmatch
have h := RightDecoder.coset_lower_of_balanced slots rows upper lower W a
repeats cut labeling input (ρ * h₀ / 4) hrep hthreshold hlist Q z
⟨σ, Φ, hΦ, hσ, herror⟩
simpa only [character_count rows upper, Nat.cast_pow, Nat.cast_ofNat] using h
theorem coset_probability_pos
(ρ h₀ : ℝ) (hρ : 0 < ρ) (hh₀ : 0 < h₀)
(hrep : RecursiveSamplerBias.RepetitionsBalanced repeats)
(hslice : (7 / 8 : ℝ) ^ repeats (Nodes.height upper) ≤ h₀ / 2)
(hlist : (7 / 8 : ℝ) ^ repeats (Nodes.height upper) ≤ (ρ * h₀ / 4) ^ 2 / 2)
(hexcluded : 1 / (2 : ℝ) ^ Module.finrank F2 W < ρ / 8)
(Q : Submodule F2 (Module.Dual F2 (UpperSpace slots upper)))
(hsize : h₀ ≤ 1 / (2 : ℝ) ^ Module.finrank F2 (Q →ₗ[F2] W))
(target : Q →ₗ[F2] W) (z : Module.Dual F2 (UpperSpace slots upper))
(offset : UpperVector rows upper)
(hmatch : ρ ≤ RightDecoderWitness.conditionalMatch slots rows upper lower W a
repeats cut labeling input Q target z offset) :
0 < (RightDecoder.law slots rows upper lower W a repeats cut labeling input (ρ * h₀ / 4)).probability
(fun decoded => decide (decoded - z ∈ Q)) := by
have hthreshold : 0 < ρ * h₀ / 4 := div_pos (mul_pos hρ hh₀) (by norm_num)
have hfactor : 0 < (ρ * h₀ / 4) ^ 2 / (2 * (2 : ℝ) ^ rows (Nodes.height upper)) :=
div_pos (pow_pos hthreshold _) (mul_pos (by norm_num) (pow_pos (by norm_num) _))
exact hfactor.trans_le (coset_probability_lower slots rows upper lower W a repeats cut
labeling input ρ h₀ hρ hh₀ hrep hslice hlist hexcluded Q hsize target z offset hmatch)
end
end PerfectCompleteness.RightDecoderAffineSuccess
end
end OAI