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Add lean source and binary files (part 46)
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import OAI.Combinatorics.LowSpaceSubsetSum.Storage.MeasuredPivotCells
import OAI.Combinatorics.LowSpaceSubsetSum.Representation.RepresentationRecordInitialization
import OAI.Combinatorics.LowSpaceSubsetSum.Representation.RepresentationKeyCells
namespace OAI
/-! # Explicit scratch for the three searches initializing a stored row
Range endpoints and modular pivots use tail binary search on the retained
right array. Only one weight evaluation is live at any time.
-/
namespace LowSpaceSubsetSum.MeasuredRowCells
variable {α : Type*}
def range (xs : Array α) (key : α → (ℤ × ℕ) × ℕ) (r : ℤ) (k : ℕ)
(hs : Monotone (fun i : Fin xs.size => (key xs[i.val]).1.1))
(hk : xs.size < 2 ^ k) : (SliceCursor.Range xs.size × ℕ) × ℕ :=
let lo := MeasuredPivotCells.search (fun i : Fin xs.size =>
let q := key xs[i.val]; ((decide (q.1.1 < r), q.1.2 + 2), q.2 + 8)) k
let hi := MeasuredPivotCells.search (fun i : Fin xs.size =>
let q := key xs[i.val]; ((decide (q.1.1 ≤ r), q.1.2 + 2), q.2 + 8)) k
have hlo : lo.1.1.lo = BinarySlice.lower xs (fun x => (key x).1.1) r k := by
simp only [lo, MeasuredPivotCells.search_value, BinarySlice.lower,
BinaryPivot.measuredSearch_value]
have hhi : hi.1.1.lo = BinarySlice.upper xs (fun x => (key x).1.1) r k := by
simp only [hi, MeasuredPivotCells.search_value, BinarySlice.upper,
BinaryPivot.measuredSearch_value]
((⟨lo.1.1.lo, hi.1.1.lo - lo.1.1.lo, by
rw [hlo, hhi]
exact (BinarySlice.range xs (fun x => (key x).1.1) r k hs hk).bounded⟩,
lo.1.2 + hi.1.2 + 4), max lo.2 hi.2 + 24)
theorem range_value (xs : Array α) (key : α → (ℤ × ℕ) × ℕ) (r : ℤ) (k : ℕ)
(hs : Monotone (fun i : Fin xs.size => (key xs[i.val]).1.1)) (hk : xs.size < 2 ^ k) :
(range xs key r k hs hk).1 = BinarySlice.measuredRange xs (fun x => (key x).1) r k hs hk := by
simp only [range, BinarySlice.measuredRange, MeasuredPivotCells.search_value]
theorem range_cells (xs : Array α) (key : α → (ℤ × ℕ) × ℕ) (r : ℤ) (k R : ℕ)
(hs : Monotone (fun i : Fin xs.size => (key xs[i.val]).1.1)) (hk : xs.size < 2 ^ k)
(hr : ∀ x, (key x).2 ≤ R) : (range xs key r k hs hk).2 ≤ R + 72 := by
have hl := MeasuredPivotCells.search_cells (fun i : Fin xs.size =>
let q := key xs[i.val]; ((decide (q.1.1 < r), q.1.2 + 2), q.2 + 8)) (R + 8)
(fun i => Nat.add_le_add_right (hr xs[i.val]) 8) k
have hh := MeasuredPivotCells.search_cells (fun i : Fin xs.size =>
let q := key xs[i.val]; ((decide (q.1.1 ≤ r), q.1.2 + 2), q.2 + 8)) (R + 8)
(fun i => Nat.add_le_add_right (hr xs[i.val]) 8) k
change max _ _ + 24 ≤ _
omega
def start (xs : Array α) (rr : SliceCursor.Range xs.size) (key : α → (ℤ × ℕ) × ℕ)
(P r : ℤ) (k : ℕ) (reverse : Bool) : (SliceCursor.State xs.size × ℕ) × ℕ :=
if h : 0 < rr.length then
haveI : NeZero rr.length := ⟨by omega⟩
let q := MeasuredPivotCells.search (fun i : Fin rr.length =>
let w := key (SliceCursor.values xs rr i)
((if reverse then decide (w.1.1 ≤ r) else decide (w.1.1 < P - r), w.1.2 + 2), w.2 + 8)) k
let pos := if reverse then Fin.ofNat rr.length q.1.1.lo - 1 else Fin.ofNat rr.length q.1.1.lo
((⟨rr, some ⟨pos, rr.length, reverse⟩⟩, q.1.2 + 8), q.2 + 32)
else ((⟨rr, none⟩, 1), 16)
theorem start_value (xs : Array α) (rr : SliceCursor.Range xs.size) (key : α → (ℤ × ℕ) × ℕ)
(P r : ℤ) (k : ℕ) (reverse : Bool) :
(start xs rr key P r k reverse).1 =
if reverse then SliceModular.measuredReverse xs rr (fun x => (key x).1) r k
else SliceModular.measuredForward xs rr (fun x => (key x).1) P r k := by
cases reverse <;>
simp only [start, Bool.false_eq_true, ↓reduceIte, SliceModular.measuredForward,
SliceModular.measuredReverse, ModularCursor.measuredForward, ModularCursor.measuredReverse]
all_goals split <;> simp only [MeasuredPivotCells.search_value]
theorem start_cells (xs : Array α) (rr : SliceCursor.Range xs.size) (key : α → (ℤ × ℕ) × ℕ)
(P r : ℤ) (k R : ℕ) (reverse : Bool) (hr : ∀ x, (key x).2 ≤ R) :
(start xs rr key P r k reverse).2 ≤ R + 80 := by
unfold start
split
· rename_i h
have : NeZero rr.length := ⟨by omega⟩
have hh := MeasuredPivotCells.search_cells (fun i : Fin rr.length =>
let w := key (SliceCursor.values xs rr i)
((if reverse then decide (w.1.1 ≤ r) else decide (w.1.1 < P - r), w.1.2 + 2), w.2 + 8))
(R + 8) (fun i => Nat.add_le_add_right (hr (SliceCursor.values xs rr i)) 8) k
dsimp only at hh ⊢
omega
· dsimp only; omega
variable {n : ℕ}
def row (I : Instance n) (F : IndexedMask n) (right : Array (LeafRecord n)) (p P c : ℤ)
(hs : FilteredRow.PrimarySorted right (fun x => (RepresentationKeys.leafWeight I F x).1) p)
(mode : FilteredStream.Mode) (x : LeafRecord n) :
(PairCursor.State (LeafRecord n) right.size × ℕ) × ℕ :=
let wx := RepresentationKeyCells.leafWeight I F x
let k := FilteredStream.steps right.size
let low := fun y => let w := RepresentationKeyCells.leafWeight I F y
((w.1.1 % p, w.1.2 + 2), w.2 + 8)
let high := fun y => let w := RepresentationKeyCells.leafWeight I F y
((w.1.1 % P, w.1.2 + 2), w.2 + 8)
let rr := range right low ((c - wx.1.1) % p) k
(by simpa only [low, FilteredRow.PrimarySorted, RepresentationKeyCells.leafWeight_value] using hs)
(FilteredStream.steps_sufficient right.size)
let q := match mode with
| .direct => start right rr.1.1 high P (wx.1.1 % P) k false
| .complementary v => start right rr.1.1 high P ((v - wx.1.1) % P) k true
((⟨x, q.1.1⟩, wx.1.2 + rr.1.2 + q.1.2 + 4), max wx.2 (max rr.2 q.2) + 3 * n + 68)
theorem row_value (I : Instance n) (F : IndexedMask n) (right : Array (LeafRecord n)) (p P c : ℤ)
(hs : FilteredRow.PrimarySorted right (fun x => (RepresentationKeys.leafWeight I F x).1) p)
(mode : FilteredStream.Mode) (x : LeafRecord n) :
(row I F right p P c hs mode x).1 = RepresentationKeys.row I F right p P c hs mode x := by
cases mode <;>
simp only [row, RepresentationKeys.row, FilteredStream.measuredRow,
range_value, start_value, RepresentationKeyCells.leafWeight_value, Bool.false_eq_true, ↓reduceIte]
theorem row_cells (I : Instance n) (F : IndexedMask n) (right : Array (LeafRecord n)) (p P c : ℤ)
(hs : FilteredRow.PrimarySorted right (fun x => (RepresentationKeys.leafWeight I F x).1) p)
(mode : FilteredStream.Mode) (x : LeafRecord n) :
(row I F right p P c hs mode x).2 ≤ 200 * inputScale I := by
have hw := RepresentationKeyCells.leafWeight_cells I F x
have hu : n + 1 ≤ inputScale I := by unfold inputScale; omega
let low := fun y => let w := RepresentationKeyCells.leafWeight I F y
((w.1.1 % p, w.1.2 + 2), w.2 + 8)
let high := fun y => let w := RepresentationKeyCells.leafWeight I F y
((w.1.1 % P, w.1.2 + 2), w.2 + 8)
have hh : ∀ y, (high y).2 ≤ 12 * n + 32 := by
intro y
exact Nat.add_le_add_right (RepresentationKeyCells.leafWeight_cells I F y) 8
have hl : ∀ y, (low y).2 ≤ 12 * n + 32 := by
intro y
exact Nat.add_le_add_right (RepresentationKeyCells.leafWeight_cells I F y) 8
let k := FilteredStream.steps right.size
let rr := range right low ((c - (RepresentationKeyCells.leafWeight I F x).1.1) % p) k
(by simpa only [low, FilteredRow.PrimarySorted, RepresentationKeyCells.leafWeight_value] using hs)
(FilteredStream.steps_sufficient right.size)
have hr := range_cells right low ((c - (RepresentationKeyCells.leafWeight I F x).1.1) % p)
k (12 * n + 32)
(by simpa only [low, FilteredRow.PrimarySorted, RepresentationKeyCells.leafWeight_value] using hs)
(FilteredStream.steps_sufficient right.size) hl
change rr.2 ≤ _ at hr
cases mode with
| direct =>
have hq := start_cells right rr.1.1 high P ((RepresentationKeyCells.leafWeight I F x).1.1 % P)
k (12 * n + 32) false hh
change max (RepresentationKeyCells.leafWeight I F x).2
(max rr.2 (start right rr.1.1 high P ((RepresentationKeyCells.leafWeight I F x).1.1 % P) k false).2) +
3 * n + 68 ≤ _
omega
| complementary v =>
have hq := start_cells right rr.1.1 high P ((v - (RepresentationKeyCells.leafWeight I F x).1.1) % P)
k (12 * n + 32) true hh
change max (RepresentationKeyCells.leafWeight I F x).2
(max rr.2 (start right rr.1.1 high P ((v - (RepresentationKeyCells.leafWeight I F x).1.1) % P) k true).2) +
3 * n + 68 ≤ _
omega
end LowSpaceSubsetSum.MeasuredRowCells
end OAI