openAI-math-dataset / lean /OAI /Algebra /Finitistic /Iteration /BinomialIteration.lean
roseking's picture
Add lean source and binary files (part 7)
216fb31 verified
Raw History Blame Contribute Delete
4.18 kB
import Mathlib.Data.Fintype.Sigma
import OAI.Algebra.Finitistic.Iteration.BinomialIndex
import OAI.Algebra.Finitistic.Iteration.BinomialBiproduct
namespace OAI
open CategoryTheory CategoryTheory.Limits
noncomputable section
namespace ExternalTensor.Binomial
universe v u w
variable {C : Type u} [Category.{v} C] [Preadditive C] [HasFiniteBiproducts C] [HasBinaryBiproducts C]
variable [HasShift C ℤ] [∀ n : ℤ, (shiftFunctor C n).Additive]
variable {S : Type w} (Phi : C ⥤ C) [Phi.Additive] [Phi.CommShift ℤ]
variable (M : S → C) (H : S → S) (b d : ℤ)
/-- Apply the splitting independently at each object; no naturality in S is present. -/
def branchIteration (split : ∀ Y, Phi.obj (M Y) ≅ (M (H Y))⟦b⟧ ⊞ (M (H Y))⟦b+d⟧)
(j : ℕ) (Y : S) :
(Phi.obj^[j]) (M Y) ≅
⨁ fun x : Branch j => (M ((H^[j]) Y))⟦(j : ℤ)*b + d*(weight x : ℤ)⟧ := by
induction j with
| zero =>
letI : Unique (Branch 0) := inferInstanceAs (Unique Unit)
simpa only [Function.iterate_zero_apply, Nat.cast_zero, zero_mul, mul_zero, zero_add, weight] using
(shiftZero ℤ (M Y)).symm ≪≫
(biproductUniqueIso (fun _ : Branch 0 => (M Y)⟦(0 : ℤ)⟧)).symm
| succ j ih =>
rw [Function.iterate_succ_apply']
refine Phi.mapIso ih ≪≫ Phi.mapBiproduct _ ≪≫ ?_
let X := M ((H^[j+1]) Y)
let f : Branch j → C := fun x => X⟦((j+1 : ℕ) : ℤ)*b + d*(weight x : ℤ)⟧
let g : Branch j → C := fun x => X⟦((j+1 : ℕ) : ℤ)*b + d*((weight x+1 : ℕ) : ℤ)⟧
have step (x : Branch j) :
Phi.obj ((M ((H^[j]) Y))⟦(j : ℤ)*b+d*(weight x : ℤ)⟧) ≅ f x ⊞ g x := by
simpa only [f, g, X, Function.iterate_succ_apply', Nat.cast_add, Nat.cast_one,
add_mul, mul_add, mul_one, one_mul, add_assoc, add_left_comm, add_comm] using
splitShift Phi (M ((H^[j]) Y)) (M (H ((H^[j]) Y))) b d
((j : ℤ)*b+d*(weight x : ℤ)) (split ((H^[j]) Y))
refine biproduct.mapIso step ≪≫ biproductBiprod f g ≪≫ biproduct.mapIso (J := Branch j ⊕ Branch j)
(f := Sum.elim f g)
(g := fun x : Branch j ⊕ Branch j =>
(M ((H^[j+1]) Y))⟦((j+1 : ℕ) : ℤ)*b + d*(weight (n := j+1) x : ℤ)⟧) ?_
intro x
cases x with
| inl x => exact Iso.refl _
| inr x => exact Iso.refl _
/-- The manuscript's repeated finite biproduct, including the multiplicity index. -/
def binomialObject (j : ℕ) (X : C) : C :=
⨁ fun q : Fin (j+1) => ⨁ fun _ : Fin (Nat.choose j q.val) =>
X⟦(j : ℤ)*b + d*(q.val : ℤ)⟧
/-- Regroup by weight using the proved fiber cardinality, not an assumed decomposition. -/
def groupBiproduct (j : ℕ) (X : C) :
(⨁ fun x : Branch j => X⟦(j : ℤ)*b + d*(weight x : ℤ)⟧) ≅
binomialObject b d j X :=
biproduct.whiskerEquiv
(f := fun x : Branch j => X⟦(j : ℤ)*b + d*(weight x : ℤ)⟧)
(g := fun p : Σ q : Fin (j+1), Fin (Nat.choose j q.val) =>
X⟦(j : ℤ)*b + d*(p.1.val : ℤ)⟧)
(groupEquiv j) (fun _ => Iso.refl _) ≪≫
(biproductBiproductIso (fun q : Fin (j+1) => Fin (Nat.choose j q.val))
(fun q _ => X⟦(j : ℤ)*b + d*(q.val : ℤ)⟧)).symm
/-- Categorical binomial iteration with a common shift offset. -/
def binomialIteration (split : ∀ Y, Phi.obj (M Y) ≅ (M (H Y))⟦b⟧ ⊞ (M (H Y))⟦b+d⟧)
(j : ℕ) (Y : S) :
(Phi.obj^[j]) (M Y) ≅ binomialObject b d j (M ((H^[j]) Y)) :=
branchIteration Phi M H b d split j Y ≪≫ groupBiproduct b d j _
/-- Section 4, final corollary, with only objectwise splitting isomorphisms. -/
def section4Iteration (split : ∀ Y, Phi.obj (M Y) ≅ M (H Y) ⊞ (M (H Y))⟦(3 : ℤ)⟧)
(j : ℕ) (Y : S) :
(Phi.obj^[j]) (M Y) ≅
⨁ fun q : Fin (j+1) => ⨁ fun _ : Fin (Nat.choose j q.val) =>
(M ((H^[j]) Y))⟦(3 : ℤ)*(q.val : ℤ)⟧ := by
have h (Y : S) : Phi.obj (M Y) ≅ (M (H Y))⟦(0 : ℤ)⟧ ⊞ (M (H Y))⟦(0+3 : ℤ)⟧ :=
split Y ≪≫ biprod.mapIso (shiftZero ℤ (M (H Y))).symm (Iso.refl _)
simpa only [binomialObject, mul_zero, zero_add] using binomialIteration Phi M H 0 3 h j Y
end ExternalTensor.Binomial
end
end OAI