openAI-math-dataset / lean /OAI /Combinatorics /EditApproximation /Refinement /ComputedCoarseHistorySpace.lean
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Add lean source and binary files (part 43)
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import OAI.Combinatorics.EditApproximation.Coarse.CoarseIndexedHistory
import OAI.Combinatorics.EditApproximation.Refinement.ComputedFreshWork
import OAI.Combinatorics.EditApproximation.Refinement.ComputedCoarseExecution
import OAI.Combinatorics.EditApproximation.Definitions
namespace OAI
namespace EditApproximation
open Filter
def physicalCoarseHistoryEntry {M J B : ℕ} [NeZero B] (source target : List ℕ) (N : ℕ)
(depth : PhysicalEntry M J target.length → ℕ) (draw : PhysicalCoarseDraw source target B depth)
(entry : PhysicalEntry M J target.length) : ℕ :=
BinaryFraction.coarseIndexedInitialArrayHistory N B (depth entry) (physicalSource source entry.1)
(substring target entry.2.lo entry.2.hi) (draw entry)
/-- The complete indexed history of each call fits the reserved capacity. -/
theorem physicalCoarseHistoryEntry_fits {M J : ℕ} :
∀ᶠ N : ℕ in atTop, ∀ B : ℕ, ∀ (_hB : NeZero B),
∀ (source target : List ℕ) (depth : PhysicalEntry M J target.length → ℕ),
B = (integerParameters N).B →
(∀ entry, depth entry = computedCoarseDepth N
(coarseEntryWidth (physicalSource source entry.1) (substring target entry.2.lo entry.2.hi))) →
source.length + target.length ≤ N →
∀ (draw : PhysicalCoarseDraw source target B depth) (entry : PhysicalEntry M J target.length),
physicalCoarseHistoryEntry source target N depth draw entry ≤ (N + 2) ^ 37 := by
filter_upwards [BinaryFraction.coarseIndexedInitialArrayHistory_polynomial] with N hN
intro B inst source target depth hB hd hsize draw entry
have hx := physicalSource_length_le source entry.1
have hy : (substring target entry.2.lo entry.2.hi).length ≤ target.length := by
simp only [substring, List.length_drop, List.length_take]
exact (Nat.sub_le _ _).trans (min_le_right _ _)
exact hN B (depth entry) (physicalSource source entry.1)
(substring target entry.2.lo entry.2.hi) hB (hd entry)
(max_le (by omega) (by omega)) inst (draw entry)
/-- Reserve a complete-history buffer at each actual coarse cache miss.
This conservatively retains those buffers until the memo run finishes. -/
theorem computedFamilyCoarseArrayCapacity_eq (source target : List ℕ) (N : ℕ)
(input : ComputedFamilyInput source target N) :
computedFamilyCoarseArrayCapacity source target N input =
((computedFamilyResult source target N input).freshKeys.map
(coarseHistoryKeyCost (fun _ => (N + 2) ^ 37))).sum :=
computedFamilyKernelWork_eq_fresh_sum source target N input _
theorem computedFamilyCoarseArrayCapacity_bound (source target : List ℕ) (N : ℕ)
(input : ComputedFamilyInput source target N) :
computedFamilyCoarseArrayCapacity source target N input ≤
(N + 2) ^ 37 * (computedFamilyResult source target N input).requests := by
apply computedFamilyKernelWork_le
exact coarseHistoryKeyCost_le _ _ (fun _ => le_rfl)
theorem computedFamilyCoarseArrayCapacity_polynomial :
∀ᶠ N : ℕ in atTop, ∀ source target : List ℕ, source.length + target.length ≤ N →
∀ input : ComputedFamilyInput source target N,
computedFamilyCoarseArrayCapacity source target N input ≤ (N + 2) ^ 39 := by
filter_upwards [computedFamilyResult_requests_near_linear 1 (by norm_num)] with N hr
intro source target hsize input
have hrequests : (computedFamilyResult source target N input).requests ≤ (N + 2) ^ 2 := by
have h := hr source target hsize input
norm_num at h
exact_mod_cast h
calc
_ ≤ (N + 2) ^ 37 * (computedFamilyResult source target N input).requests :=
computedFamilyCoarseArrayCapacity_bound source target N input
_ ≤ (N + 2) ^ 37 * (N + 2) ^ 2 := Nat.mul_le_mul_left _ hrequests
_ = _ := by rw [← pow_add]
end EditApproximation
end OAI